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3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kumar, Shailender Divyanka Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9781665438117

A sensational rise of various applications and businesses has led to the rise of data being collected, it is getting harder to store this data on a single machine. It has become more feasible to manage dispersed data sets and organizations. That is the reason distributed data mining methods have become popular. Perhaps the main hurdle of data mining is to perform data clustering. While many clustering calculations exist for certain and uncertain data sets, there is need of productive calculations for distributed data sets. In this paper, a modified hierarchical clustering algorithm is proposed for uncertain dataset to improve timing in execution of the process. © 2021 IEEE.

关键词： Cluster analysis

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A Case Study of Anomaly Detection in Tinplate Lids: Supervised vs Unsupervised Approaches

A Case Study of Anomaly Detection in Tinplate Lids: Supervis...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Henry O. Velesaca Angel D. Sappa Juan A. Holgado-Terriza ESPOL Polytechnic University FIEC - CIDIS Guayaquil Ecuador Software Engineering Department University of Granada Granada Spain Computer Vision Center Barcelona Spain

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This research presents a comparative study of deep learning-based anomaly detection methods, both supervised and unsupervised, applied to industrial systems for detecting product defects in manufacturing. The study implements the OPC-UA protocol for data communication and algorithm execution using finite state machines, demonstrating its practical application in a tinplate lid system. Integration with OPC-UA ensures realtime data access, interoperability, and scalability across various industrial environments. The experimental results, evaluated using metrics such as Average Precision, Mean AUROC, Mean Pixel AUROC, and Execution Time (CPU and GPU), reveal the strengths and limitations of each approach, providing valuable insights for addressing modern challenges in industrial anomaly detection.

关键词： Deep learning YOLO Visualization Protocols Accuracy Service robots Scalability Automata Security Anomaly detection

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CLeAR: continual learning on algorithmic reasoning for human-like intelligence 23

CLeAR: continual learning on algorithmic reasoning for human...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bong Gyun Kang HyunGi Kim Dahuin Jung Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of humans who are excellent continual learners. While most research on CL has been done on structured data such as images, there is a lack of research on CL for abstract logical concepts such as counting, sorting, and arithmetic, which humans learn gradually over time in the real world. In this work, for the first time, we introduce novel algorithmic reasoning (AR) methodology for continual tasks of abstract concepts: CLeAR. Our methodology proposes a one-to-many mapping of input distribution to a shared mapping space, which allows the alignment of various tasks of different dimensions and shared semantics. Our tasks of abstract logical concepts, in the form of formal language, can be classified into Chomsky hierarchies based on their difficulty. In this study, we conducted extensive experiments consisting of 15 tasks with various levels of Chomsky hierarchy, ranging from in-hierarchy to inter-hierarchy scenarios. CLeAR not only achieved near zero forgetting but also improved accuracy during following tasks, a phenomenon known as backward transfer, while previous CL methods designed for image classification drastically failed.

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

arXiv

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arXiv 2024年

作者： Su, Xiaoxin Zhou, Yipeng Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR Hong Kong

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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An integrated fuzzy adjusted cosine similarity and TOPSIS based recommendation system for information system requirements selection

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Decision Analytics Journal 2024年 11卷

作者： Akram, Faiz Ahmad, Tanvir Sadiq, Mohd. Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

Selecting the requirements for an information system based on their ranking values is one of the critical steps in software development. Various methods for computing the ranking order of the requirements have been developed using multi-criteria decision-making methods. This study shows selecting the requirements for different software releases using a recommendation system is an unresearched problem. We present a recommendation system (PRecSys) for computing the ranking order of the requirements in an information system using fuzzy-based adjusted cosine similarity measure and collaborative filtering to recommend the requirements to stakeholders with similar interests. The ranking order of the information systems requirements in PRecSys is computed using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The applicability of PRecSys is discussed by considering the requirements for a library information system. The PRecSys is compared with PHandler, an expert system for choosing the requirements, based on the following criteria: agreement measure, the number of judgments by decision-makers, and time complexity. © 2024 The Authors

关键词： Collaborative filtering Fuzzy TOPSIS Library information system Multi-criteria decision making Recommendation system Requirements elicitation

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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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An improved kNN method based on Spearman's rank correlation for handling medical missing values

An improved kNN method based on Spearman's rank correlation ...

