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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Anik Saha Doina Logofătu Jiban Kumar Ray Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Amidst an era of speedy technological growth, fraud is a complex challenge. This article presents an innovative analytical method that uses utility consumption data to identify probable instances of fraud in utility-based services. This study evaluates the efficacy of neural networks, specifically Artificial Neural Network (ANN) — a subset of neural networks, compared to Light Gradient Boosting Machine (LGBM), a tree-based model. It uses data from the Zindi challenge to analyze their capability to detect fraudulent consumption patterns. The article focuses on optimizing features using Pearson correlation and discusses the difficulties associated with imbalanced data sets. The LGBM model has exceptional performance, as seen by its impressive ROC AUC score of 0.878242. This number highlights its remarkable ability to differentiate fraudulent actions from ANN, thus establishing future advancements in fraud detection in utility-based services.

关键词： Technological innovation Correlation Artificial neural networks Boosting Fraud Intelligent systems

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An In-Memory Physics Environment as a World Model for Robot Motion Planning

TechRxiv

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TechRxiv 2024年

作者： Ipe, Navin K. Department of Computer Science and Engineering M S Ramaiah University of Applied Sciences Bengaluru India

Conventional robots are capable of detecting terrain and creating a 2D or 3D map of the terrain in memory, which is utilized by the robot’s algorithms to plan navigation. Such algorithms are primarily focused on path optimality, gaits and joint positioning. This paper investigates the possibility of utilizing a physics simulation environment as the imagination of a robot, where it creates a replica of the detected terrain in a physics simulation environment in its memory, and "imagines" a simulated version of itself in that memory, performing actions and navigation on the terrain. The physics of the environment simulates the movement of robot parts and its interaction with the objects in the environment and the terrain, thus avoiding the need for explicitly programming many calculations. The robot chooses the best possible action from multiple simulations of movement, and executes it in the real world. Moreover, as the complexity of motion increases with each degree of freedom of the robot’s joints, this paper also explores the utility of uniform pseudo-randomness to explore the fitness landscape of robot motility, and compares it with Computational Intelligence algorithms. Such techniques could potentially simplify the algorithmic complexity of programming multi-jointed robots, and also be capable of dynamically adjusting the "mental" simulation of the robot when it encounters environments with different gravity, viscosity or traction, merely by adjusting parameters of the simulated environment. © 2024, CC BY.

关键词： Robot programming

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Dynamic Stacking Optimization in Uncertain Environments: A Rolling Mill Complex System

Dynamic Stacking Optimization in Uncertain Environments: A R...

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Aida Čolović Amina Čolović Doina Logofătu Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Dynamic stacking optimization is of critical importance in industries where timely production and delivery are essential. This process involves the strategic scheduling of crane operations to relocate products while adhering to stringent time constraints. This paper presents a study on dynamic stacking optimization for the Rolling Mill track of the DynStack competition hosted at GECCO 2024. The primary objective is to develop a heuristic-based approach to minimize rolling program messups, blocked mill time, and crane manipulations. The challenges posed by the uncertainty in block arrivals and the need for real-time decision-making are addressed. Various heuristic and optimization techniques were explored, with an emphasis on enhancing crane efficiency and block sorting accuracy.

关键词： Cranes Job shop scheduling Accuracy Uncertainty Tracking Heuristic algorithms Stacking Real-time systems Optimization Sorting

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Level Generation Using ChatGPT: A Case Study on the science Bird Game

Level Generation Using ChatGPT: A Case Study on the Science ...

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Julia Garbaruk Jiban Kumar Ray Doina Logofătu Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

The increasing complexity of video games and development costs have necessitated innovative approaches to content creation. Procedural Content Generation (PCG) and AI tools like ChatGPT offer promising solutions. This paper explores the application of ChatGPT in Procedural Level Generation (PLG) for the game “science Birds”. Through a combination of theoretical exploration and practical experiments, we demonstrate how ChatGPT can be leveraged to automate level design, enhancing both efficiency and creativity in game development.

