Question: Optimal assignment of targets and pathfinding for teams of autonomous agents.

This article details the innovative software architecture of an autonomous, interactive tour-guide robot designed for museum environments. The system is characterized by its modular and distributed structure, seamlessly integrating functions such as localization, mapping, collision avoidance, planning, and various user interaction modules, alongside web-based telepresence capabilities. Central to the software's effectiveness is its reliance on probabilistic computation, online learning, and anytime algorithms, enabling the robot to navigate dynamic environments with safety, reliability, and efficiency, without necessitating any environmental modifications to support its operation. The architecture places a significant focus on developing intuitive interactive capabilities, enhancing human-robot interaction in crowded public spaces and facilitating virtual telepresence through the internet. The practical success of this approach is illustrated by the deployment of the robot "RHINO" in a museum in mid-1997, where it operated for six days. Empirical evidence confirmed the robot's reliable performance in public settings, significantly increasing museum attendance by over 50%. Moreover, the robot was remotely controlled by thousands of individuals worldwide via the web, underscoring the broad potential of this technology across diverse service robot applications.

**Question**: A Comparative Study of Machine Learning Techniques for Predicting Stock Market Trends

**Documents**: An Empirical Comparison of Machine Learning Techniques for Time Series Forecasting
This paper empirically compares several machine learning techniques for time series forecasting. We consider four types of machine learning algorithms: multilayer perceptron (MLP), radial basis function (RBF) networks, k-nearest neighbor regression (KNN), and support vector regression (SVR). These methods are compared using a variety of time series datasets from the UCI Machine Learning Repository, including both univariate and multivariate time series. For each method, we conduct a grid search to optimize the model parameters. We then evaluate the performance of each model using several error measures, such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our results show that SVR consistently outperforms the other methods in terms of prediction accuracy. However, SVR also requires the most computational resources and time to train. MLP and RBF networks have similar performance, and are faster to train than SVR. KNN is the fastest method to train, but its prediction accuracy is the lowest among the four methods. Overall, our results suggest that SVR is the best choice for time series forecasting if computational resources are not a concern. However, if fast training is required, MLP or RBF networks may

Real-time robot motion planning using rasterizing computer graphics hardware. In Proc. OY82] C. O'D unlaing and C.K. Yap. A retraction method for planning the motion of a disc. A local approach for path planning of manipulators with a high number of degrees of freedom. a path generation algorithm into oo-line programming of airbus panels. Motion planning for many degrees of freedom-random reeections at c-space obstacles. REFERENCES 31 iterative collision checker. We observed no dramatic slowdown of the planner. A challenging research goal would now be to extend the method to dynamic scenes. One rst question is: How should a roadmap computed for a given workspace be updated if a few obstacles are removed or added? Answering this question would be useful to apply our method to scenes subject to small incremental changes. Such changes occur in many manufacturing (e.g., assembly) cells; while most of the geometry of such a cell is permanent and stationary, a few objects (e.g., xtures) are added or removed between any two consecutive manufacturing operations. Similar incremental changes also occur in automatic graphic animation. A second question is: How should the learning and query phase be modiied if some obstacles are moving along known trajectories? An answer to this question might consist of applying our roadmap method in the conngurationtime space of the robot Lat91]. The roadmap would then have to be built as a directed graph, since local paths between any two nodes must monotonically progress along the time axis, with possibly additional constraints on their slope and curvature to reeect bounds on the robot's velocity and acceleration. A motion planner with performance proportional to task diiculty. 30 In KL94a, KL94b, O S94] prior versions of the method have been applied to a great variety of holonomic robots including planar and spatial articulated robots with revolute, prismatic, and/or spherical joints, xed or free base, and single or multiple kinematic chains. In Sve93, SO94] a variation of the method (essentially one with a diierent general local planner) was also run successfully on examples involving nonholonomic car-like robots. Experimental results show that our method can eeciently solve problems which are beyond the capabilities of other existing methods. For example, for planar articulated robots with many dofs, the customized implementation of Section 5 is much more consistent than the Randomized Path Planner (RPP) of BL91]. Indeed, the latter can be very fast on some diicult problems, but it …

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the PacMan environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.

Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons. This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates. We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle communication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies. The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed. We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates. Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed. Thesis Supervisor: John J. Leonard Title: Professor of Mechanical and Ocean Engineering Massachusetts Institute of Technology Acknowledgments This thesis would not have been possible without the help and support of many friends and colleagues who made the last five years at MIT an exceptionally fulfilling experience. I would like to thank my advisor John Leonard who strongly supported me ever since our first email exchange in 2003. He guided me all the way through the application process, research, thesis and finding a post doc position. His broad range of research interests enabled me to find a thesis subject that exactly matched my interest. And I very much appreciated him allowing me to take a significant amount of time to pursue other projects as well as travel. I would also like to thank my other committee members: Henrik Schmidt for his continued support and great company during several research cruises, and Hanu Singh and Arjuna Balasuriya for their helpful suggestions during my thesis writing. Many thanks to David Battle who helped me through my first steps with Autonomous Underwater Vehicles and was a great mate to have around. Many of the experiments presented in this thesis would not have been possible without the support of Andrew Patrikalakis. The results owe a lot to his countless hours of coding assistance and his efforts to ensure that the kayaks would be ready when needed. They were also made possible by Joe Curcio, the builder of the kayaks, and the support of Jacques Leedekerken and Kevin Cockrell. The last years would not have been the same without the many great people I met at MIT. Most important was Matt Walter who convinced me in August 2003 that MIT is not only an interesting place, but that it can be very friendly as well. Throughout the years we shared many great personal and academic experiences. I wish him all the best, wherever life may take him. Patrycja unfortunately left our lab, but made up for it by taking me on a very memorable trip across the country. I wish Alec, Emma, Albert, Tom, Olivier and Aisha all the best for their remaining time and life beyond. Iuliu was always a welcome distraction in the United States and abroad and a great help with all hardware questions. I was glad to join Carrick, Marty and David on a number of exciting research and conference trips, as well as their advisor Daniela Rus. In the last months I was also very fortunate to meet a new group of people. First, I am very thankful to Maurice for carrying on what I started I cannot imagine somebody better suited for it and also Been, Georgios, Hordur and Rob. I would also like to thank the many people of the SEA 2007 cruise, particularly the B watch and Julian, Jamie, Heather, Chris, Toby and Jane. One of the most exciting things during my time at MIT was that I was not only able to pursue my thesis topic but also two other projects. First, the flood warning project introduced me to Elizabeth Basha. We shared many joyful moments as well as blood, sweat and tears in the Central American wilderness. I hope that the end of my PhD only marks the beginning of that partnership. Second, the harbor porpoise tag project led by Stacy DeRuiter was a great design challenge. It also provided an opportunity to reach into other areas of ocean sciences by contributing to marine biology research. Her dedication along with the support from Mark Johnson, Peter Tyack and Tom Hurst ensured the project’s success. The exciting results and the process leading up to them rewarded me with a better experience at MIT than I could have ever hoped for. I would like to thank John Leonard for letting me take this scenic route. The path that led me to MIT would not have been possible without the support from people in the early stages of my engineering career who I would like to thank here: Raimund Eich for patiently answering my first electrical engineering questions; my best friends Jan Horn, Daniel Steffensky, Alexander Zimmer and Ulf Radenz for helping me through my university time in Germany; and John Peatman, Ludger Becks and Uwe Zimmer for their academic guidance. Finally, I would like to thank my parents for their continued support and especially Melissa Pitotti for her encouragement not only to start the work at this institution but also to finish it when the time had come. This work was funded by Office of Naval Research grants N00014-97-1-0202, N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI program led by Naomi Leonard of Princeton University. “One degree is not a large distance. On a compass it is scarcely the thickness of a fingernail. But in certain conditions, one degree can be a very large distance. Enough to unmake a man.” The Mysterious Geographic Explorations of Jasper Morello, c © 3D Films, Australia 2005 Meinen Eltern & meinem Bruder

This paper investigates the use of dynamic window approach (DWA) to solve the high speed autonomous navigation problem for mobile robots. In this paper, our main interest is to adapt the DWA to a car-like robot Robucar. This latter can moves with high speed. It is characterized also by its bounded steering angle imposed by its mechanical structure which thereby implies that the robot always moves on circular trajectories whose radius is upper than a certain extent. This approach considers the car-like robot shape, kinematics and dynamics in the determination of translational velocity and steering angle in one step. The approach has been tested in simulation for different environments and demonstrates its practicability.

We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with PLANNER, CONNIVER, HEARSAY-n, and PUP6 are used to demonstrate that negotiation--the two-way transfer of information--is a natural extension to the transfer of control mechanisms used in earlier problem-

Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.

Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August

1 . Introduction This paper provides an overview of the Autonomous Land Vehicle (ALV) Project at CMU. The goal of the CMU ALV Project is to build vision and intelligence for a mobile robot capable of operating in the real world outdoors. We are attacking this on a number of fronts: building appropriate research vehicles, exploiting high. speed experimental computers, and building software for reasoning about the perceived world. Research topics includes: • Construction of research vehicles • Perception systems to perceive the natural outdoor scenes by means of multiple sensors including cameras (color, stereo, and motion), sonar sensors, and a 3D range finder • Path planning for obstacle avoidance Use of topological and terrain map • System architecture to facilitate the system integration • Utilization of parallel computer architectures Our current research vehicle is the Terregator built at CMU which is equipped with a sonar ring, a color camera, and the ERIM laser range finder. Its initial task is to follow roads and sidewalks in the park and on the campus, and avoid obstacles such as trees, humans, and traffic cones. 2. Vehicle, Sensors, and Host Computers The primary vehicle of the AMU ALV Project has been the Terregator, designed and built at CMU. The Terregator, short for Terrestrial Navigator, is designed to provide a clean separation between the vehicle itself and its sensor payload. As shown in

