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FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Journal of Cyber Security and Mobility 2023年第4期12卷 465-496页

作者： Shetty, Nisha P. Muniyal, Balachandra Priyanshu, Aman Das, Vedant Rishi Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem;however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy. © 2023 River Publishers.

关键词： Learning systems

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To Study Traffic Behaviors for Detecting Unusual Activities by Analyzing HTTP Packets

To Study Traffic Behaviors for Detecting Unusual Activities ...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Deswal, Pooja Lal, Niranjan Singh, Mayank Srm Institute of Science and Technology Computer Science & Engineering Delhi NCR India Jss Academy of Technical Education Computer Science & Engineering Noida India

ISBN: (纸本)9798350344387

Cloud computing refers to the on-demand resource utilization, storage capability, and networking solutions over the Internet. Although, cloud computing is one of the critical mechanisms that provide the online storage solution with a subscription to the cloud environment. However, there are several security, privacy, and data breach issues with cloud computing. Network intrusion detection systems (NIDS) play a vital role in identifying and mitigating potential security threats. Traditional NIDS are not sufficient for complex and dynamic cloud problems, particularly with the widespread use of Hypertext Transfer Protocol (HTTP) as a primary communication protocol. This article proposes a behavioral intrusion detection system based on machine learning techniques to identify attacks by analyzing the HTTP traffic. The system is designed to monitor and analyze network traffic, specifically focusing on HTTP-based communications, to detect and prevent various types of network intrusions. The proposed NIDS employs a combination of supervised and unsupervised machine learning algorithms to handle the diverse nature of cloud-based network traffic. To enhance the accuracy and efficiency of the NIDS, feature engineering techniques are applied to extract relevant features from HTTP network traffic, including packet payload analysis, header inspection, and traffic flow characteristics. The results demonstrate that the system achieves high detection accuracy while maintaining low false-positive rates, showcasing its potential as a reliable and efficient solution for securing cloud-based networks. © 2023 IEEE.

关键词： Decision tree Intrusion detection NIDS Random Forest

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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SN computer science 2024年第1期5卷 99页

作者： Purushothaman, Sony Shanmugam, G. Siva Nagarajan, Sureshkumar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science Govt. Model Engineering College Kerala Kochi India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnakovil India

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of science articles discussing semantic interoperability solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Internet of medical things Privacy Semantic interoperability

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Video-based Hand Gesture Recognition using Random Forest for Sign Language Interpretation

Video-based Hand Gesture Recognition using Random Forest for...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Himasree, J. Jeevitha, P.L. Deekshitha, K. Kolisetty, Aashrita Naveen, Soumyalatha REVA University School of Computer Science & Information Technology Bangalore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798350361537

Effective communication is essential for all individuals, yet it can be particularly challenging for the deaf community, as sign language is their major means of communication. We are researching how to create a reliable and effective system that can understand sign language motions correctly without the help of a human, realizing the importance of this *** this research work we have chosen to construct our own dataset rather than depending on pre-existing ones that may be accessed online because we understand how important authenticity and relevance are. This method gives us more control over the quality of the data and guarantees that the dataset closely matches our study goals. In addition, our system can be readily expanded and recognizes a wide range of hand motions and signs, including the alphabet. We can support a broad variety of sign language expressions thanks to this versatility, which improves inclusivity and usability. Random Forest is used to encode temporal variations among motions in sign language. The system's overall accuracy and reliability are increased thanks to this methodology, which enables it to detect minute variations in hand gestures and movements. Using the Keras and OpenCV libraries in conjunction with the Python programming language, we build a strong foundation for sign language recognition. In addition to streamlining the development process, these technological tools open the system to a larger developer and research community. In alignment with the research, the proposed work achieved an accuracy of 97% with overall precision, recall and F1 score of 99%. © 2024 IEEE.

关键词： Gesture recognition

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Contamination 5

Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Co...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Sanath, G. Deekshitha, S. Shravya, H. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Computer Science and Engineering Bengaluru India Cambridge Institute of Technology Information Science and Engineering Bengaluru India Senior Data Science Trainer Dhaarini Academy Bengaluru India

ISBN: (纸本)9798331528539

Effective monitoring of water quality remains a problem, in real-time contamination detection and prediction. Despite advancements in the sector, many existing approaches continue to rely on labor-intensive laboratory testing, which limits their capacity to give rapid and accurate results. By leveraging advanced ML/DL models, it is possible to build a more robust and adaptable platform for real-time analysis, prediction, and decision support. Approaches based on models such as Random Forest, XGBoost, CNN, RNN, LSTM, and GRU have been good with the accuracy of water quality predictions. They help identify pollution sources and diagnose water quality issues, providing essential information to stakeholders in sustainable water resource management. The ML and DL approaches help improve water quality monitoring and boost future research in environmental sustainability and natural resource management. © 2024 IEEE.

