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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

computer Systems science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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BFT Consensus Algorithms 10

BFT Consensus Algorithms

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Xiaojun Zhong, Weiyu Yang, Ce Chen, Lin Liao, Jing Xiong, Naixue QuanZhou University of Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

With the advent of Bitcoin, the blockchain as the underlying technology of Bitcoin has entered people's field of vision. In recent years, the blockchain and its related technologies have developed rapidly. The consensus algorithm is the key to the realization of blockchain technology. There will be Byzantine nodes in the process of reaching consensus among the nodes in the blockchain. Byzantine nodes are tolerated in the blockchain to achieve consensus. Consensus is an essential performance of consensus algorithms. This paper first introduces blockchain technology and the application of consensus algorithms in it;secondly, introduces the classic PBFT algorithm;then, introduces some existing consensus algorithms;finally summarizes the research status and challenges of consensus algorithms, and analyzes its Prospects. © 2023 IEEE.

关键词： Blockchain

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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

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computer Systems science & engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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ERODRL: Efficient ISP Routing Optimization via Deep Reinforcement Learning 21

ERODRL: Efficient ISP Routing Optimization via Deep Reinforc...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Changrui Chen, Xiaolin Zeng, Deze Kunming University of Science & Technology School of Information Engineering & Automation Kunming China Qiongtai Normal University School of Information Science & Technology Haikou China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798350329223

Routing optimization is a critical issue to Internet Service Providers (ISPs) since it is highly related to the network traffic management efficiency. State-of-the-art approaches utilize Reinforcement Learning (RL) technology to minimize Maximum Link Utilization (MLU). However, these approaches have two significant shortcomings: 1) the rudimentary structure of Neural Networks (NNs) restricts their ability to capture network traffic patterns accurately, resulting in sub-optimal critical flow selection, and 2) the training algorithm is ineffective in utilizing experience, leading to training instability and prolonged duration. To overcome the above shortcomings, we propose ERODRL, a network traffic management scheme which incorporates a RL based critical flow selection strategy and a flow rerouting algorithm, towards the goal of MLU minimization. In details, the RL based critical flow selection strategy incorporates a Convolutional Neural Network (CNN) block to analyze traffic features and utilizes the GELU activation function to improve traffic distribution matching. It then introduces full connection layers to the actor and critic networks respectively, allowing for detailed traffic behavior analysis. To improve training efficiency, a trust region algorithm is also involved fast the training speed of RL agent. After the critical flow are identified, the flow routing problem is formulated into Linear Programming (LP) form and a low-complexity rerouting solution is proposed to better fit the traffic patterns of ISPs. Extensive simulation results are conducted and the results demonstrate that ERODRL improves training speeds by 2.3 to 8.7 times compared to previous RL-based schemes, and reduces the MLU by 43.24% and 16.01% averagely compared to state-of-the-art solutions. © 2023 IEEE.

关键词： Reinforcement learning

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iCrop: a framework for identification of millet crop diseases via segmentation and feature extraction

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Indian Phytopathology 2023年第3期76卷 803-812页

作者： Singh, Sonal Dashore, Swastik Prasad, Jatin Naik, Nenavath Srinivas Data Science and Artificial Intelligence International Institute of Information Technology Chhattisgarh Naya Raipur 493661 India Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Kurnool Andhra Pradesh Kurnool 518008 India

Crop diseases are important in agricultural productivity and quality. It is essential to recognise and diagnose these diseases. Deep learning’s ability to correctly classify small data sets is a major research challenge. This work proposes an approach using image segmentation, feature extraction and deep learning techniques to develop a multi-class classifier for millet crop disease identification. The proposed image processing approach and use of Artificial Neural Networks demonstrate disease classification over 2632 images with a training accuracy of 97.25% and a testing accuracy of 96.88%. Compared with the pre-trained model MobileNet V3, the implemented model achieved better training and validation accuracy over 100 epochs and required less training time. The proposed model’s simplicity makes crop disease detection in agriculture fast and efficient. As a result, the suggested method paves the way for large-scale crop disease diagnosis. © 2023, Indian Phytopathological Society.

关键词： Artificial neural networks Deep learning Feature extraction Image segmentation Millet crop disease

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KTI-RNN:Recognition of Heart Failure from Clinical Notes

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Tsinghua science and technology 2023年第1期28卷 117-130页

作者： Dengao Li Huiting Ma Wenjing Li Baofeng Zhao Jumin Zhao Yi Liu Jian Fu College of Data Science Taiyuan University of TechnologyJinzhong 030600China Department of Statistics and Applied Probability University of CaliforniaSanta BarbaraCA 93106USA College of Mining Engineering Taiyuan University of TechnologyTaiyuan 030024China College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China

Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult *** particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment *** study uses data from the Medical information Mart for Intensive Care *** with structured data,unstructured data contain abundant patient ***,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse *** solve these problems,we propose the KTI-RNN model for unstructured data *** proposed model overcomes sparse content and obtains good classification *** term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword *** latent dirichlet allocation(LDA)model is adopted to extract the topic word *** models enable the expansion of the medical record text ***,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output *** call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical *** show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.