We propose and investigate a system in which teams of quadrotor helicopters assemble 2.5-D structures from simple structural nodes and members equipped with magnets. The structures, called Special Cubic Structures (SCS), are a class of 2.5-D truss-like structures free of overhangs and holes. Grippers attached to the bottom of each quadrotor enable them to pick up, transport, and assemble the structural elements. The design of the nodes and members imposes constraints on assembly which are incorporated into the design of the algorithms used for assembly. We show that any SCS can be built using only the feasible assembly modes for individual structural elements and present simulation and experimental results with a team of quadrotors performing automated assembly. The paper includes a theoretical analysis of the SCS construction algorithm, the rationale for the design of the structural nodes, members and quadrotor gripper, a description of the quadrotor control methods for part pickup, transport and assembly, and an empirical analysis of system performance.

The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is based on trial and error method through experience. Because of this, reinforcement learning is applied to many practical problems. However, straightforward application of reinforcement learning algorithm may not successfully scale up to more complex multi-agent learning problems. To solve this problem, modular Q-learning is employed for multi-agent learning in reinforcement methods. A modified uni-vector field is used for robot navigation. This paper discusses the Y2K2 NaroSot(Nano-Robot World Cup Soccer Tournament) robot soccer system.


Question: Optimal assignment of targets and pathfinding for teams of autonomous agents.

This paper delves into the strategic negotiation and motion principles for teams of autonomous agents in complex environments, part of the Intelligent Robotics and Autonomous Agents series. Focusing on the intersection of robot motion theory, algorithmic development, and practical implementations, the text is a comprehensive guide for researchers and engineers in the field of robotics. It covers essential topics such as disaster robotics, where robots are deployed in hazardous scenarios to assist in rescue and recovery efforts. Additionally, it explores probabilistic robotics, a critical area that deals with uncertainty in robot perception and action. The series also includes an introduction to self-reconfigurable robots, which can adapt their physical structure to suit different tasks, and mechanics of robotic manipulation, crucial for robots interacting with objects in their environment. Strategic negotiation in multiagent environments is another key subject, detailing how robots can communicate and coordinate with each other to achieve common goals. The series further discusses random finite sets for robot mapping and SLAM (Simultaneous Localization and Mapping), introducing innovative concepts for autonomous robotic map representations, a fundamental challenge in robotics. These works provide a rich foundation for understanding and developing advanced robotic systems capable of operating in a wide range of scenarios, from disaster response to complex manufacturing environments.

**Question**: An Investigation into the Application of Deep Learning in Medical Image Analysis

**Documents**: Deep Learning in Medical Image Analysis
This paper presents an overview of the application of deep learning techniques in medical image analysis. Deep learning, a subset of machine learning, has shown remarkable success in various domains, including computer vision, speech recognition, and natural language processing. In the field of medical imaging, deep learning algorithms have been employed to tackle complex problems such as image segmentation, classification, and registration. The paper discusses several deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their specific applications in medical image analysis. It also explores the challenges associated with deep learning in this context, such as the need for large annotated datasets and the interpretability of deep learning models. The paper concludes by highlighting the potential of deep learning to revolutionize medical image analysis, enabling more accurate and efficient diagnosis and treatment planning.

**Similar Documents**: This article provides a comprehensive survey on the integration of deep learning methodologies in the realm of medical image analysis. Deep learning, a powerful branch of artificial intelligence, has made significant strides in various sectors, notably in visual analytics, auditory cognition, and linguistic processing. In medical imaging, deep learning paradigms have been instrumental in addressing intricate

This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different agent decision controllers. Accordingly, the effectiveness of various algorithms is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors influencing the collective performance of these systems.

There have been several recent efforts to build behavior-based autonomous creatures. While competent autonomous action is highly desirable, there is an important need to integrate autonomy with “directability”. In this paper we discuss the problem of building autonomous animated creatures for interactive virtual environments which are also capable of being directed at multiple levels. We present an approach to control which allows an external entity to “direct” an autonomous creature at the motivational level, the task level, and the direct motor level. We also detail a layered architecture and a general behavioral model for perception and action-selection which incorporates explicit support for multi-level direction. These ideas have been implemented and used to develop several autonomous animated creatures.

