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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Qadir, Brzo Aziz Ati, Modafar Erbil Polytechnic University Department of Automation Industrial Technology Erbil Iraq Abu Dhabi University Department of Computer Science & Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331532895

The DC-DC buck-boost SiC base that was shown in this research uses a PID controller converter and primarily displays a slightly lower output voltage than the input voltage. With the exception of the SiC Schottky diode, PID controller, paired inductors, and switching capacitor, it is composed of the same components as a traditional DC-DC buck boost converter. The efficiency of the suggested DC-DC buck boost SiC base employing PID controller converter and traditional DC-DC buck boost converter have been compared and contrasted utilizing a PSIM simulation. The outcome demonstrates that, in terms of both switching frequency and load change, the suggested DC-DC buck boost SiC base employing PID controller converter has a greater efficiency than the traditional DC-DC buck boost converter. In order to use inverter power supplies and consume less power and operate more efficiently, modern electronic devices require clean energy. © 2024 IEEE.

关键词： Buck-Boost converter

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Cybersecurity Challenges to VANET When RFID Tags Utilized: a Study 3

Cybersecurity Challenges to VANET When RFID Tags Utilized: a...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Shembesh, Ainor H. Darbi, Mariam K. Benamer, Sami Younis, Younis A. Libyan International Medical University Faculty of It Computer Networks Department Benghazi Libya University of Benghazi Faculty of Information Technology Departwmtnt of Inforamtion Systems Benghazi Libya University of Benghazi Faculty of Information Technology Department of Communication and Computer Networks Benghazi Libya

ISBN: (纸本)9798350319897

An emerging new technology called the Vehicular Ad Hoc Network (VANET) uses wireless technologies to enhance inter-vehicle communications and traffic safety. Due to its hybrid network designs, node movement characteristics, and novel application scenarios, VANET differs from other types of ad hoc networks. Moreover, the VANET utilizes some technologies for navigating. One of these technologies is Radio Frequency Identification (RFID) technology, which is used in many fields and applications to facilitate good communication and management of resources. Although RFID technology helps reduce the complexity of everyday life, it faces some challenges, the most important of that is security, which will be addressed in this paper. This paper extensively surveys the most severe cybersecurity challenges and attacks on VANET-RFID. © 2023 IEEE.

关键词： Cybersecurity

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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Framework for Detection of Malware Using Random Forest Classifier 5th

Framework for Detection of Malware Using Random Forest Class...

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

作者： Manzoor, Mohsin Arora, Bhavna Department of Computer Science and Information Technology Central University of Jammu Samba India Samba181143 India

ISBN: (纸本)9789819906000

Malware poses a challenging threat to this digital world of communication, as it can manipulate or perform any baffling activity inside a computer by corrupting important files and disabling the network system with malicious attacks. Since the malware writers have evolved in their malware designing technique over the past decade, so the detection mechanism should be robust enough to identify the latest malware and also should curb its propagation inside a computer. The traditional. method of malware detection like signature-based. methods fails to detect the new complex malware as the developers now develop variations of these and camouflage the malware. Hence, the malware goes undetected using the traditional techniques. So, there is a dire need for an effective and an efficient malware detection system that could identify and detect the obscured and encrypted malware with maximum accuracy so that the big enterprise or any other institution that relies on the digital exchange of information should not sustain any kind of financial and information loss. The utilization of machine learning in malware detection has been found effective in detecting hidden and obscured malware. This study focuses on a quick and concentrated assessment of malware detection utilizing several machine learning approaches and recommends a framework that uses the Random Forest classifier-based strategy for detecting malware. The maximum accuracy obtained during this research was about 98.5%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and information Organization). All Rights Reserved.

关键词： Deep learning

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Influence Ranking of Hate Users in Content Propagation on Twitter 5

Influence Ranking of Hate Users in Content Propagation on Tw...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Perera, Suresha Perera, Indika Ahangama, Supunmali University of Moratuwa Department of Computer Science and Engineering Katubedda Sri Lanka University of Moratuwa Department of Information Technology Katubedda Sri Lanka

ISBN: (纸本)9798331530983

Social media platforms are now essential for communication, but they also lead to the quick spread of hate speech, which leaves people and society at serious risk. To lessen the influence of hate content, it is crucial to identify the top influential users who spread it. This research ranks the influence of hate users who propagate Twitter content. A novel method of Influence Score for measuring user influence was developed by integrating activity and engagement metrics, such as the number of hate tweets, retweets, likes, and replies. Users were divided into LOW, MEDIUM, and HIGH influence groups using k-means clustering, which allowed for a more focused examination of user behaviour. The findings showed that a smaller percentage of HIGH-influence users contribute to spreading hate content than users with LOW and MEDIUM influence. The validation process made clear how much better the suggested influence score was than followers, highlighting the value of using many metrics to find real influencers. The validation of the Influence Score was confirmed by comparison with crowdsourced rankings. With its scalable approach for detecting and classifying the influence of hate users, this study gives policymakers and social media administrators useful information. Platforms can significantly reduce the spread of hate content by targeting user clusters based on their influence levels and implementing actions based on the severity of each cluster. Future researchers can adopt this as a validation method for evaluating and improving their hate user ranking approaches. © 2025 IEEE.