关键词： Video games Procedural generation Costs Scientific computing Chatbots Birds Complexity theory Artificial intelligence Creativity

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Energy Management Systems for Electric Vehicle Charging Stations: A Review 48

Energy Management Systems for Electric Vehicle Charging Stat...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Golder, Anindita Williamson, Sheldon S. Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Looking at how electric vehicle charging stations are using renewable and clean energy resources such as fuel cells, solar photovoltaic and energy storage systems to reduce the impact on the grid, it is important that these resources are managed optimally. Therefore, the energy management systems (EMS) play a significant role for charging stations. In this paper, we analyze the different strategies and types of energy management systems used for electric vehicle charging stations. © 2022 IEEE.

关键词： Electric vehicles

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Toward Human-Robot Teaming for Robot Navigation Using Shared Control, Digital Twin, and Self-Supervised Traversability Prediction

Toward Human-Robot Teaming for Robot Navigation Using Shared...

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Telepresence, IEEE Conference on

作者： Trung Kien La Eric Guiffo Kaigom Department of Computer Science & Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany

ISBN: (数字)9798331528249

ISBN: (纸本)9798331528256

Collision prediction and avoidance are essential for the autonomous navigation of mobile robots. The prediction of a traversability map is a way to achieve this goal. However, this approach might fail if the prediction model is exposed to novel semantic classes unseen during self-supervised training or the environment is subject to highly dynamic motions of living and artificial entities. Included are pedestrians, animals, and vehicles that the robot can hardly handle. In this paper, we embrace this challenge by describing the development of a flexible human-robot teaming that leverages the shared control of the robot to accommodate critical situations and allow the human operator to be otherwise engaged. The operator can remotely perceive the surrounding and actively guide the robot as well as participatively share or leave full control to the robot that autonomously moves toward a desired common goal. A situation-aware control signal balances inputs from the motion planner of the autonomous robot and cognitive inputs from the remote operator issued by using affordable interfaces. The human-in-the-loop control is realized through a bidirectional wireless communication between the physical robot and its digital twin. The human-robot teaming enhances the likelihood of a safe and successful navigation under dynamic obstacles and enables a navigation without pre-defined goals. The conceptual architecture is introduced and preliminary results are shared.

关键词： Wireless communication Training Telepresence Navigation Dynamics Semantics Digital twins Vehicle dynamics Robots Autonomous robots

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Multi-Layered Deep Learning Features Fusion for Human Action Recognition

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computers, Materials & Continua 2021年第12期69卷 4061-4075页

作者： Sadia Kiran Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Yunyoung Nam Robertas Damaševicius Muhammad Sharif Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Mathematics Silesian University of TechnologyGliwicePoland Department of Computer Science COMSATS University IslamabadWah CampusPakistan

Human Action Recognition(HAR)is an active research topic in machine learning for the last few *** surveillance,robotics,and pedestrian detection are the main applications for action *** vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of *** this article,we proposed a new method for the use of deep learning for *** the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer *** Resnet-50 Pre-Trained Model is used as a deep learning model in this *** are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold *** selected features are finally passed to multiple classifiers for final *** are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and *** accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,*** with existing techniques has shown that the proposed method provides improved accuracy for ***,the proposed method is computationally fast based on the time of execution.

关键词： Action recognition transfer learning features fusion features selection classification

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Fault Analysis of Microgrids with Inverter Interfaced Resources in Grid-Connected and Islanded Modes 1

Fault Analysis of Microgrids with Inverter Interfaced Resour...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Sharma, Jigyesh Lulbadda, Kushan Tharuka Golder, Anindita Sidhu, Tarlochan Williamson, Sheldon S. Faculty of Engineering and Applied Science Department of Electrical and Computer Engineering OshawaONL1G 0C5 Canada

ISBN: (纸本)9798350398069

The increasing penetration of inverter-based generation such as solar, wind and battery energy storage systems (BESS) has an impact on the fault currents in a microgrid. Also, if the microgrid is grid-connected or islanded affects the fault current. As such, it is important to conduct fault analysis of microgrids in grid-connected and islanded modes with different penetration levels of inverter-based generation sources to understand the impact of increasing levels of renewable energy on the fault currents. Different types of faults in a microgrid with inverter-based resources are simulated and the results are discussed and analyzed in this paper. © 2022 IEEE.