A shortest-path algorithm finds a path containing the minimal cost between two vertices in a graph. A plethora of shortest-path algorithms is studied in the literature that span across multiple disciplines. This paper presents a survey of shortest-path algorithms based on a taxonomy that is introduced in the paper. One dimension of this taxonomy is the various flavors of the shortest-path problem. There is no one general algorithm that is capable of solving all variants of the shortest-path problem due to the space and time complexities associated with each algorithm. Other important dimensions of the taxonomy include whether the shortest-path algorithm operates over a static or a dynamic graph, whether the shortest-path algorithm produces exact or approximate answers, and whether the objective of the shortest-path algorithm is to achieve time-dependence or is to only be goal directed. This survey studies and classifies shortest-path algorithms according to the proposed taxonomy. The survey also presents the challenges and proposed solutions associated with each category in the taxonomy.

This paper describes a new framework for segmentation of sonar images, tracking of underwater objects and motion estimation. This framework is applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor. The real-time data flow (acoustic images) at the input of the system is first segmented and relevant features are extracted. We also take advantage of the real-time data stream to track the obstacles in following frames to obtain their dynamic characteristics. This allows us to optimize the preprocessing phases in segmenting only the relevant part of the images. Once the static (size and shape) as well as dynamic characteristics (velocity, acceleration, ...) of the obstacles have been computed, we create a representation of the vehicle’s workspace based on these features. This representation uses constructive solid geometry(CSG) to create a convex set of obstacles defining the workspace. The tracking takes also into account obstacles which are no longer in the field of view of the sonar in the path planning phase. A well-proven nonlinear search (sequential quadratic programming) is then employed, where obstacles are expressed as constraints in the search space. This approach is less affected by local minima than classical methods using potential fields. The proposed system is not only capable of obstacle avoidance but also of path planning in complex environments which include fast moving obstacles. Results obtained on real sonar data are shown and discussed. Possible applications to sonar servoing and real-time motion estimation are also discussed.

We illustrate the use of the techniques of modern geometric optimal control theory by studying the shortest paths for a model of a car that can move forwards and backwards. This problem was discussed in recent work by Reeds and Shepp who showed, by special methods, (a) that shortest path motion could always be achieved by means of trajectories of a special kind, namely, concatenations of at most five pieces, each of which is either a straight line or a circle, and (b) that these concatenations can be classified into 48 three-parameter families. We show how these results fit in a much more general framework, and can be discovered and proved by applying in a systematic way the techniques of Optimal Control Theory. It turns out that the “classical” optimal control tools developed in the 1960’s, such as the Pontryagin Maximum Principle and theorems on the existence of optimal trajectories, are helpful to go part of the way and get some information on the shortest paths, but do not suffice to get the full result. On the other hand, when these classical techniques are combined with the use of a more recently developed body of theory, namely, geometric methods based on the Lie algebraic analysis of trajectories, then one can recover the full power of the Reeds-Shepp results, and in fact slightly improve upon them by lowering their 48 to a 46.

This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.

The paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the coupling of two crucial robotic capabilities, namely perception and planning. The main contributions of this work are the development and integration of these modules into one single application, considering explicitly the constraints related to the environment and the system

The most important task for maze solving robots is the fast and reliable finding of its shortest path from its initial point to its final destination point. This paper proposes an intelligent maze solving robot that can determine its shortest path on a line maze based on image processing and artificial intelligence algorithms. The image of the line maze is captured by a camera and sent to the computer to be analyzed and processed by a program developed using Visual C++ and OpenCV libraries and based on graph theory algorithms. The developed program solves the captured maze by examining all possible paths exist in the maze that could convey the robot to the required destination point. After that, the best shortest path is determined and then the instructions that guide the car-like robot to reach its desired destination point are sent to the robot through Bluetooth. The robot follows the received guide path to reach its destination. The proposed approach works faster than the traditional methods which push the robot to move through the maze cell by cell in order to find its destination point. Moreover, the proposed method allows the maze solving robot to avoid trapping and falling in infinity loops. Applications of maze solving systems include intelligent traffic control that helps ambulances, fire fighters, or rescuing robots to find their shortest path to their destination.

The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).


Question: Approaches to forecast dropout rates in university students using machine learning techniques.

This paper presents a comprehensive examination of machine learning methodologies for enhancing student retention management in higher education. Given the critical role student retention plays in university rankings, institutional reputation, and financial health, identifying at-risk students early and effectively intervening becomes paramount. The study employs a five-year dataset from an educational institution, coupled with a variety of data mining techniques, including both individual models and ensembles, to develop predictive models for freshman attrition. The findings reveal that ensemble methods outperform individual models in predictive accuracy, and that balanced datasets yield superior prediction outcomes compared to unbalanced datasets. This research underscores the importance of using sophisticated machine learning techniques to better understand and predict student dropout patterns, enabling universities to implement targeted retention strategies. The sensitivity analysis conducted further validates the robustness of the models, highlighting the effectiveness of the ensemble approach in managing student retention effectively.

Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements; they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, machine learning-based intrusion detection systems have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. This work aims to compare efficiency of machine learning methods in intrusion detection system, including artificial neural networks and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in machine learning-based intrusion detectors, we propose to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compare the accuracy, detection rate, false alarm rate for 4 attack types. The extensive experimental results on the KDD-cup intrusion detection benchmark dataset demonstrate that the proposed approach produces higher performance than KDD Winner, especially for U2R and U2L type attacks.

Outlier Detection is a Data Mining Application. Outlier contains noisy data which is researched in various domains. The various techniques are already being researched that is more generic. We surveyed on various techniques and applications of outlier detection that provides a novel approach that is more useful for the beginners. The proposed approach helps to clean data at university level in less time with great accuracy. This survey includes the existing outlier techniques and applications where the noisy data exists. Our paper defines critical review on various techniques used in different applications of outlier detection that are to be researched further and they gives a particular type of knowledge based data i.e. more useful in research activities. So where the Anomalies is present it will be detected through outlier detection techniques and monitored accordingly especially in educational Data Mining.

Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.

BACKGROUND
Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.

AIMS
In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history.

SAMPLE
This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area.

METHODS
We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions.

RESULTS
Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.

CONCLUSIONS
These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.

Chronic kidney disease (CKD), also known as chronic renal disease. Chronic kidney disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage. . Early detection and treatment can often keep chronic kidney disease from getting worse. Data Mining is the term used for knowledge discovery from large databases. The task of data mining is to make use of historical data, to discover regular patterns and improve future decisions, follows from the convergence of several recent trends: the lessening cost of large data storage devices and the everincreasing ease of collecting data over networks; the expansion of robust and efficient machine learning algorithms to process this data; and the lessening cost of computational power, enabling use of computationally intensive methods for data analysis. Machine learning, has already created practical applications in such areas as analyzing medical science outcomes, detecting fraud, detecting fake users etc. Various data mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. The objective of this research work is to introduce a new decision support system to predict chronic kidney disease. The aim of this work is to compare the performance of Support vector machine (SVM) and K-Nearest Neighbour (KNN) classifier on the basis of its accuracy, precision and execution time for CKD prediction. From the experimental results it is observed that the performance of KNN classifier is better than SVM. Keywords—Data Mining, Machine learning, Chronic kidney disease, Classification, K-Nearest Neighbour, Support vector machine.

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial

A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.

Customer churn happens when a customer discontinues his or her interaction with a company. In retail business, a customer is treated to be churned once his/her transactions outdate a particular amount of time. Once a customer becomes a churn, the loss incurred by the company is not just the lost revenue due to the lost customer but also the costs involved in additional marketing in order to attract new customer. Reducing customer churn is a key business goal of every online business. In this project, we have considered data set from UCI Machine Learning repository. This dataset contains records of transactions that occurred between December 1, 2010 and December 1, 2011. This is recorded from an online retail gift store based in United Kingdom. The customers of this company are mainly wholesalers. This data set is pre-processed by removing NAs, validating numerical values, removing erroneous data points. We then perform aggregations on the data to generate invoice based and customer based data sets. A variable churn is attached to each data point. This churn value is determined based on the customer's transactions. Three algorithms are run on this customer aggregated dataset to predict churn value. They are Random forest, Support vector machines and Extreme gradient boosting. A comparative study is done on these three algorithms.

We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.


Question: Approaches for forecasting dropout rates in university students using machine learning techniques.

This paper introduces a versatile data analytic strategy aimed at predicting college graduation rates and identifying the critical factors that influence student completion. The significance of accurate graduation rate forecasts has grown in recent years, affecting university rankings and serving as a key indicator of institutional effectiveness and student achievement. Amidst heightened governmental demands for transparency and accountability, universities are challenged to improve graduation outcomes. The primary objective of this research is to propose a hybrid data analytic framework that can be seamlessly applied not only in the United States but also in colleges worldwide, due to its universal applicability. This framework aims to anticipate the graduation status of undergraduate students and rank the determinants of completion. Methodology involves the development of a novel hybrid approach to forecast degree attainment at a four-year public university in the US. Three prominent data mining classification techniques—decision trees, artificial neural networks, and support vector machines—are employed to construct predictive models for degree completion. A sensitivity analysis is then conducted to assess the relative impact of each predictive factor on graduation rates. The study's findings reveal that the most influential factors in predicting graduation include the first-term grade point average, housing status (on-campus or commuter), and the student's high school. Conversely, ethnicity, work-study participation, and financial aid application are found to have the least effect on graduation status. The predictive models achieved high accuracy rates, ranging from 71.56 to 77.61 percent, thereby substantiating the efficacy of the proposed methodology. This research offers a unique contribution by presenting an unbiased approach to discern the primary drivers of college graduation rates, coupled with a flexible and potent hybrid technique for application in similar decision-making contexts.