关键词： Tweets

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Application of computer Vision and Deep Learning Neural Network in Multi-Modal information Fusion 2

Application of Computer Vision and Deep Learning Neural Netw...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Wu, Qiongfei Xie, Qubo Xia, Dingchun Wuhan Institute of Design and Science Department of Information Engineering Hubei Wuhan China School of Computer Science&Technology Huazhong University of Science and Technology Hubei Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan China

ISBN: (纸本)9798350373110

Multimodal data often requires manual annotation for training, and the annotation process is tedious and time-consuming. The lack of labels makes it difficult to build large-scale labeled multi-modal datasets. In response to the current problems, this article proposes a solution based on a deep neural network algorithm to classify and identify multi-modal data and realize mutual conversion between different features. It can be seen from the experimental results that when the sample error is less than 5%, using deep model training can significantly reduce the difference between the original sample linearity and the local maximum likelihood. This article adopts a multi-layered structure of hyper-realization method to achieve higher accuracy than traditional artificial prediction values. Through verification, it was found that the average coefficients of the hidden layer and additive features were 0.53 and 0.67 respectively, there were multiple correlations between these two data, the closer the correlation coefficient was to 1, then they were positively correlated;the average data input convergence was 64%, and the average output convergence was 72.3%;the matching efficiency of this fusion model could reach up to 97%. © 2024 IEEE.

关键词： information fusion

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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Throughput Enhancement in Wireless Sensor Network by Novel Genetic Algorithm-Fuzzy Method (NGA-F) 3rd

Throughput Enhancement in Wireless Sensor Network by Novel G...

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3rd International Conference on VLSI, communication and Signal processing, VCAS 2020

作者： Prakash, N. Rajalakshmi, M. Nedunchezhian, R. Department of Electronics and Communication Engineering KIT-Kalaignarkarunanidhi Institute of Technology Coimbatore India Department of Computer Science and Engineering and Information Technology Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789811627606

Demodulating the unsafe hubs along with reducing the vitality squander in sensor hubs can drag out the life expectancy of remote sensor systems. By doing the examination, adaptive heuristic search algorithms and an adaptive neuro fuzzy inference system (ANFIS) framework were utilized to lessen the vitality misuse of sensors. Biased trust assessment was connected to look for unsafe hubs in the system for drawing out the life expectancy of the sensor network. Low-vitality versatile grouping chain that has some importance strategies were utilized for breaking down the outcomes. It was found that looking for destructive hubs having genetic algorithm (GA)—Adaptive Network-based Fuzzy Inference System (ANFIS) utilizing subjective trust assessment altogether expanded the life expectancy of WSNs. For assessment of the proposed technique that was utilized by the means of vitality of all sensors against the round, information that are obtained in base station, least vitality versus adjusts and number sensors that are alive sensors versus rounds. Likewise, the manuscript has the proposed strategy along with the results and Dual Fuzzy Logic-Cluster Protocol (DFLCP), Low-Energy Adaptive Clustering Hierarchy (LEACH), Centralized Low-Energy Adaptive Clustering Hierarchy (LEACH-C), Enhanced Distributed Energy Efficient Clustering (EDEEC), Hybrid Energy Efficient Distributed clustering (HEED). As results the proposed Novel Genetic algorithm with Fuzzy (NGA-F) strategy has high life time than different techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy inference

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Video key concept extraction using Convolution Neural Network 3

Video key concept extraction using Convolution Neural Networ...

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3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024

作者： Sardar, Tanvir H Amin Hazarika, Ruhul Pandey, Bishwajeet Guru Prasad, M.S. Mahmudul Hassan, Sk Dodmane, Radhakrishna Gohel, Hardik Dept. of Computer Science & Engg Bengaluru India Manipal Institute of Technology Department of Information Technology Bengaluru India Astana It University Department of Intelligent System and Cyber Security Kazakhstan Dept. of Computer Science & Engg. Dehradun India VIT-AP School of Computer Science and Engineering India Nmam Institute of Technology Department of Computer Science & Engg. Nitte India University of Houston Department of Cse Victoria United States

ISBN: (纸本)9798350381856

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video. © 2024 IEEE.

关键词： Speech recognition

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