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An automated framework for advertisement detection and removal from sports videos using audio-visual cues

Frontiers of computer Science 2021年第2期15卷 185-188页

作者： Abeer TOHEED Ali JAVED Aun IRTAZA Hassan DAWOOD Hussain DAWOOD Ahmed S.ALFAKEEH Department of Software Engineering University of Engineering and Technology-TaxilaTaxila47050Pakistan Department of Computer&Network Engineering University of JeddahJeddah21577Saudi Arabia Information System Department King Abdulaziz UniversityJeddah21577Saudi Arabia

1 Introduction Advertisements detection and replacement with different ads based on the user preferences is employed during sports rebroadcasts that offers more value to both the distributor and *** advertisements detection is a laborious activity and demands an urgent need to develop automated advertisement detection techniques to save the time,storage space,and transmission bandwidth.

关键词： automated visual framework

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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MYSTERIOUS ACTION DETECTION by SPEED and DISPLACEMENT ANALYSIS in IMAGE PROCESSING 1

MYSTERIOUS ACTION DETECTION by SPEED and DISPLACEMENT ANALYS...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vishnupriya, S. Darwin Nesakumar, A. Mounika, S. Pavithra, K.N. R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering India R.M.K. Engineering College Department of Electronics and Communication Engineering India R.M.D.Engineering College Department of Computer Science and Business System India R.M.D.Engineering College Department of Computer Science Engineering India

ISBN: (纸本)9781665456876

There are now three moving target detection algorithms: The optical flow methodology, the background difference technique, and the inter-frame difference method are all employed. The optical flow technique has the most complex algorithm and takes the longest to process data compared to other approaches. The background subtraction(BS) approach, on the other hand, is recognised for its high sensitivity to light changes. Finally, while the frame difference approach is very straightforward and quick to use, the findings are insufficiently accurate due to backdrop brightness fluctuations causing mistakes. In this paper, the objective is to discover strange actions in image processing using speed and displacement analysis. Motion is detected using Incremental and Multi-feature Tensor Subspace Learning (IMTSL) Applied for Background Modeling and Subtraction. Following IMTSL Background Subtraction, different image analysis were performed on the resulting pictures for displacement estimate, followed by speed computation. The results demonstrate improved backdrop modelling and action prediction. © 2022 IEEE.

关键词： Optical flows

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Combating Digital Deception: Machine Learning Approaches for Detecting Political Misinformation and Clickbait on Social Media

Combating Digital Deception: Machine Learning Approaches for...

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Advancements in Computing (ICAC), International Conference on

作者： Dewmini Harindi Nirmal Perera Chamath Liyanage Hirushini Dematagoda Harinda Fernando Samadhi Rathnayake Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This paper presents machine learning-based strategies for mitigating the problem of misinformation in social media, with a special focus on political misinformation detection and clickbait detection. The following research work attempts to address the pressing issue of the rapid spread of false or misleading information by making use of deep recurrent neural networks (RNN s) combined with semi-supervised learning methodologies. For political misinformation detection, a hybrid model integrating Long Short-Term Memory (LSTM) networks with fully connected layers was developed, achieving 94.22 % accuracy on the CIC Truth Seeker dataset. Clickbait detection was made using a sequential architecture that combines LSTM and Gated Recurrent Units (GRU) layers with Simple RNN layers to achieve an accuracy of 96% on test data. This study underlines the effectiveness of different machine learning techniques in battling misinformation that would contribute to online dignity in political discourse and ensure better public communication in a digital world.

关键词： Accuracy Recurrent neural networks Social networking (online) Machine learning Pressing Semisupervised learning Data models Parallel architectures Fake news Long short term memory

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Decision making

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection system(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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IEEE International Conference on systems, Man and Cybernetics

作者： Guotong Wu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC.

关键词： Training Accuracy Machine learning Approximation algorithms Graph neural networks Approximation methods Cybernetics Testing

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Decentralized Federated Deep Reinforcement Learning Framework for Energy-Efficient Base Station Switching Control

Decentralized Federated Deep Reinforcement Learning Framewor...

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International Symposium on Telecommunications, IST

作者： Narges Movahedkor Reza Shahbazian Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnique) Tehran Iran Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Rende Italy

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

G cellular networks have achieved significant improvements in capacity and performance, while they have incurred a substantial increase in energy consumption at the base station (BS) level. To address this escalating energy consumption in dense 5G networks, this paper proposes a decentralized federated learning (DFL)-enhanced Deep Reinforcement Learning (DRL) framework for intelligent BS switching. Our approach leverages the distributed nature of 5G networks to collaboratively learn optimal BS on/off policies without centralized control. By combining DFL with DRL, we enable efficient knowledge sharing among BSs while maintaining privacy and reducing communication overhead. The cost function designed in this paper aims to balance energy savings as well as Quality of Service ($\mathbf{Q o S}$) requirements. Moreover, to enhance exploration and accelerate convergence, we incorporate an exploration network into the DRL agent and adopt a novel approach of model training. Performed simulations demonstrate the effectiveness of our proposed framework in achieving significant energy reduction of over 23% and total cost of 19%, while maintaining satisfactory QoS performance compared to the existing methods.

关键词： Training Base stations Energy consumption Privacy 5G mobile communication Federated learning Switches Quality of service Deep reinforcement learning Telecommunications

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Optimal Strategies Trajectory with Multi-Local-Worlds Graph

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computers, Materials & Continua 2023年第4期75卷 2079-2099页

作者： Xiang Yu Chonghua Wang Xiaojing Zheng Chaoyu Zeng Brij BGupta School of Electronics and Information Engineering Taizhou UniversityTaizhouZhejiang318000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China School of Economics and Management Weifang University of Science and TechnologyShouguangShandong262700China National Computer System Engineering Research Institute of China Beijing102699China Department of Computer Science and Information Engineering Asia UniversityTaiwanTaichung413China Research and Innovation Department Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates Staffordshire University Stoke-on-TrentST42DEUK

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

关键词： Multi-Local-world graph stochastic differential game optimal strategy attractor

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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International Electrical engineering Congress (iEECON)

作者： Waris Rattanimit Thanyarat Nomponkrang Electrical Technology and Automation System Faculty of Engineering and Technology Siam Technology College Bangkok Thailand Department of Computer Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

In the digital era, learning programming has become an essential skill, and assessing students' understanding in this area plays a crucial role in enhancing educational quality. However, evaluating subjective programming exam responses, especially those involving conceptual explanations or procedural descriptions, remains a challenging task due to the diversity and complexity of answers. Manual grading can lead to delays and inconsistencies in scoring. This research aims to develop an automated system for evaluating Thai subjective programming exams using Natural Language Processing (NLP) techniques to assess the correctness of responses. The system is implemented in Python, utilizing the ThaiNLP library for word tokenization, alongside Machine Learning algorithms such as Naive Bayes, Decision Tree, and Support Vector Machine (SVM) for classification and prediction of answers. The experimental dataset includes responses from 120 students who answered subjective questions about Python commands. Expert testing indicated high user satisfaction with an average score of 4.05. The findings show that SVM achieved the highest accuracy at 85.33%. This system can enhance efficiency and consistency in exam evaluation, reducing grading time and contributing to standardized scoring.

关键词： Support vector machines Electrical engineering Machine learning algorithms Manuals Machine learning Tokenization Libraries Programming profession Python Testing

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