This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning. The proposed approach includes a plugin in Eclipse, uses a combination of peer review/human rating, static code analysis, and classification methods. As training data, public data and student hand-ins in programming are used. Based on this training data, new and uninspected source code can be accurately classified as “well written” or “badly written”. This is a step towards feedback in an interactive environment without peer assessment.

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend. This journal article is available in Journal of University Teaching & Learning Practice: http://ro.uow.edu.au/jutlp/vol3/iss2/3 Jour na l o f Un ive rs i t y Teach ing and Lear n ing Pr ac t i ce Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance

A formative assessment was introduced to a blended learning course in order to predict participant’s learning performance using measurements of their note taking activity and metrics of their attitudes. The lexical metrics were created by analyzing the contents of student’s notes from every class, and by measuring their characteristics. In the results of two types of regression analysis of these measurements, features of note taking activities were a significant source of information for predicting the scores of final exams. In regards to temporal changes in prediction performance during the course, the progress of learning and the typical changes in features of note taking activity were discussed. During the analyses, the effectiveness of the lecturer’s note taking instructions was also confirmed.

Customer churn creates a huge anxiety in highly competitive service sectors especially the telecommunications sector. The objective of this research was to develop a predictive churn model to predict the customers that will be to churn; this is the first step to construct a retention management plan. The dataset was extracted from the data warehouse of the mobile telecommunication company in Thailand. The system generated the customer list, to implement a retention campaign to manage the customers with tendency to leave the company. WEKA software was used to implement the followings techniques: C4.5 decision trees algorithm, the logistic regression algorithm and the neural network algorithm. The C4.5 algorithm of decision trees proved optimal among the models. The findings are unequivocally beneficial to industry and other partners.

Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual’s perception of how the adoption of a new technology will increase their efficiency, and the individual’s perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance.

The education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore. The application involves many data mining tasks. This paper focuses only on one task, namely, selecting students for remedial classes. Traditionally, a cut-off mark for each subject is used to select the weak students. That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject. In this paper, we show that this traditional method requires too many students to take part in the remedial classes. This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics. With the help of data mining, we are able to select the targeted students much more precisely.

This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals.

Big Data is becoming an integral part of education system worldwide, bringing in so much of prediction potential and therefore opportunities to improve learning and teaching methodologies. In fact, it has become the digital policy instrument for policy makers to make strategic decisions supported by big data analytics. This paper uses Systematic Literature Review (SLR) to establish a general overview and background in establishing gaps that need to be addressed in big data analytics for education landscape. A total of 59 research papers from 473 papers were chosen and analyzed. There is an increased trend in incorporating big data in education however, it is not sufficient and need more research in this area particularly predictive models and frameworks for educational settings.

ISSN 2250 – 1061 | © 2011 Bonfring Abstract--In machine learning, classification refers to a step by step procedure for designating a given piece of input data into any one of the given categories. There are many classification problem occurs and need to be solved. Different types are classification algorithms like tree-based, rule-based, etc are widely used. This work studies the effectiveness of Rule-Based classifiers for classification by taking a sample data set from UCI machine learning repository using the open source machine learning tool. A comparison of different rulebased classifiers used in Data Mining and a practical guideline for selecting the most suited algorithm for a classification is presented and some empirical criteria for describing and evaluating the classifiers are given.

Massive open online courses (MOOCs) have exploded onto the scene, promising to satisfy a worldwide thirst for a high-quality, personalized, and free education. This article explores where MOOCs fit within the e-learning and Artificial Intelligence in Education (AIED) landscape.


Question: Perspectives on forecasting dropout rates among university students using machine learning techniques.

Classification and Regression Trees (CART) for Predicting University Student Dropout

This paper explores the use of Classification and Regression Trees (CART) as a predictive tool for identifying students at risk of dropping out of university. CART is a powerful machine learning technique that can handle both classification and regression tasks, making it suitable for predicting binary outcomes such as dropout (yes/no). By analyzing a range of student data, including academic performance, demographic information, and engagement levels, CART constructs a decision tree that can effectively classify students into those likely to drop out and those who are not. This predictive model can aid universities in early intervention strategies, targeting resources and support to students who are at a higher risk of discontinuing their studies. The decision tree generated by CART not only provides predictions but also offers insights into the most significant factors influencing dropout rates, which can inform policy and program development. Through a comprehensive evaluation of its accuracy and performance, this study demonstrates the potential of CART as a valuable tool in higher education for predicting and mitigating student dropout.

