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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Tuhin, Hemayet Hossain Kayum, Md Abdul Rahmatul Kabir Rasel Sarker, Md. Tanvir Siddiquee, Shah Md Pandey, Bishwajeet Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Intelligent System and Cyber Security Astana IT University Kazakhstan

ISBN: (纸本)9798350374865

Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have_ been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry. © 2024 IEEE.

关键词： coconut leaf disease Inception v3 MobileNet v2 nutrient deficiency pest attacks ResNet50 VGG19

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Crime Against Women in India: Statistical Analysis Using Linear Regression 2

Crime Against Women in India: Statistical Analysis Using Lin...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mim, Zarin Tasnim Paul, Rittika Foysal, Mollah Md Tohfa, Fairuz Nowshin Mahi, Shah Abrar Bangalore India Bangalore India B-Tech of Computer Science and Engineering East Delta University Chattagram Bangladesh B-Tech of Computer Science and Engineering-Cyber Security Chandigarh University Delhi India

ISBN: (纸本)9798331519056

The crime reports of offenses occurring against women all over India are increasing day by day. It has become one of today's most concerning socio-economic and cultural issues. Crime against women hampers the overall prosperity and development of the nation. This crime violates human rights, impacts women's physical and mental health, and becomes an educational barrier to women's empowerment. This paper surveys the reasons for the influence of reported Crime Against Women (CAW) in 35 states and union territories in India from 2001-2012. The dataset describes different types of Crime Against Women (CAW): rape, kidnapping, abduction, assault on women, insult to the modesty of women, cruelty by husband or relative. This paper also contains a report on the arrested rate for crimes against women in India from 2001-2012. According to the comparison between these two datasets, the person arrest rate is more than the crime against women rate in every state in India. We used machine learning-based linear regression analysis to format the relationship between two datasets, where crime against women is the dependent variable and people arrested rate is the independent variable. This paper discusses two types of linear regression: Simple Linear Regression and Multiple Linear Regression. The relation between these two datasets helps to predict the hot location of crimes, and the activeness of law enforcement regarding the crimes and encourages victims to take action. For the datasets, P-values, R-squared, and adjusted R-squared values are used to evaluate the statistically significant prediction in explaining the variation in crime rates. This prediction is used to reduce the rate of crime against women all over India. © 2024 IEEE.

关键词： Multiple linear regression

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Advancing Stance Detection of Political Fan Pages: A Multimodal Approach 24

Advancing Stance Detection of Political Fan Pages: A Multimo...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Kuo, Kuan-Hung Wang, Ming-Hung Kao, Hung-Yu Dai, Yu-Chen National Chung Cheng University Department of Computer Science and Information Engineering Chiayi Taiwan National Institute of Cyber Security Taipei Taiwan

ISBN: (纸本)9798400701726

The evolution of political campaigns is evident with the ascent of social media. Ideological beliefs are increasingly disseminated through political-affiliated fan pages. The interaction between politicians and the general public on these platforms plays a pivotal role in election outcomes. In this study, we utilize a multimodal approach to explore and quantify similarities of ideologies among political fan pages. we employed visualization techniques to demonstrate the political stance of each fan page. To validate our proposal, we concentrated on an analysis of the 2021 national referendums in Taiwan, encompassing a collection of fan pages and their corresponding posts that were related to these referendums. Through a qualitative analysis of the content of these fan pages, the efficacy of our multimodal framework in clustering fan pages according to their respective political ideologies was evaluated. The findings of this study underscore the significant enhancement in the accuracy of stance detection when integrating multiple modalities of data, namely textual content, visual imagery, and user interactions. © 2024 Copyright held by the owner/author(s).

关键词： Image enhancement

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A MULTIMODAL FRAMEWORK FOR SATIRE VS. SARCASM DETECTION 15

A MULTIMODAL FRAMEWORK FOR SATIRE VS. SARCASM DETECTION

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Goyal, Devansh Mishra, Santosh Kumar Rai, Vikash Kumar Department of Computer Science and Engineering RGIPT Jais Amethi India School of Cyber Security and Digital Forensics NFSU Goa Goa India

ISBN: (纸本)9798350370249

Sarcasm and satire are common ways to communicate complicated ideas with humor and irony, both expressing criticism or mockery. Detecting sarcasm vs. satire in real life can foster better communication by understanding intent, preventing misunderstandings, and promoting critical thinking and empathy. We introduce an innovative dataset for detecting Multi-modal Satire vs. Sarcasm, leveraging the MUStARD dataset for its construction. This dataset includes visual utterances annotated with sarcasm and satire labels, enriching its potential for studying multimodal satire and sarcasm detection. This enriched dataset serves as a robust foundation for training and evaluating machine learning and deep learning architectures that combine textual and visual cues to identify satire and sarcasm in multimedia content. By incorporating this enhanced dataset, the research aims to advance the field of multimodal satire and sarcasm detection and contribute to a deeper understanding of humor and irony in online communication. We carry out experimentation with various architectures like LSTM, GPT2, BERT, SVM, and Logistic Regression. The proposed architecture achieves an F1 score of 0.956, outperforming other baselines. © 2024 IEEE.