关键词： Resource allocation

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DRCS: Document Recommendation Framework for Cultural Studies as a Prospective Domain 15th

DRCS: Document Recommendation Framework for Cultural Studies...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Deepak, Gerard Hariharan, Nitin Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Karaikudi India

ISBN: (纸本)9783031889912

The need for extensions of documents for highly special sensitive domains like cultural studies which involves countries, and which involves cultural relationships and political scenarios between cultures and across countries. As the worldwide web continues to evolve, it is transitioning into a densely populated semantic web, which is considered the standard for the next generation of the web, commonly known as web 3.0. This paper suggests a framework for Document Recommendation framework for Cultural Studies (DRCS) that utilizes methods like term frequency - inverse document frequency (TF-IDF) and LDA (Latent Dirichlet Allocation) to analyze the term data set. Metadata generation is used to collect and arrange the information, employing transformers for classification. The XGBoost classifier, which is feature-controlled, is used for data set classification. To measure semantic relevance, strong similarity measures like APMI (Adaptive Pointwise Mutual Information) and Pianka Index, Ren-Konen Index, Sim Rank are used. The model incorporates LDA for topic modelling and utilizes resources like the Google Knowledge Graph API to access community-verified knowledge. To optimize the solutions, the UnderCricket Algorithm optimization algorithm is employed. The proposed model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 94.75%, 96.74%, 95.745%, and 95.7346598% respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.06% is achieved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

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DALR: A Dynamically Adjustable Linear Regression Model for Handling Stream Data

DALR: A Dynamically Adjustable Linear Regression Model for H...

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International Conference on computer, Communication, Control and Information technology (C3IT)

作者： Rishita Mondal Sourab Mandal Paramartha Dutta Arindrajit Pal Department of Computer & System Sciences Visva-Bharati Santiniketan WB India Department of Computer Science and Engineering Academy of Technology Adisaptagram WB India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

In response to the escalating demand for machine learning techniques capable of handling real-time data streams, particularly in applications like stock markets, this research dives deep into the domain of stream regression. The objective is to adeptly navigate challenges posed by evolving data over time, differentiating it from conventional static regression. Existing models, such as Fast and Incremental Model Trees with Drift Detection (FIMT-DD), k-Nearest Neighbours (KNN), and Adaptive Linear Regression (ALR) and some other studies, though competent, exhibit certain limitations like learning based on certain portions of data, using a significantly large memory and not capable of handling each data in every timestamp it arrives. We propose the Dynamically Adjustable Linear Regression Model (DALR) to address these shortcomings. DALR introduces an adaptive regression model that dynamically evolves based on historical knowledge and newly acquired data, providing an innovative solution to the intricacies of dynamic data streams. This endeavor has also highlighted that the DALR algorithm consistently demonstrates strong performance across various datasets according to the RMSE metric, surpassing other state-of -the-art algorithms in most instances.

关键词： Adaptation models Heuristic algorithms Linear regression Aerodynamics Data models Robustness Real-time systems Numerical models Stock markets Streams

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Design and beam dynamics study of a magnet system for an 11 MeV superconducting isochronous cyclotron

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Nuclear science and Techniques 2025年第6期36卷 56-65页

作者： Pan-Pan Zheng Xiang-Hui Wang Zi-Feng He Yue Wu Zhi-Min Dai Wei-Shi Wan School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China School of Computer Engineering Wefang UniversityWeifang 261061China

In recent years,due to the scarcity of domestic radioisotopes,the Chinese government has strongly supported the development of dedicated radioisotope production *** paper presents conceptual design simulations of an 11 MeV,50μA,H^(-) compact superconducting cyclotron for radioisotope *** paper focuses primarily on four aspects:magnet system design,central region configuration,beam dynamics analysis,and extraction system *** paper outlines the cyclotron's primary parameters and key steps in the development process.

关键词： Superconducting cyclotron magnet Beam dynamics Central region Stripping extraction

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