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2022 International Conference on Machine Learning and Knowledge engineering, MLKE 2022

作者： Liang, Chuchao Zhang, Lei Wan, Zhi Li, Dongze Li, Dong Li, Wentao Sichuan University School of Computer Science School of Software Engineering Chengdu610065 China Sichuan University West China Hospital Department of Emergency Medicine Chengdu610041 China

ISBN: (纸本)9781665495677

Acute chest pain is a common symptom of cardiovascular disease, and its data have important research value. However, the presence of missing value in medical datasets is almost inevitable, which may adversely affect the results of medical data mining. The imputation method plays an important role in the missing value processing method and it can avoid the loss of information due to sample deletion. In this paper, we proposed a new weighted kNN method based on Spearman correlation, named Spearman-Weights kNN(SW-kNN). It is used for missing value imputation in medical datasets that contain a large proportion of samples with missing values. We conducted experiments on the acute chest pain dataset from West China Hospital and compared SW-kNN with seven other methods (Zero, Median, Mean, Random Forest, kNN, NS-kNN, DNNI). The results show that our proposed method has the best performance among all imputation methods in the experiments, which indicates that SW-kNN can successfully handle missing values in medical data. © 2022 IEEE.

关键词： Decision trees

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A knowledge-driven framework for synthesizing designs from modular components 34

A knowledge-driven framework for synthesizing designs from m...

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34th CIRP Design Conference, CIRP 2024

作者： Chaumet, Constantin Rehof, Jakob Schuster, Thomas Software Engineering by Algorithms and Logic Department of Computer Science TU Dortmund University Otto-Hahn-Str. 12 Dortmund44227 Germany Lamarr Institute for Machine Learning and Artificial Intelligence TU Dortmund University Joseph-von-Fraunhofer-Straße 25 Dortmund44227 Germany

Creating a design from modular components necessitates three steps: Acquiring knowledge about available components, conceiving an abstract design concept, and implementing that concept in a concrete design. The third step entails many repetitive and menial tasks, such as inserting parts and creating joints between them. Especially when comparing and implementing design alternatives, this issue is compounded. We propose a use-case agnostic knowledge-driven framework to automate the implementation step. In particular, the framework catalogues the acquired knowledge and the design concept, as well as utilizes Combinatory Logic Synthesis to synthesize concrete design alternatives. This minimizes the effort required to create designs, allowing the design space to be thoroughly explored. We implemented the framework as a plugin for the CAD software Autodesk Fusion 360. We conducted a case study in which robotic arms were synthesized from a set of 28 modular components. Based on the case study, the applicability of the framework is analyzed and discussed. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer circuits

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ACE-PE: An Automated Code Evaluation software Tool For programming Education

ACE-PE: An Automated Code Evaluation Software Tool For Progr...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Burcu Alper Selma Nazlioglu Hürevren Kilic Cybersoft Information Technologies Bilkent Cyberpark Ankara Turkey Department of Software Engineering Atilim University Ankara Turkey Department of Computer Engineering Atilim University Ankara Turkey

An automated code evaluation tool that combines the usage of software quality metrics and object-oriented programming teaching subjects is designed and developed. The tool (called ACE-PE) gives flexibility to instructors to assess student assignments at the level of precision of specific subjects which reveals the degree of student's understanding of covered subjects, and to observe his/her own effort, as well. Provision of content-aware automated fast feedback to students to improve quality of their products and development efforts is another outcome of the proposed solution.

关键词： Codes software metrics Education software quality User interfaces Security Usability

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

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International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Artificial Intelligence Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The paper proposes “AdaptVR” a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique.

关键词： Training Vibrations Drilling Solid modeling Adaptive learning Adaptation models Force feedback Virtual reality Teeth Dentistry

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