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Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

computer Systems science & engineering 2023年第1期44卷 629-645页

作者： P.Renukadevi A.Rajiv Kannan Department of Computer Science and Engineering Paavai Engineering CollegeNamakkal637018Tamil NaduIndia Department of Computer Science and Engineering K.S.R.College of EngineeringTiruchengodeNamakkal637215Tamil NaduIndia

About 170 nations have been affected by the COvid VIrus Disease-19(COVID-19)*** governing bodies across the globe,a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive,and they feel challenging to tackle this *** researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these *** the previous works,Long Short-Term Memory(LSTM)was used to predict future COVID-19 *** to LSTM network data,the outbreak is expected tofinish by June ***,there is a chance of an over-fitting problem in LSTM and true positive;it may not produce the required *** COVID-19 dataset has lower accuracy and a higher error rate in the existing *** proposed method has been introduced to overcome the above-mentioned *** COVID-19 prediction,a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network(LDIWCSO-HBDCNN)approach is *** this suggested research study,the COVID-19 predicting dataset is employed as an input,and the min-max normalization approach is employed to normalize *** features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization(LDIWCSO)algorithm,enhancing the accuracy of *** Cat Swarm Optimization(CSO)algorithm’s convergence is enhanced using inertia weight in the LDIWCSO *** is used to select the essential features using the bestfitness function *** a specified time across India,death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network(HBDCNN)technique based on selected *** demonstrated by empirical observations,the proposed system produces significant performance in terms of f-measure,recall,precision,and accuracy.

关键词： Binomial distribution min-max normalization Cat Swarm Optimization(CSO) COVID-19 forecasting

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Applying Deep Neural Networks and NLP Techniques for Sentiment Analysis in Social Media Data 2

Applying Deep Neural Networks and NLP Techniques for Sentime...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Nathiya, N. Raguraman, P.J. Srinivasan, K. Paavai College of Engineering Computer Science and Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Excel Engineering College Computer Science and Engineering Namakkal India Paavai College of Engineering Artificial Intelligence and Data Science Namakkal India

ISBN: (纸本)9798350349221

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks - like transformers or recurrent neural networks - with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation. © 2024 IEEE.

关键词： Sentiment analysis

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Practical cloud storage auditing using serverless computing

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science China(Information sciences) 2024年第3期67卷 33-47页

作者： Fei CHEN Jianquan CAI Tao XIANG Xiaofeng LIAO College of Computer Science and Software Engineering Shenzhen University College of Computer Science Chongqing University

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

关键词： cloud storage auditing serverless computing object storage usability

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An Integrated Blockchain Framework for Secure Data Sharing in IoT Fog Computing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 957-977页

作者： Peda Narayana Bathula M.Sreenivasulu Department of Computer Science Engineering Jawaharlal Nehru Technological UniversityAnanthapur 515002India Department of Computer Science Engineering Matrusri Engineering CollegeHyderabad 500059India

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

关键词： BlocFogSec Internet of Things(IoT) fog computing blockchain

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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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A Privacy-Preserving Graph Neural Network Framework with Attention Mechanism for Computational Offloading in the Internet of Vehicles

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computer Modeling in engineering & sciences 2025年第4期143卷 225-254页

作者： Aishwarya Rajasekar Vetriselvi Vetrian Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India

The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ***,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of *** recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a *** this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is *** Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as ***,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)***,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading ***,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.

关键词： Internet of vehicles vehicular ad-hoc networks(VANET) multiaccess edge computing task offloading graph neural networks differential privacy

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Prediction and analysis of Covid-19 using the Deep Learning Models

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Research on Biomedical engineering 2024年第1期40卷 183-197页

作者： Indira, V. Geetha, R. Umarani, Srikanth Priyadarshini, D. Annal Department of Computer Science and Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

Purpose: The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans. Methods: Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers

关键词： COVID-19

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Task scheduling in multi-cloud environment via improved optimisation theory

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 64-77页

作者： Jawade, Prashant Balkrishna Ramachandram, S. Computer Science and Engineering Government College of Engineering Maharashtra Nagpur India Computer Science and Engineering University College of Engineering Osmania University Telangana Hyderabad India

As one of the most popular technologies nowadays, cloud computing has a big demand in the distributed software space. It is highly difficult for CSPs to work together in a multi-cloud context, and contemporary literature does not adequately address this issue. In this work, a protected TS paradigm in a multi-cloud environment is introduced. The suggested scheme mainly focuses on the optimal scheduling of tasks by considering a modified Deep Neural Network (DNN) as a task scheduler. Accordingly, the task is allotted based upon ‘makespan, execution time, security constraints (risk assessment), utilisation cost, maximal Service Level Agreement (SLA) adherence and Power Usage Effectiveness (PUE)’. Moreover, the weights of DNN are tuned optimally by Self-Improved Aquila Optimisation (SI-AO) technique. The developed model has obtained a lower MAE value = 0.052581 which is 46.67%, 90.85%, 89.29% and 86.43% better than DNN, NN, RNN and LSTM, respectively. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Risk assessment

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Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme

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Digital Communications and Networks 2025年第1期11卷 60-70页

作者： Zhuo Chen Bowen Zhu Chuan Zhou College of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China

Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.

关键词： Edge computing Network virtualization Container cluster Deep reinforcement learning Graph convolutional network

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