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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Saeed Hajnayeb Fatemeh Amerian Mohammad Hoseintabar Marzebali Vahid Abolghasemi Seyyed Meysam Seyyedbarzegar Faculty of Electrical Engineering Department Shahrood University of Technology Shahrood Iran School of Computer Science and Electronic Engineering Department University of ESSEX Colchester UK

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Induction machines are widely used in industrial and household applications due to their efficiency in electromechanical energy conversion. However, broken rotor bars can significantly disrupt machine performance, typically manifesting as sideband harmonics near the fundamental frequency which can be utilized for fault detection. Nonetheless, frequency leakage can impede fault detection, concealing fault until substantial damage occurs. The proposed method, based on the rectified three-phase stator current (RTPSC), effectively reduces leakage frequency while maintaining the fault characteristic frequency (FCF) at its original amplitude. In addition, generate the fundamental harmonic at a frequency of 6 times. This approach aims to is to enhance diagnostic capabilities in the maintenance and monitoring of induction machines, improving operational efficiency and it also reduces the chance of catastrophic failures. The proposed methodology is tested on cases involving one, two, and three broken rotor bars, with the simulation results validated through experimental verification.

关键词： Wavelet transforms Time-frequency analysis Fault detection Simulation Rotors Stators Harmonic analysis Wavelet analysis Monitoring Bars

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Enhanced Software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved Software Performance Reliability

Enhanced Software Defect Prediction using Quantum Hamiltonia...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jyoti Chaudhary Vijaya Kumar A V Manikannan K Amol Sapatnekar Amit Barve Ramya Maranan Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani Haryana India Department of Computer Science & Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering R.M.K Engineering College Thiruvalluvar Tamil Nadu India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The development of software systems with high quality and decreased maintenance costs depends on Software Defect Prediction (SDF). The current dataset qualities combined with feature redundancies make confident fault detection difficult through conventional methods. Current defect prediction techniques display multiple deficits through inaccurate results along with many false alarms and dataset bias. A high-performance quantum-inspired method must be researched in working to create an accurate precision model. The project will create the Enhanced Software Defect Prediction using Quantum Hamiltonian Generative Adversarial Network for Improved Software Performance Reliability (DBO-QH-GAN) model. Several software metrics need to be retrieved from the PROMISE datasets before this process can be initiated. The second step includes Fuzzy K- Top Matcher (FK- TM) functionality that removes undesirable noise factors such as redundant features and missing values as well as outliers. The Botox Optimization (BotO) algorithm selects five essential metrics that encompass cyclomatic complexity as well as Halstead measures and coupling and code churn and historical defect density. These preprocessed set of characteristics are fed to a Quantum Hamiltonian Generative Adversarial Network (QH-GAN) which identifies the defects from probabilistic thresholding and thereafter optimized using Duck Bevy Optimization (DBO) to achieve the optimal possible performance. Accuracy of the model was 99.2%, precision was 99.4%, and recall was 99.1 %. The DBO-QH-GAN model sets new standards for defect prediction by providing an improved enhancement of reliability along with decreased maintenance cost.

关键词： Quantum computing Costs Accuracy Software performance Predictive models Generative adversarial networks Software reliability Maintenance Optimization Standards

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Deep Learning Approach for Feed Intake Monitoring in Cattle

Deep Learning Approach for Feed Intake Monitoring in Cattle

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Krishna Saurabh Soni Pranshi Talati Dweepna Garg Parth Goel Department of Computer Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR) Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Anand India Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR) Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Anand India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

A crucial indicator of animal health and feed efficiency is the precise recording of animals’ daily feed intake. In the past, average feed intake for a bunch of animals or manual measures have been used, leading to human error and generally inconsistent results for individual animals. The aim of this study is to develop an accurate model by utilizing transfer learning to improve performance and save computational costs when classifying images of cattle feed according to the amount of leftovers. The suggested method classifies feedlot images into six bunk score categories (score 0, score ½, score 1, score 2, score 3, and score 4) using pre-trained models such as ResNet50, MobileNetV2, DenseNet121, EfficientNetB2, and EfficientNetB3. Among these the EfficientNetB3 model demonstrated superior performance, achieving the highest validation accuracy of 86%, thereby outperforming the other four pre-trained models utilized in this study.

