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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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PointBi-FPN: An Extention to Pointpillars for LiDAR 3D Object Detection in Autonomous Vehicles Using Bi-Directional Feature Pyramid Network

PointBi-FPN: An Extention to Pointpillars for LiDAR 3D Objec...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Soumya, A. Krishna Mohan, C. Cenkeramaddi, Linga Reddy Babu, Sobhan Indian Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Agder Department of Information and Communication Technology Grimstad Norway

ISBN: (纸本)9798350350821

Autonomous vehicles increasingly rely on accurate three-dimensional (3D) object detection for safe navigation. While two-dimensional (2D) methods offer computational efficiency, the shift to 3D detection enhances precision in understanding environments. Point-based and voxel-based approaches are accurate but computationally intensive for onboard deployment. Pillar-based methods like PointPillars provide efficiency but may lack detection accuracy compared to voxel-based approaches. In this paper, we focus on enhancing the performance of PointPillars, a popular pillar-based detector, using LiDAR data. The proposed approach (PointBi-FPN) employs a bi-directional feature pyramid network (Bi-FPN) as a backbone, that aggregates multiscale features in the input data, providing a holistic view of the environment, which is crucial for detecting objects of varying sizes and distances accurately. Bi-FPN improves the model's understanding of complex scenes, making it more robust to occlusions. Through extensive experimentation on the KITTI dataset, the PointBi-FPN approach demonstrates better performance across all three detection benchmarks: BEV (Bird's Eye View), 3D (Three-Dimensional), and AOS (Average Orientation Similarity). Notably, the proposed approach exhibits significant improvements, particularly in accurately detecting objects labeled as 'hard' difficulty. © 2024 IEEE.

关键词： Object detection

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An Innovative Method to Detect Cyberbullying on Social Media 3

An Innovative Method to Detect Cyberbullying on Social Media

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

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YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy

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Recent Advances in computer Science and Communications 2023年第6期16卷 44-55页

作者： Kotikam, Gnanendra Selvaraj, Lokesh Department of Information and Communication Engineering Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India

Objective: Big Data processing is a demanding task, and several big data processing frameworks have emerged in recent decades. The performance of these frameworks is greatly dependent on resource management models. Methods: YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. Results: In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Conclusion: Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues. © 2023 Bentham Science Publishers.

关键词： Energy utilization

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A Hybrid Approach for Underwater Image Enhancement 8

A Hybrid Approach for Underwater Image Enhancement

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Abin, Deepa Surdas, Spandan Suryawanshi, Atharva Thakare, Khushi Saravane, Yash Vishwakarma Institute of Technology Department of Ai & Ds Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350376425

Underwater imaging presents significant challenges primarily due to adverse environmental conditions, including insufficient light, hazy visibility, and light scattering, which result in uneven colors. Research in this field focuses on enhancing specific attributes of images, such as blurriness reduction, color restoration, and contrast enhancement. However, the combination of these algorithms does not always yield visually pleasing images or the desired outcomes. This paper presents experiments on various combinations of algorithms to determine the fusion method that produces consistent and intended results. Techniques explored include dehazing with boundary constraint and contextual regularization, integrated color models, gray world adjustment, and histogram equalization, along with their combinations, to identify the fusion method that demonstrates the greatest improvement. The results indicate that, despite variations in the quality of input images, the proposed fusion procedure consistently delivers superior outcomes. © 2024 IEEE.

关键词： Underwater imaging

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Rainfall Prediction using XGB Model with the Australian Dataset

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-4页

作者： Vinta, Surendra Reddy Peeriga, Radhika VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science & Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Dundigal India

Rainfall prediction is a critical field of study with several practical uses, including agriculture, water management, and disaster preparedness. In this work, we examine the performance of several machine learning models in forecasting rainfall using a dataset of Australian rainfall observations from Kaggle. Six models are compared: random forest (RF), logistic regression (LogReg), Gaussian Naive Bayes (GNB), k-nearest neighbours (kNN), support vector classifier (SVC), and XGBoost (XGB). Missing value imputation and feature selection were used to preprocess the dataset. To analyse the models, we employed cross-validation and performance indicators such as accuracy, precision, recall, and F1-score. According to our findings, the RF and XGB models fared the best, with accuracy ratings of 87% and 85%, respectively. With accuracy ratings below 70%, the GNB and SVC models performed the poorest. Our findings imply that machine learning algorithms can be useful tools for predicting rainfall, but careful model selection and evaluation are required for reliable results. Copyright © 2024 S. R. Vinta et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Nearest neighbor search

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Revolutionizing Education through Holistic Application of technology: A Blockchain-Powered Framework for Smart Learning

Revolutionizing Education through Holistic Application of Te...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Verma, Rohan Singh, Kiran Deep Singh, Prabh Deep Maurya, Sudhanshu Nerkar, Sanskruti Thapliyal, Nitin Department of Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

The convergence of blockchain technology and education has ushered in a new era of innovation and transformation in the field of learning. This paper presents a comprehensive framework for integrating blockchain into the educational landscape, offering a blueprint for developing a more secure, transparent, and efficient smart education ecosystem. The framework encompasses key aspects such as identity management, content sharing, student records, micro-credentials, financial transactions, governance, data privacy, interoperability, user interfaces, and scalability. By exploring the potential benefits and challenges of blockchain adoption in education, this research paper seeks to inspire educators, institutions, and policymakers to embark on a journey toward a blockchain-enabled smart education future. As the world witnesses the growing importance of lifelong learning and digital credentials, this framework paves the way for a more accessible, equitable, and globally recognized education system poised to meet the evolving needs of learners in the 21st century. © 2024 IEEE.

关键词： Federated learning

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第11期77卷 1445-1465页

作者： Arif Hussain Magsi Ali Ghulam Saifullah Memon Khalid Javeed Musaed Alhussein Imad Rida State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Information Technology Center Sindh Agriculture UniversityTandojam70050Pakistan Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirate Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting ***,existing NDN faces three significant challenges,including security,privacy,and *** particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious *** instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road *** such a situation,trust in the content-providing vehicles brings a new *** the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another ***,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in *** this connection,our contribution is *** existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate *** on ML evaluation results,vehicles accept or discard served ***,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ***,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)*** implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive *** qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

关键词： Named data networking vehicular networks reputation caching machine-learning

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Proposed Model for Detection of Pneumonia Using Deep Learning 6th

Proposed Model for Detection of Pneumonia Using Deep Learnin...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Rai, Bipin Kumar Srivastava, Anoop Kumar Sharma, Shivani Kamboj, Shashank Department of Information Technology ABES Institute of Technology Ghaziabad India Department of Computer Science and Engineering ACED Alliance University Bengaluru India

ISBN: (纸本)9789819971367

Accurate detection of Pneumonia is highly challenging. Pneumonia is first diagnosed by a doctor through the x-ray, but it can be time taking and can have a lot of investments. We used a Deep Learning algorithm to solve this problem. This paper presents a proposed model called Totally Automated Pneumonia Discovery (TAPD) Model for detection of Pneumonia using Deep Learning. We developed an algorithm which utilizes Convolution Neural Network (CNN) and with the help of some other layers we made a custom Deep Learning Model. Deep Learning algorithms are widely used in analyzing medical images and CNN has become very useful for disease classification. We have used a dataset containing images and created an algorithm which can detect whether a person is suffering from Pneumonia or not? Unlike other methods, our model handles the problem of overfitting with ease and also eliminates the problem of vanishing and exploring gradients. Our model is less complex and has less complexity. Our model is developed on the Flask framework which is totally based on Python and also our model has provided a user interface to test. Accuracy of the model is great as compared to the other models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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