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Sentimental Analysis of COVID-19 Twitter Data Using Machine Learning 8th

Sentimental Analysis of COVID-19 Twitter Data Using Machine ...

8th International Conference on Emerging Research in Computing, information, Communication and Applications, ERCICA 2023

作者： Likhith, S.R. Pooja Ahuja, S. Prathibha, B.N. Uma Shankari, B. Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India

ISBN: (纸本)9789819976218

Corona virus is considered as a scourge all over world by which most of the people got infected. Since the virus was spreading very quickly governments had to bring in very tough rules such as lock down. Lock down disrupted almost all sector like education, business etc.… During lock down and after the lock down people have used social media as their platform to express the opining regarding the pandemic. Tweeter is one such social media where people had shared their thoughts on the pandemic. In this study tweets which are made by the public is considered to analyses the sentiments. And in this research machine learning models are used to classify the data into five different classes namely positive, negative, neutral, extremely positive, and extremely negative. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes are the ML models used to classify the sentiment and are achieving on accuracy of 99%, 99%, 97%, 94%, and 73%, respectively. Further precision, recall, F1-score, and world clod are calculated for each and every class. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 Decision tree Logistic regression ML Naïve Bayes Random forest Sentiment analysis SVM Tweets

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Semantic association rules for data interestingness using domain ontology

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International Journal of Metadata, Semantics and Ontologies 2022年第1期16卷 47-67页

作者： Abhilash, C.B. Mahesh, Kavi Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka Dharwad India Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka Dharwad India

The COVID-19 pandemic is a major public health crisis threatening people’s health, well-being, freedom to travel and the global economy. Understanding COVID-19 symptoms for determining the severity of cases is critical. This study aimed to discover interesting facts from the COVID-19 data set considering symptoms, medicines and comorbidity. For data mining research, the semantic web raises new possibilities. Resource Description Framework (RDF) triple format is commonly used to express semantic web data. Association Rule Mining (ARM) is one of the most effective methods of detecting frequent patterns. However, finding potential rules is a difficult task. We propose an improved method that uses ontology with ARM for finding semantic-rich rules from COVID-19 data sets. The outcomes are semantic association rules that are potentially beneficial for decision-makers. We compare our results with one of the most recent approaches in this field to demonstrate the importance of ontology-based methods. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： COVID-19

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Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

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Intelligent Automation & Soft Computing 2023年第5期36卷 1767-1782页

作者： M.P.Thiruvenkatasuresh Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia Corresponding

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient *** a novel ML algorithm speciﬁc to Diabetic Retinopathy(DR)is a chal-lenge and need of the *** images include several challenges,including relevant feature selection,class variations,and robust classiﬁ*** the cur-rent research in DR has yielded favourable results,several research issues need to be *** is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-ﬁ*** is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classiﬁer(SKPD-PSC)*** novelty of this method relies on the appropriate non-linear classiﬁcation of exclusive image processing models in har-mony with the Platt Scale Classiﬁer(PSC)to improve the accuracy of DR ***,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background ***,a Platt Scale Classiﬁer(PSC)is applied to the localized features for robust classiﬁ*** the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classiﬁ*** results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to

关键词： Diabetic retinopathy retinal images machine learning image localization Platt Scale classiﬁer accuracy

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Robust Human Interaction Recognition Using Extended Kalman Filter

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Computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Visual Analytics Technologies for Situation Awareness 8

Visual Analytics Technologies for Situation Awareness

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8th International Conference on Big Data and information Analytics, BigDIA 2022

作者： Meng, Xiangxia Rao, Jiaren Fang, Yujie Li, Tingting Diao, Lianwang Xie, Ke Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

Visual analytics technologies are very important for mining information and knowledge from big data. Its goal is to generate images that people can obtain information intuitively. A good visual analytics method is an important way to promote human-machine interaction, and it is also an important technology in the current era of big data and artificial intelligence. Situation awareness is a typical application of mining information from data. By reviewing the role of visual analytics on human cognition process, it is proposed that visual analytics also plays an important role in situation awareness. Combining the visual analytics technologies and situation awareness process, three models are studied in this paper. Starting from the information transformation model of situational awareness, how the situation information are transformed is illustrated. Then the situation awareness model with visual analytics is proposed, in which the process flow with analytics models and visualization technologies is illustrated to enhance human's situation awareness. Finally, the processing flow from the perspective of data state is proposed, which provides a basis for the development of better human-machine interaction situation awareness methods. © 2022 IEEE.

