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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Secure Communication Protocols for IoT-Enabled Smart Grids

Secure Communication Protocols for IoT-Enabled Smart Grids

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Soni, Vandana Naveen Kumar, C. Jain, Virendra Santhoshkumar, G. Praveen Yuvasri, B. Rajendiran, M. Shah & Anchor Kutchhi Engineering College Department of Computer Engineering Maharashtra Mumbai400088 India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru560098 India Mandsaur University Department Electrical & Electronics Engineering Madhya Pradesh 458002 India Department Electronics and Communication Engineering Tamil Nadu Erode638052 India R.M.K College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Thiruvallur601206 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798331543617

The integration of the Internet of Things (IoT) into smart grids has changed energy management and distribution, and among its benefits are enhanced operational efficiency and real-time monitoring. However, there are significant security dangers associated with this growing interconnectedness, especially about the communication protocols. The goal of this project is to safeguard Internet of Things (IoT)-enabled smart grids from data breaches and cyberattacks by looking at secure communication methods. We provide a study of the available data integrity checks, authentication methods, and encryption technologies to suit the unique requirements of smart grid infrastructure. Furthermore, we assess how well different protocols mitigate common security threats including denial-of-service attacks, man-in-the-middle attacks, and unauthorized data access assaults. The study's conclusions highlight the importance of lightweight security solutions that can maintain high-performance levels and offer reliable protection at the same time. Advanced secure communication protocols can be implemented to increase the resilience of IoT-enabled devices. © 2024 IEEE.

关键词： Data integrity

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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A Dynamic and Secure Join Query Protocol for Multi-User Environment in Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 512-525页

作者： Li, Hongjun He, Debiao Feng, Qi Yang, Xiaolin Luo, Qingcai Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu610041 China Inspur Cloud Information Technology Company Ltd. Shandong Jinan250000 China Inspur Academy of Science and Technology Shandong Jinan250101 China

The development of cloud computing needs to continuously improve and perfect the privacy-preserving techniques for the user’s confidential data. Multi-user join query, as an important method of data sharing, allows multiple legitimate data users to perform join query over the data owner’s encrypted database. However, some existing join query protocols may face some challenges in the practical application, such as practicality, security, and efficiency. In this article, we put forward a dynamic and secure join query protocol in the multi-user environment. Compared with some existing protocols, the proposed protocol has the following advantages. On the one hand, we utilize the dynamic oblivious cross tags structure to realize an efficient join query with forward and backward security. On the other hand, we combine the randomizable distributed key-homomorphic pseudo-random functions with join query to support multiple data users, which can provide resilience against the single user’s key leakage and resist collusion attacks between the cloud server and a subset of data users. We formally define and prove the security of proposed protocol. In addition, we give a detailed analysis of computation and communication overheads to demonstrate the efficiency of proposed protocol. Finally, we carry out some experimental evaluations to further demonstrate the superiority of functionality and efficiency. © 2013 IEEE.

关键词： Structured Query Language

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EDSA-Ensemble: An Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2025年第2期16卷 555-572页

作者： Petrescu, Alexandru Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian National University of Science and Technology Politehnica Bucharest Computer Science and Engineering Department Faculty of Automatic Control and Computers Bucharest060042 Romania Academy of Romanian Scientists 3 Ilfov Bucharest Romania Freie Universitaet Berlin Fraunhofer Institute for Open Communication Systems Berlin14195 Germany Leipzig04109 Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSA-Ensemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. © 2010-2012 IEEE.

关键词： Sentiment analysis

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Multi‐granularity re‐ranking for visible‐infrared person re‐identification

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CAAI Transactions on Intelligence technology 2023年第3期8卷 770-779页

作者： Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonAlbertaCanada System Research Institute Polish Academy of SciencesWarsawPoland

Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient *** is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality *** works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained ***,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐*** this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature *** converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality *** takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity *** experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art ***,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关键词： computer vision recognition

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TINYCOD: Tiny and Effective Model for Camouflaged Object Detection 48

TINYCOD: Tiny and Effective Model for Camouflaged Object Det...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xing, Haozhe Gao, Shuyong Tang, Hao Mok, Tsui Qin Kang, Yanlan Zhang, Wenqiang Information Technology and Electrical Engineering Eth Zurich Zurich Switzerland Fudan University Academy for Engineering & Technology Shanghai China Fudan University School of Computer Science Shanghai China

ISBN: (纸本)9781728163277

关键词： Camouflaged object detection edge features feature fusion image segmentation lightweight model

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I-DLMI: Web Image Recommendation Using Deep Learning and Machine Intelligence 22nd

I-DLMI: Web Image Recommendation Using Deep Learning and Mac...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Kannan, Beulah Divya Deepak, Gerard Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031274084

Web Image Recommendation is the need of the hour because of the increasing exponential contents especially the multimedia content in the World Wide Web. The IDLMI framework has been proposed which is a query centric knowledge driven approach for Web Image recommendation. This model hybridizes Wiki Data and YAGO for entity enrichment and generation of Meta Data takes place which is further subjected to semantic similarity computation and mapped reduce algorithm for mapping and reducing the complexity within the Pearson’s correlation coefficient. LSTM (Deep Learning Intrinsic Classifier) is used for automatic classification of data set. The model also classifies the data for upper ontology which enhances auxiliary knowledge. Performance of the proposed I-DLMI framework approach is calculated by utilizing F-measure, Precision, Recall, Accuracy percentages and False Discovery Rate (FDR) as the potential metrics. The proposed model furnishes the largest average precision of 96.02%, largest average recall percentage of 98.19%, largest average accuracy percentage of 97.10% and the largest F-measure percentage of 97.09%, while the lowest FDR is 0.17. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Bi-CSem: A Semantically Inclined Bi-Classification Framework for Web Service Recommendation 13th

Bi-CSem: A Semantically Inclined Bi-Classification Framework...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Surya, Deepak Palvannan, S. Deepak, Gerard Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031274985

Web services are products in the era of service-oriented computing and cloud computing. As the number of web services on the Internet grows, selecting and recommending them becomes more important. Consequently, in the realm of service computing, how to propose the finest Web services for researchers is now a popular research topic. To determine the proper recommendation, the BI-CSem model was proposed and tested with multiple baseline models using real-world Web Service datasets in this research. Aside from that, thesaurus is built using Web service keywords gathered from Web service repositories such as UDDI,WSDI, and from the World Wide Web Cloud. The extracted terms are then subjected to semantic similarity, which is determined using SemantoSim, Concept similarity, and KL divergence measure, and the terms from the user, such as query, user click, and previous historical data, are pre-processed the terms from the semantic alignment are then classified using XGBoost, while the Web service dataset is classified using XGBoost and GRU. Semantic similarity is determined using just SemantoSim, based on the classification intersection of the top 75 percent words from the extracted terms from the two classifiers and features from the created intermediate term tree generated using STM. Finally, terms are reranked and recommended to the user, and the precision, accuracy, recall, F-measure, and FDR for the Web service recommendation system are calculated, and the Bi-CSem model is found to have an excellent precision percentage of 94.37% and the lowest FDR of 0.06. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Web services

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