关键词： Renewable energy sources Accuracy Animals Computational modeling Transfer learning Cows Recording Feeds Monitoring Residual neural networks

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Enhancing Physical-Layer Security in Full-Duplex Cooperative Wireless Networks over α–μ Fading Channels

Enhancing Physical-Layer Security in Full-Duplex Cooperative...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Ayam Saddouq Mamoun F. Al-Mistarihi Mahmoud A. Khoderi Khalid A. Darabkh Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid Jordan Computer Engineering Department School of Engineering The University of Jordan Amman Jordan

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

Cooperative communication is an emerging method that allows devices with a single antenna to share their antennas and assist other nodes in transmitting signals. This leads to enhanced spatial diversity, lower power consumption, and increased reliability of signal reception. This study uses full-duplex mode and $\alpha-\mu$ fading channels and derives the secrecy outage probability for a single relay to assess the system’s secrecy performance and examine how utilizing jamming signals affects system security. Theoretical results for the derived expression are given.

关键词： Fading channels Wireless networks Transmitting antennas Full-duplex system Probability Throughput Power system reliability Security Relays Jamming

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Revolutionizing B2B E-Commerce: A Legal Study on the Transformative Impact of Hybrid AI Integration 4

Revolutionizing B2B E-Commerce: A Legal Study on the Transfo...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bulakh, Ashish Kumar Sajan, Vishnu Gupta, Ashwani Kumar Singh, Vratika Pundir, Dhannjay Singh Rao, N. Venkata Cdgi Department of Computer science and Engineering Indore India School of Management Studies Bannari Amman Institute of Technology Sathyamangalam India Chandigarh University Faculty of Management and Commerce Unnao India Mullana Haryana Ambala India Department of Management Studies AP India

ISBN: (纸本)9798331529635

The rapid expansion of B2B e-commerce has introduced new challenges in managing legal risks, ensuring contract compliance, and resolving disputes efficiently. This paper presents a novel Hybrid Model Integrating Transformer-based Architecture (HMTBA) using BERT and Variational AutoEncoder (VAE) for enhanced legal risk prediction in B2B e-commerce. The proposed hybrid deep learning model addresses the growing complexities in contract compliance, legal disputes, and transactional analysis by combining the strengths of BERT for textual understanding and VAE for dimensionality reduction of high-dimensional structured data. The model was evaluated against nine existing models, including CNN, RNN, GRU, LSTM, Transformer, XGBoost, BERT, VAE, and Hybrid CNN-LSTM, using a dataset of 4318 data points consisting of B2B transactions, legal case studies, contracts, and compliance reports. The proposed HMTBA model achieved superior performance with an accuracy of 95.82%, precision of 93.72%, recall of 94.35%, and F1-score of 94.03%. This significant improvement over existing models demonstrates the potential of HMTBA to enhance legal risk management and improve contract compliance in B2B e-commerce. The results also show that despite higher computational demands, the accuracy and robustness of the proposed model offer a transformative solution for legal analysis and compliance monitoring in e-commerce platforms. © 2024 IEEE.

关键词： Dimensionality reduction

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Exploring Worst Arc Flow Minimization: A Comparative Study of a Provided Wireless Network and Its Derivation via Spanning Tree Topology 20th

Exploring Worst Arc Flow Minimization: A Comparative Study o...

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Adasme, Pablo Viveros, Andres Soto, Ismael Firoozabadi, Ali Dehghan Rodríguez, Demóstenes Zegarra Department of Electrical Engineering Faculty of Engineering Universidad de Santiago de Chile Avenida Víctor Jara 3519 Estación Central Santiago Chile Department of Electricity Universidad Tecnológica Metropolitana Av. Jose Pedro Alessandri 1242 Santiago Chile Department of Computer Science Federal University of Lavras Lavras37200-000 Brazil