关键词： Contrastive Learning

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Adaptive System-Level Fault Diagnosis of Bijective Connection Networks

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IEEE Transactions on Reliability 2025年第2期74卷 2916-2926页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan School of Mathematics and Statistics Fujian Normal University College of Computer and Cyber Security Fuzhou 350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan 701 Taiwan

As the multiprocessor systems are becoming largescale, fault-diagnosis is crucial to ensure the reliability of multiprocessor systems. In order to improve the self-diagnosis capability of a multiprocessor system, a pessimistic fault diagnosis scheme such as t/s-diagnosis allows some fault-free processors to be mistakenly identified as faulty. All faulty processors in a t/s-diagnosable multiprocessor system (t ≤ s) should be identified into a set with size up to s, when the total amount of faulty processors in the system does not exceed t. This article focuses on the t/s-diagnosis for the n-dimensional bijective connection network X_n. An adaptive t/s-diagnosis algorithm APDMM*t/s of complexity O(M(log₂ M)²) under the comparison model is proposed, where M is the total amount of nodes in X_n. Then, the correctness of algorithm APDMM*t/s is proved by the fault-tolerant properties of the network itself. Moreover, we calculate the t/s-diagnosability of X_n by theoretical method in mathematics, which is (Formula presented) for 2 ≤ y ≤ n under comparison model, where (Formula presented). Furthermore, we apply algorithm APDMM*t/s on the hypercube and the real world network WSN-DS to verify our main results, and analyze the experimental outcomes in terms of true positive rate, false positive rate, accuracy and precision. The experimental results reveal the advantage and high performance of our algorithm APDMM*t/s. Besides, we compare the t/s-diagnosability of X_n with traditional accurate diagnosability, and it turns out that as n gets larger, the t/s-diagnosability of X_n is significantly better than traditional accurate diagnosability. © 1963-2012 IEEE.

关键词： adaptive fault diagnosis bijective connection network reliability t/s-diagnosability

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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PriFace: a privacy-preserving face recognition framework under untrusted server

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2967-2979页

作者： Zhao, Shan Zhang, Lefeng Xiong, Ping School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China Center for Cyber Security and Privacy School of Computer Science University of Technology Sydney Sydney Australia

Alongside the rapid development of artificial intelligence and mobile Internet, face recognition has gradually become a mainstream intelligent terminal authentication technology that is widely applied in security, finance, and social networking, among other fields. However, face images usually contain sensitive personal information, which can have serious consequences for users if this information is leaked or misused. Most existing face privacy protection methods are based on data encryption techniques or differential privacy mechanisms, which make it difficult to strike a good balance between privacy and usability. In this paper, we propose PriFace, a new privacy-preserving method for face recognition based on eigenfaces and locality-sensitive hashing (LSH). PriFace aims to achieve accurate and efficent face recognition with privacy preserved. It applies eigenface technique to reduce the dimension of face images for improving retrieval performance. With the assumption that the server is untrustworthy, PriFace uses the locality-sensitive hashing technique to introduce randomness into the face information for privacy preservation so that the server, or other adversaries, can not reconstruct any user’s face image. Experiments on real-world datasets demonstrate that the proposed PriFace can effectively preserve the privacy of face images and further outperforms the traditional methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Face recognition

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IDCE:Integrated Data Compression and Encryption for Enhanced security and Efficiency

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computer Modeling in engineering & sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Novel method for ranking batsmen in Indian Premier League

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Data science and Management 2023年第3期6卷 158-173页

作者： M.K.Manju Abin Oommen Philip Department of Computer Science&Engineering Toch Institute of Science&TechnologyErnakulamKerala682313India Department of CSE-Cyber Security Indian Institute of Information TechnologyKottayam686635India

Sports analytics have benefited immensely from the growth and popularity of artificial intelligence and machine *** techniques enable sports analysts to evaluate player performance more effectively.A literature review of player performance evaluation methods shows the need to develop a new performance evaluation index for Twenty20(T20)cricket.A novel framework was proposed to evaluate batsman strength based on individual performance,role in the team,and team ***,proposed ranking systems are derived from static networks,that is,the aggregation of game results over ***,the scores of the players(or teams)fluctuate over ***,defeating a renowned player during peak performance is more rewarding than defeating the same player during other *** account for this,we propose a new method and apply it to the T20 format Indian Premier *** method serves three main purposes:First,it creates a new performance index for players to rank them more accurately and ***,the players are clustered based on their *** the third phase,a social network analysis approach is applied to visualize and analyze crickets as a network to gain better insights into players’team *** novel approach is a helpful index for sports coaches,analysts,cricket fans,and managers to evaluate player performance and rank for future aspects.

关键词： Indian Premier League Social network analysis Artificial intelligence Machine learning

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Efficient Publication of Distributed and Overlapping Graph Data Under Differential Privacy

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Tsinghua science and Technology 2022年第2期27卷 235-243页

作者： Xu Zheng Lizong Zhang Kaiyang Li Xi Zeng the School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China China Electronics Technology Cyber Security Co.Ltd. Chengdu 610000China

Graph data publication has been considered as an important step for data analysis and *** data,which provide knowledge on interactions among entities,can be locally generated and held by distributed data *** data are usually sensitive and private,because they may be related to owners’personal activities and can be hijacked by adversaries to conduct inference *** solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed ***,this work proposes a novel framework for distributed graph *** this framework,differential privacy is applied to justify the safety of the published *** includes four phases,i.e.,graph combination,plan construction sharing,data perturbation,and graph *** published graph selection is guided by one data coordinator,and each graph is perturbed carefully with the Laplace *** problem of graph selection is formulated and proven to be ***,a heuristic algorithm is proposed for *** correctness of the combined graph and the differential privacy on all edges are *** study also discusses a scenario without a data coordinator and proposes some insights into graph publication.

关键词： graph data distributed data publication differential privacy

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