关键词： Big data

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An Attention-based LSTM price prediction model based on a single time series information 6

An Attention-based LSTM price prediction model based on a si...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Tian, Jiaming Dang, Yuzhuo Huang, Hongbin National University of Defense Technology Laboratory for Big Data and Decision Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9798331516550

With the continuous improvement of people's living standards, people's income from work has gradually increased, and their remaining savings have also gradually increased. In order to avoid the problem of currency devaluation and shrinking savings caused by inflation, investment is regarded as an indispensable asset allocation method. Among investment products, gold has always been favored for its store of value;in the past decade, the cryptocurrency Bitcoin has been limited in number due to its unique technology, causing a new wave of investment in Bitcoin. At present, most of the existing forecasting models are for forecasting stock prices, and there are multiple forecasting models. We used grayscale prediction, time series analysis method and LSTM prediction model based on Attention mechanism to predict and analyze the results of two investment products. The experiment found that the LSTM prediction model based on Attention mechanism had the best prediction performance of the two investment products. © 2024 IEEE.

关键词： Prediction models

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Through-polymer,via technology-enabled,flexible,lightweight,and integrated devices for implantable neural probes

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Microsystems & Nanoengineering 2024年第2期10卷 279-291页

作者： Cunkai Zhou Ye Tian Gen Li Yifei Ye Lusha Gao Jiazhi Li Ziwei Liu Haoyang Su Yunxiao Lu Meng Li Zhitao Zhou Xiaoling Wei Lunming Qin Tiger H.Tao Liuyang Sun College of Electronics and Information Engineering Shanghai University of Electric PowerShanghaiChina 2020 X-Lab Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina School of Graduate Study University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina School of Physical Science and Technology ShanghaiTech UniversityShanghaiChina Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Neuroxess Co. Ltd.(Jiangxi)NanchangJiangxiChina Guangdong Institute of Intelligence Science and Technology HengqinZhuhaiGuangdongChina Tianqiao and Chrissy Chen Institute for Translational Research ShanghaiChina

In implantable electrophysiological recording systems,the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal *** advancements in MEMS and CMOS technology have significantly improved these components,their interconnection still relies on conventional printed circuit boards and sophisticated *** conventional approach adds considerable weight and volume to the package,especially for high channel count *** address this issue,we developed a through-polymer via(TPV)method inspired by the through-silicon via(TSV)technique in advanced three-dimensional *** innovation enables the vertical integration of flexible probes,amplifier chips,and PCBs,realizing a flexible,lightweight,and integrated device(FLID).The total weight of the FLIDis only 25%that of its conventional counterparts relying on adapters,which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without ***,by incorporating a platinum-iridium alloy as the top layer material for electrical contact,the FLID realizes exceptional electrical performance,enabling in vivo measurements of both local field potentials and individual neuron action *** findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.

关键词： alloy neural recording

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study 2

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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2nd IEEE International Conference on Data, Decision and systems, ICDDS 2023

作者： Hegadi, Rajendra Akella, Syam Sankar Karthik Reddy, Koushik Kumar Pavan, C. Indian Institute of Information Technology Dharwad Department of Data Science and Intelligent Systems Karnataka India Indian Institute of Information Technology Dharwad Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350314168

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology. © 2023 IEEE.

关键词： Smart contract

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VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems

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Digital Communications and Networks 2023年第4期9卷 981-989页

作者： Jiale Zhang Yue Liu Di Wu Shuai Lou Bing Chen Shui Yu School of Information Engineering Yangzhou UniversityYangzhou225009China Water Conservancy and Civil Engineering College Inner Mongolia Agricultural UniversityHohhot010018China Deakin Blockchain Innovation Lab School of Information TechnologyDeakin UniversityMelbourneVIC3125Australia College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China School of Computer Science University of Technology SydneySydney2007Australia

Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central ***,the frequently transmitted local gradients could also leak the participants’private *** protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been ***,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification *** tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission ***,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex ***,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge *** security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and *** last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

关键词： Federated learning Edge computing Privacy-preserving Verifiable aggregation Homomorphic cryptosystem

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