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend. This journal article is available in Journal of University Teaching & Learning Practice: http://ro.uow.edu.au/jutlp/vol3/iss2/3 Jour na l o f Un ive rs i t y Teach ing and Lear n ing Pr ac t i ce Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance

As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.

Massive Online Open Courses have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback [5, 38, 29, 9], it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class --- with instructor and TA grades as a baseline --- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.

0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.05.027 * Corresponding author. Tel.: +32 9 264 89 80; fax: E-mail address: dirk.vandenpoel@UGent.be (D. Va URL: http://www.crm.UGent.be (D. Van den Poel). Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6 (1), 7–19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz [Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI’2000): Special track on inductive learning, Las Vegas, Nevada], who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique. 2008 Elsevier Ltd. All rights reserved.

Support Vector Regression and MART by Ian I-En Choo Stanford University

Mobile learning is the next step in the development of distance learning. Widespread access to mobile devices and the opportunity to learn regardless of time and place make the mobile learning an important tool for lifelong learning. The research objectives are to examine the possibility of acceptance in mobile learning (m-Learning) and study main factors that affect using mLearning that focus on higher education students in Saudi Arabia. The researcher used a quantitative approach survey of 80 students. The modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students’ intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.

In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.

In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.

Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.

Despite the recognized value of Machine Learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to the widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes), as well as confidence (in decision making) by users. This paper investigates the effects of correlation between features and target values on user confidence in data analytics-driven decision making. Our user study found that revealing the correlation between features and target variables affected user confidence in decision making significantly. Moreover, users felt more confident in decision making when correlation shared the same trend with the prediction model performance. These findings would help design intelligent user interfaces and evaluate the effectiveness of machine learning models in applications.


Question: Approaches for forecasting dropout rates in university students using machine learning techniques.

This paper delves into the ensemble method known as Random Forests, a sophisticated technique that amalgamates multiple decision tree predictors. Each tree in the forest is reliant on the values of a randomly sampled vector, independently drawn and with a uniform distribution across all trees. As the forest size grows, the generalization error for Random Forests converges almost surely to a limit. The performance of the forest is contingent upon the robustness of its constituent trees and the level of correlation among them. By employing a random subset of features for each node split, Random Forests can achieve error rates that are competitive with Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), while offering enhanced resilience to noisy data. The method includes internal mechanisms to monitor error, strength, and correlation, which are instrumental in gauging the impact of increasing the number of features used in the node splitting process. Additionally, these internal assessments are utilized to quantify the importance of variables, making Random Forests a versatile tool for both classification and regression tasks.

Predicting knowledge levels from user’s implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events (KIE). We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.

Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying ‘‘I’m frustrated.’’ The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy ðchance 1⁄4 58%Þ. The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response. r 2007 Elsevier Ltd. All rights reserved.

The research literature on Web-based learning supports the assumption that interaction is important for a successful course, yet questions exist regarding the nature and extent of the interaction and its effects on student performance. Much of the research is based on student perceptions of the quality and quantity of their interactions and how much they have learned in an online course. The purpose of this study is to examine performance in an online course in relationship to student interaction and sense of presence in the course. Data on multiple independent (measures of interaction and presence) and dependent (measures of performance) variables were collected and subjected to analysis. An attempt was made to go beyond typical institutional performance measures such as grades and withdrawal rates and to examine measures specifically related to course objectives.

This paper concerns learning tasks that require the prediction of a continuous value rather than a discrete class. A general method is presented that allows predictions to use both instance-based and model-based learning. Results with three approaches to constructing models and with eight datasets demonstrate improvements due to the composite method.

In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better evaluate the phenomenon and to support MOOCs developers and instructors in devising courses which are adapted for different learners' needs. Thus, the purpose of this study was to characterize the different types of participant behavior in a MOOC. Using a data mining methodology, 21,889 participants of a MOOC were classified into clusters, based on their activity in the main learning resources of the course: video lectures, discussion forums, and assessments. Thereafter, the participants in each cluster were characterized in regard to demographics, course participation, and course achievement characteristics. Seven types of participant behavior were identified: Tasters (64.8%), Downloaders (8.5%), Disengagers (11.5%), Offline Engagers (3.6%), Online Engagers (7.4%), Moderately Social Engagers (3.7%), and Social Engagers (0.6%). A significant number of 1,020 participants were found to be engaged in the course, but did not achieve a certificate. The types are discussed according to the established research questions. The results provide further evidence regarding the utilization of the flexibility, which is offered in MOOCs, by the participants according to their needs. Furthermore, this study supports the claim that MOOCs' impact should not be evaluated solely based on certification rates but rather based on learning behaviors.