关键词： Electric inverters

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Computational Model Based on CNN to Identify Masses from Liver Images 2

Computational Model Based on CNN to Identify Masses from Liv...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Jyothirmai, Ch. N. V. Hemalakshmi, G.R. Prakash, N.B. Upadhye, Vijay Jagdish Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of CSE Andhra Pradesh 521 225 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh 466114 India National Engineering College Department of Electrical and Electronics Engineering Kovilpatti 628503 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat 391760 India Department of Computer Science and Engineering Maharashtra 412 115 India Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Saveetha School of Engineering Chennai602105 India

ISBN: (纸本)9798350327533

Therefore, Convolutional Neural Networks (CNNs) are capable of predicting or extrapolating from identified patterns to unknown data, enabling the analysis and extraction of knowledge from large images, in order to produce consistent outputs for inputs not previously presented. On the downside, the training process takes a long time as it is computationally expensive. Aiming to reduce the computational time to perform the training of the neural network (NN), and the analysis of images during the testing process, as well as taking advantage of the natural distributed characteristic of ANN, it was decided to transfer the processing-intensive parts of the tool from CPU (Central Processing Unit) to a GPU (Graphics Processing Unit). Finally, simulator tests were carried out, with consequent verification of the effectiveness of diagnoses on ultrasound images of the liver. The accuracy and total processing time for carrying out the analyses proposed during this research were then evaluated, making a comparison between the network training and testing times obtained on the CPU and GPU. It can also be seen-in both Tr&Tt cases-that GPU had a higher gain than CPU (about 10.6 times faster), even when dealing with a small database, with 120 images of 202500 pixels each. © 2024 IEEE.

关键词： Central Processing Unit Convolutional Neural Networks Graphics Processing Unit Masses Ultrasound liver images

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NoteLink: A Point-and-Shoot Linking Interface between Students' Handwritten Notebooks and Instructional Videos 21

NoteLink: A Point-and-Shoot Linking Interface between Studen...

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Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries

作者： Ranjitha Jaddigadde Srinivasa Samuel Dodson Kyoungwon Seo Dongwook Yoon Sidney Fels Department of Electrical & Computer Engineering University of British Columbia Department of Information Science University at Buffalo Department of Applied Artificial Intelligence Seoul National University of Science and Technology Department of Computer Science University of British Columbia

ISBN: (纸本)9781665417709

When learning from instructional videos, students frequently take handwritten notes to improve recall and comprehension. When reviewing their notes, it can be difficult to return to the corresponding part of the video. In this paper, we present NoteLink, a mobile application that allows students to take pictures of their notes to re-find and play relevant videos on their smartphone or tablet. Our study followed four phases. In Phase I, we identified the characteristics of students' notes by analyzing 10 engineering students' handwritten notes taken while watching instructional videos. We found: 1) students' notes are comprised of four content types: text, formula, drawing, and a hybrid of two or more types, 2) at least 75% of the notes, regardless of content type, manifest some degree of verbatim overlap with the corresponding video content, and 3) videos are referenced at three scales of temporal granularity: point, interval, and whole video. In Phase II, we designed a prototype mobile application, NoteLink, that retrieves instructional videos that are similar to students' notes. In Phase III, we ran a usability study with 12 engineering students to evaluate their preferences for the temporal granularity of retrieved videos and how search results are displayed. Students reported a preference for matches at the interval temporal granularity. Interviews with participants suggest that NoteLink-like tools for re-finding instructional videos are useful. In Phase IV, we evaluated the retrieval accuracy of NoteLink using the data collected in Phase I. The overall accuracy was 78%, and 98% for textual notes. We also provide design recommendations for optimizing NoteLink.

关键词： handwritten recognition

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