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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Chowdhury, Shovon Das Ahmed, Tanjil Amin, Ruhul Chowdhury, Rahat Ahmed Tanjim, Shah M.D. Lysuzzaman, Md Sylhet Engineering College Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350350289

The escalating growth in population has led to a substantial increase in both private and public transportation on roads. Consequently, the surge in accidents, traffic rule violations, and other traffic-related offenses has become a daily concern. Addressing these issues is crucial for ensuring the safety of passengers and enhancing transparency in driver recruitment, especially when considering violations that may have occurred abroad. This paper introduces S- DrivingRecords, focusing on pivotal functionalities such as the registration of cases related to traffic rule violations or accidents, case withdrawal, and the sharing of driver history with external Decentralized Applications (DApps). The system facilitates case registration through a dedicated traffic DApp, enabling drivers to share their records with other entities, be they companies or individuals. This feature allows individuals to conduct thorough checks on a driver's history before making decisions such as hiring or booking. The implementation employs Hyperledger Fabric as a distributed ledger to ensure the security of data, while the use of Hyperledger Cacti facilitates seamless collaboration with different systems. © 2024 IEEE.

关键词： Blockchain

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Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models

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Journal of Bionic engineering 2024年第6期21卷 3041-3075页

作者： Jinpeng Huang Zhennao Cai Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065SichuanChina Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of ***,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO *** comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been *** accuracy and stability of simulated data and real data in the parameter extraction of PV systems are *** to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent *** further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and *** summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

关键词： Partial reinforcement optimizer Learner phase Nelder-Mead simplex algorithm Parameter extraction

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An IoT-Based Energy Conservation Smart Classroom System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3785-3799页

作者： Talal H.Noor El-Sayed Atlam Abdulqader M.Almars Ayman Noor Amer S.Malki College of Computer Science and Engineering Taibah UniversityYanbu46421-7143MedinahKSA Department of Computer Science Faculty of ScienceTanta UniversityTantaEgypt College of Computer Science and Engineering Taibah UniversityMedinahKSA

With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an inﬂuential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation *** proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we ***,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)***,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't *** feasibility and beneﬁts of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering,Taibah University,Yanbu *** results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

关键词： Energy consumption energy conservation energy cost Internet of Things(IoT) smart classroom

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Enhancing Speech Recognition in Noisy Environments Through Multiscale-Multichannel Feature Learning and Adaptive Noise Resilience 3

Enhancing Speech Recognition in Noisy Environments Through M...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sengottaiyan, N. Sathishkumar, S. Rajesh Sharma, R. Sungheetha, Akey Ghantasala, G. S. Pradeep Karthika, K. Alliance University Department of Computer Science and Engineering Bangalore India Builders Engineering College Department of Computer Science and Engineering Kangeyam Tiruppur India Sri Sairam College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518592

Speech Emotion Recognition (SER) has seen much research done recently, but little is being done to minimize the effect of environmental noise on the predictions. Existing SER models primarily aim to learn the best feature representations of speech from clean datasets while neglecting the pragmatic constraint when it is deployed in adverse acoustic settings. The variations in signal-to-noise ratios, background noises and acoustic interferences make existing SER systems less robust. Here, we focus on a new method that combines adaptive feature learning and noise-resilient techniques using a multiscale-multichannel framework. The objective is to help common SER models generalize better in different acoustic conditions, thus improving the accuracy and robustness of SER in noisy real-life settings. Therefore, these SER models are still not efficient against challenging acoustic conditions and the research area lacks addressing it => thus we utilize state-of-the-art IDCNNs and also explore novel data augmentations to help develop SER systems with great capability for arbitrary acoustical channels that fills the gap of very limited related works till now. © 2024 IEEE.

关键词： Background noise

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Performance of pre-trained deep learning models for land use land cover classification using remote sensing imaging datasets

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Environmental Earth sciences 2025年第11期84卷 1-36页

作者： Haider, Irfan Khan, Muhammad Attique Masood, Saleha Algamdi, Shabbab Ali Alasiry, Areej Marzougui, Mehrez Nam, Yunyoung Department of Computer Science HITEC University Taxila Pakistan Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia IRC for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Software Engineering College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of

This study presents a comparative analysis of ten pre-trained convolutional neural network (CNN) models, evaluated across three remote sensing datasets: EuroSat, NWPU, and Earth Hazards (Land Sliding). We investigate the interplay between model architecture and classifier selection by incorporating five different neural network (NN) classifiers, emphasizing their impact on predictive accuracy and computational efficiency. Due to its densely connected architecture, DenseNet201 achieved the highest accuracy—97% on EuroSat, 99.40% on NWPU, and 97.80% on Earth Hazards. In contrast, MobileNetV2, while slightly less accurate, demonstrated superior computational efficiency, recording the shortest prediction times of 39.943 s on EuroSat, 27.482 s on NWPU, and 2.8986 s on Earth Hazards. Additionally, classifier choice significantly influenced performance, with the Wide NN classifier excelling in diverse datasets and the Medium NN classifier optimizing speed. Our findings underscore the importance of balancing accuracy and efficiency in selecting CNN models for remote sensing applications, suggesting future research should explore ensembling techniques and lightweight models to enhance performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Software design