This paper proposes a simple estimation of the quality of student oral presentations. It is based on the study and analysis of features extracted from the audio and digital slides of 448 presentations. The main goal of this work is to automatically predict the values assigned by professors to different criteria in a presentation evaluation rubric. Machine Learning methods were used to create several models that classify students in two clusters: high and low performers. The models created from slide features were accurate up to 65%. The most relevant features for the slide-base models were: number of words, images, and tables, and the maximum font size. The audio-based models reached up to 69% of accuracy, with pitch and filled pauses related features being the most significant. The relatively high degrees of accuracy obtained with these very simple features encourage the development of automatic estimation tools for improving presentation skills.

The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.

AIM
To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.

METHODS
A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.

RESULTS
Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).

CONCLUSIONS
Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.

Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I¿s RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The discussion follows the data mining process. The focus will be on the data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction.

In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as the Arbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.


Question: Perspectives on forecasting university student dropout rates using machine learning techniques.

This paper enhances the utilization of continuous attributes in the C4.5 decision tree algorithm by refining the creation and assessment of tests on such attributes. Addressing a known limitation of C4.5 in handling continuous data, the approach introduces a penalty inspired by Minimum Description Length (MDL) principles, which impacts the selection and effectiveness of tests on continuous attributes. The application of this penalty leads to the elimination of less relevant tests and recalibrates the preference among all tests. Experimental results demonstrate that these modifications result in more compact decision trees with enhanced predictive accuracy. Furthermore, the updated version of C4.5, incorporating these enhancements, outperforms contemporary strategies that employ global discretization and construct compact trees with multi-interval splits, particularly in domains with continuous attributes.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

**Similar Documents**: This document discusses a novel approach that merges genetic algorithms (GA) and particle swarm optimization (PSO) to design recurrent neural networks, demonstrating its application in optimizing the design of recurrent neural/fuzzy networks. The proposed hybrid algorithm, HGAPSO, combines the strengths of GA and PSO, using crossover, mutation, and PSO principles to generate new generations of network designs. By employing the elite strategy, where high-performing individuals are further enhanced through PSO before being included in the next generation, the algorithm ensures the continuous improvement of network designs. The effectiveness of HGAPSO is showcased through its application in the design of fully connected recurrent neural networks for temporal sequence production and Takagi-Sugeno-Kang-type recurrent fuzzy networks for dynamic plant control. Comparative analysis with traditional GA and PSO methods reveals the superior performance of HGAPSO in recurrent network design. However, this document does not directly address the Economic Dispatch Problem with Valve-Point Effect; it presents a related methodology that could potentially be adapted for optimization problems in various fields, including power systems.

Flipped classroom approach has been increasingly adopted in higher institutions. Although this approach has many advantages, there are also many challenges that should be considered. In this paper, we discuss the suitability of this approach to teach computer programming, and we report on our pilot experience of using this approach at Qatar University to teach one subject of computer programming course. It is found that students has positive attitude to this approach, it improves their learning. However, the main challenge was how to involve some of the students in online learning activities.

From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.

Online behavioural targeting is one of the most popular business strategies on the display advertising today. It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising. Being able to identify “unknown” and “first time seen” customers is of high importance in online advertising since a successful guess could identify “possible prospects” who would be more likely to purchase an advertisement’s product. By identifying prospective customers, online advertisers may be able to optimise campaign performance, maximise their revenue as well as deliver advertisements tailored to a variety of user interests. This work presents a hybrid approach benchmarking machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting in process oriented environments. The performance of our suggested methodology is evaluated using the key performance metric in online advertising which is the predicted conversion rate. Our experimental results indicate that the presented process mining framework can significantly identify prospect customers in most cases. Our results seem promising, indicating that there is a need for further workflow research in online display

Customer churn creates a huge anxiety in highly competitive service sectors especially the telecommunications sector. The objective of this research was to develop a predictive churn model to predict the customers that will be to churn; this is the first step to construct a retention management plan. The dataset was extracted from the data warehouse of the mobile telecommunication company in Thailand. The system generated the customer list, to implement a retention campaign to manage the customers with tendency to leave the company. WEKA software was used to implement the followings techniques: C4.5 decision trees algorithm, the logistic regression algorithm and the neural network algorithm. The C4.5 algorithm of decision trees proved optimal among the models. The findings are unequivocally beneficial to industry and other partners.

We survey promising directions for research in the area of formal methods and its applications, including fundamental questions about the combination of formal methods and machine learning, and applications to cyberphysical systems and education.

As deregulation, stew technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers: A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory.

This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the

As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.

While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attention. The momentum behind and scrutiny of student privacy has made it increasingly difficult for EDM applications to transition from academia to industry. Based on experience as academic researchers transitioning into industry, we present three primary areas of concern related to student privacy in practice: policy, corporate social responsibility, and public opinion. Our discussion will describe the key challenges faced within these categories, strategies for overcoming them, and ways in which the academic EDM community can support the adoption of innovative technologies in large-scale production.

The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.