ISBN: (纸本)9783031680045

This paper addresses the challenge of attaining an optimal wireless spanning tree backbone with a dual focus: minimizing both the worst arc flow within the network and the overall tree structure. Our approach assumes node-to-node simultaneous communication and introduces mixed-integer linear programming formulations to tackle the problem. The primary formulation adopts the Miller-Tucker-Zemlin approach to derive the tree backbone. Additionally, we consider its exponential counterpart, characterized by its numerous constraints. To evaluate the models, we conduct a comparative analysis considering CPU processing times, solution qualities, and Mipgaps achieved. Subsequently, we explore scenarios where the emphasis is solely on minimizing the worst arc flow while omitting the tree backbone. Our initial numerical findings indicate that excluding the tree backbone from the input graph notably reduces the burden of worst arc flow. However, this benefit forces the need for a denser connected graph. So far, the computational experiments encompass instances with up to 25 nodes for both sparse and complete graphs. Finally, our study underscores the significance of the proposed models, well-suited for assessing diverse transmission flow-based scenarios while accommodating forthcoming technologies in the evolution of 5G, 5G+, and 6G future infrastructure networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Flow balancing in wireless networks Flow-based models Future wireless network communications Mixed-integer linear programming Worst arc flow minimization

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Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own Information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

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Impact of artificial intelligence on civilization: Future perspectives

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Materials Today: Proceedings 2022年 56卷 252-256页

作者： Rajendra, P. Kumari, Mina Rani, Sangeeta Dogra, Namrata Boadh, Rahul Kumar, Ajay Dahiya, Mamta Department of Mathematics CMR Institute of Technology Bengaluru India Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Gurugram Haryana India Department of Computer Science and Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122505 India Department of Orthodontics and Dentofacial Orthopaedic Shree Guru Gobind Tricentenary University Haryana Gurugram122505 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122505 India

Artificial intelligence is a scientific term that refers to artifacts, detect situations and respond to those circumstances with actions. The ability to create such improved artifacts has more impact on our society. This paper describes the economic and social changes with the use of artificial intelligence since the beginning of smartphones. Smartphones have contributed significantly to big data and that adds more efficiency to machine learning. Artificial intelligence goes on to explain the political, economic, and personal issues that humanity will face soon, as well as regulatory strategies to address them. In general, Artificial intelligence isn't always as precise a generation as one would possibly anticipate, and the problems it increases can be extra vital as a result. Because of extended get entry to understanding of each people and nations, there's a danger of threatening identification and autonomy. © 2022

关键词： Artificial intelligence

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Transforming Business Management Practices for Increased Production through Deep Learning and Cloud Computing integration

Transforming Business Management Practices for Increased Pro...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： P. Santhosh Kumar Anupam Singh Soniya Shaik Farhana Amar Choudhary Susmi Biswas Department of Information Technology Ramapuram Faculty of Engineering and Technology Srm Institute of Science and Technology Chennai India Department of Computer Science and Engineering Graphic Era Hill University Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Management Sanskar College of Engineering Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Management Science Haldia Institute of Management MAKAUT India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This research is vital to meet the need for improved predictive maintenance in manufacturing, thus enabling predictive operations and minimizing expensive downtime. The aim is to assess and compare different machine learning models like Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees( DT ) with respect to predicting maintenance requirements using historical sales data, social media trends, and machinery sensor readings. The methodology included gathering a dataset of 3,200 records from the industry and then the pre-processing phase to compute the preprocessing operations like data cleaning, normalization and feature selection. The various models were then trained and tested, using 70% of the data for training to generalise the model, with the rest of the unseen test set consisting of the remaining 30%. The models were evaluated based on performance metrics (accuracy, precision, recall, F1 score and Dice Similarity Coefficient or DSC). Results showed the proposed ANN model as the most accurate and efficient prediction mechanism in comparison with precision, recall and F1 score for remaining useful life (RUL) predictions. The SVM model also worked well, with an accuracy of 95.45% but low rates of false positives and negatives. Although the DT model was informative, its performance (93.34% accuracy) was less robust than that of our RF ensemble trainer. This research demonstrates the use of these findings to be applied in real-time maintenance prediction by ANN. This research would help industries and other manufacturing sectors to reduce downtime, achieve optimal planned maintenance scheduling and improve production efficiency.

关键词： Support vector machines Accuracy Computational modeling Artificial neural networks Production Predictive models Data models Real-time systems Maintenance Manufacturing

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