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Environmental sound classification using convolutional recurrent neural network and data augmentation

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Multimedia Tools and Applications 2025年 1-23页

作者： Bansal, Anam Garg, Naresh Kumar Computer Science and Engineering GZS Campus College of Engineering and Technology Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda151001 India

Environmental sound classification(ESC) is the trending research area. ESC categorizes sounds such as dog barking, gunshots, and children playing in the surroundings. Due to overlapping sound signals, the presence of several audio sources while recording audio, and different distances from audio sources to the microphone make this problem complex. This study proposes a robust model for ESC, which can help in crime investigation systems, security warning systems, and the development of smart homes and hearing aids. Researchers have designed numerous frameworks for classifying surrounding events. Various techniques for ESC have been used in the past, but they are either computationally intensive or provide less accuracy. A hybrid model consisting of Convolutional Neural Network and Recurrent Neural Network for ESC is proposed to provide an accuracy of 99.89%, which is the highest till now, as far as we know. The model is a combination of both models;it is called CRNN. CRNN has already been used in a few past studies, but raw waveforms are used, and the accuracy attained is quite low. The publicly available Dataset UrbanSound8 K is used. Augmentation techniques are used to overcome the scarcity of datasets. The cepstral features are extracted and input to the CRNN. CRNN is encouraged due to its ability to capture spatial and temporal dependencies of environmental sound waves. Various hyperparameters, such as the number of LSTM layers, number of filters, batch size, momentum, and number of neurons in the LSTM layer, are altered to find the best value for hyperparameters for ESC. It is found that 0.5 momentum, 128 filters, 512 neurons in the LSTM layer, 256 batch size, and one LSTM layer give the highest accuracy. Another dataset, ESC- 10, is used to validate the model. It is found that the proposed model provides considerable accuracy for ESC- 10, even though it is lower than in the case of UrbanSound8 K. In the future, the model can be applied to different applications

关键词： Recurrent neural networks

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An Efficient Investigation of Cloud Computing Security with Machine Learning Algorithm 7

An Efficient Investigation of Cloud Computing Security with ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Senthilkumar, R. Yasotha, S. Manochithra, P.M. Senthil, J. Sivakumar, G. Shree Venkateswara Hi Tech Engineering College Computer Science And Engineering Tamilnadu Erode India Sri Eshwar College of Engineering Computer Science And Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering And Technology M.Tech Computer Science And Engineering Tamilnadu Coimbatore India Erode Sengunthar Engineering College Computer Science And Engineering Tamilnadu Erode India

ISBN: (纸本)9798350359299

Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector Machine (SVM) are used in this study for attack detection. Therefore, the implementation of SoftMax in the GRU model's last output layer, this study introduces a linear SVM. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches. © 2024 IEEE.

关键词： Support vector machines

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Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

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computer Systems science & engineering 2023年第3期44卷 2619-2636页

作者： J.Shanthini P.Punitha S.Karthik Department of Computer Science&Engineering Dr.N.G.P.Institute of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

关键词： Diplomatic mobile Ad-hoc network grouping mobility interior stable hybrid routing scheme adaptive switch structure clustering communication

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Detecting Evolving Fake News in Social Media by Leveraging Heterogeneous Deep Learning Model 2

Detecting Evolving Fake News in Social Media by Leveraging H...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Computer Science and Engineering Paavai College of Engineering Namakkal India Information Technology KSR College of Technology Thiruchengode India Computer Science and Engineering Excel Engineering College Komarapalayam India

ISBN: (纸本)9798350383867

A recent study revealed that fake articles spread considerably faster than factual content on Twitter, specifically at a 70% higher rate of retweets and reaching the first 1,500 users six times faster. This highlights the urgent need for scalable automated detection solutions to combat the pervasive threat of fake news online. While machine learning methods often rely solely on semantic text analysis, evolving fake news campaigns mimic genuine news, employing tactics like bot networks and sophisticated language imitation. This renders text-based meth- ods increasingly vulnerable. To address this challenge,we propose a heterogeneous deep learning model that incorporates richer information beyond text, specifically dissemination structure features from social networks. These features capture aspects like user interactions, sharing patterns, and network topology, providing valuable insights into the spread of information. Our model leverages the sentiment and affects the enriched Alt dataset for text analysis. Instead of using a popular language model called BERT, we explore alternative pre-trained language models like the Sentence Transformer, which focuses on capturing con- textual sentence embeddings, or the Universal Language Model (ULMFiT), which excels at understanding semantic relationships within text. Additionally, we consider spaCy, a versatile NLP library offering similar functionalities for text processing and feature extraction. While The Quint Dataset Challenge Dataset lacks sentiment annotations, it could be a potential alternative to Alt depending on research goals. This network-aware approach achieves an impressive 95.27% accuracy on The Boom Live dataset and demonstrates generalizability to The Quint dataset with 92.1% accuracy, highlighting its effectiveness in combatting evolving fake news tactics. © 2024 IEEE.

关键词： Latent semantic analysis

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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