关键词： heart failure diagnosis text classification deep learning

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CHART:Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning

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computers, Materials & Continua 2024年第12期81卷 4171-4194页

作者： Rashid Ahmad Asif Nawaz Ghulam Mustafa Tariq Ali Mehdi Tlija Mohammed AEl-Meligy Zohair Ahmed University Institute of Information Technology PMAS Arid Agriculture UniversityRawalpindi46000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Jadara University Research Center Jadara UniversityIrbid21110Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Science and Engineering Central South UniversityChangsha410083China

Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public *** methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the *** some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal *** research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these *** proposed methodology begins with comprehensive data collection from the police *** dataset includes detailed attributes such as crime type,location,time and demographic *** key steps in the proposed framework include:Data Preprocessing,Feature engineering that leveraging domain-specific knowledge to extract and transform relevant *** Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various *** Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),*** designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime *** of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhanc

关键词： Crime hotspot heat map kernel density estimation(KDE) support vector machine(SVM)

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches 4

Early Detection of Childhood Malnutrition Using Survey Data ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Zumma, Thoufiq Rahaman, Anikur Islam Prova, Nuzhat Noor Haque, Tasriful Joy Bose, Julias Caeser Youki, Ravina Akter Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Washington University Science and Technology Department of Computer Science New York United States Pace University Department of Seidenberg School of Csis New York United States Victorian Institute of Technology Master of Information Technology and Systems Australia University of Wollongong Faculty of Artificial Intelligence and Science Australia University of Technology Sydney Faculty of Engineering and It Australia

ISBN: (纸本)9798331523923

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%. © 2025 IEEE.

关键词： Adversarial machine learning

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Convolutional neural network based children recognition system using contactless fingerprints

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International Journal of information technology (Singapore) 2023年第5期15卷 2695-2705页

作者： Rajaram, Kanchana Amma, N. G. Bhuvaneswari Selvakumar, S. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai 603 110 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600 127 India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli 620 015 India Indian Institute of Information Technology Himachal Pradesh Una 177 209 India

Biometric features are useful for unique identification, authentication, and security applications. Among all biometric features, fingerprints are the most commonly used because they contain ridges and valleys. There are challenges in recognizing child or infant fingerprints since the ridges are not mature as the hands are covered with a white substance and acquisition of fingerprint images is difficult. In the context of COVID-19 pandemic, contactless fingerprint acquisition gains importance as it is not infectious especially with children. In this study, a Convolutional Neural Network (CNN) based children recognition system named Child-CLEF, that uses Contact-Less Children Fingerprint (CLCF) dataset acquired using a mobile phone-based scanner is proposed. The quality of captured fingerprint images is enhanced using a hybrid image enhancement method. Furthermore, the minutiae features are extracted using the proposed Child-CLEF Net model and the identification of children is made using a matching algorithm. The proposed system is tested with a self-captured children fingerprint dataset, CLCF and publicly available PolyU fingerprint dataset. It is found that the proposed system outperforms the existing fingerprint recognition systems in terms of accuracy and equal error rate. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Biometric security Child fingerprints Contactless fingerprints Convolutional neural network Fingerprint identification Image acquisition

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Optimal Deployment of Heterogeneous Visual Sensor Networks for Target Coverage and Feature Aggregation with Lifetime Guarantee 21

Optimal Deployment of Heterogeneous Visual Sensor Networks f...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Zhu, Xiaojian Zhou, MengChu School of Computer Science and Engineering Changshu Institute of Technology Changshu215500 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

Due to their need to handle a huge size of image data and limited battery power at each node, visual sensor networks (VSNs) suffer from short network lifetime and long network delay. Consequently, how to reduce network traffic is very important in their operation. The deployment of processing nodes for feature extraction is an effective way to reduce it. Deploying multiple types of camera nodes should help do so as well. Therefore, in this work we investigate how to deploy a heterogeneous VSN for target coverage and feature aggregation with lifetime guarantee at the minimum cost. Since this problem is NP-hard, we propose two heuristics, i.e., separate and joint node deployment algorithms, to solve it. A large number of experimental results expose that the latter has higher success rate and can obtain lower network deployment cost than the former at the expense of extra computation. © 2024 IEEE.

关键词： Sensor nodes

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