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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kadam, Bhagyashree Swapnil Dewangan, Leelkanth Parida, Prasanta Kumar Chaterjee, Tanushree Nahak, Kirti Sadashiv, Ebhad Swagat Sinhgad Institute of Technology and Science Narhe Pune It Department India Swangi Dept of Computer Science & Engineering Wardha India Kiit University Dept of Computer Science & Engineering Odisha Bhubaneswar751024 India Raipur Institute of Technology Department of Biotechnology Raipur India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Trinity Academy of Engineering Department of Engineering Science Kondhwa Pune India

ISBN: (纸本)9798350330915

Breast cancer continues to be a major worldwide health issue, demanding accurate subtype categorization for personalized treatment approaches. In order to accurately identify subtypes, this work introduces a unique hybrid classifier that uses multi-modal data integration as well as cutting-edge machine learning algorithms. The model uses a Convolutional Neural Network (CNN) for image-based feature extraction alongside machine learning techniques for genomic and clinical features, combining mammographic, histopathological, genomic, and clinical data. The interpretative framework, which depends on an interpretivist philosophy, makes it less difficult to comprehend subtype discrimination on a more in-depth level. With an overall accuracy of 92.5% across HER2-positive, estrogen receptor-positive, and triple-negative subtypes, the results clearly demonstrate the hybrid classifier's better performance. The model's sensitivity and specificity ratings regularly outperformed those of existing techniques, demonstrating its aptitude for picking up on fine subtype distinctions. Particularly, the classifier demonstrated a low incidence of false positives, which is critical for reducing pointless interventions. Key genetic markers, physical traits, and hormone receptor status have been recognized as important discriminators employing feature importance analysis. These discoveries broaden the understanding of the biological foundations of each subtype. The study offers a useful tool for accurate breast cancer detection and treatment planning in clinical practice. The hybrid classifier's integrative methodology makes use of several data sources in order to provide a thorough understanding of breast cancer subtypes. This work lays the groundwork for future oncology and multi-modal data analysis studies, with potential implications in personalized therapy and other cancer types. There is still much more to discover about ethical issues, model generalization, and therapeutic use in th

关键词： Breast Cancer Subtype Classification Convolutional Neural Networks (CNNs) Interpretivism Philosophy Multi-modal Data Integration Personalized Treatment Strategies

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DIGITAL TWINS FOR REAL-TIME MONITORING AND OPERATION OF COFFEE VALUE CHAIN AND SUPPLY CHAIN

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and Applied Mechanics 2024年第17期2024卷 114-124页

作者： Le, Chi An Le, Chi Hieu Nguyen, Van Duy Zlatov, Nikolay Le, Tan Hung Nguyen, Tien Anh Chu, Anh My Mahmud, Jamaluddin Le, Van Dang Nguyen, Ho Quang Ramesh, Dharavath Mengistu, Samueal Behera, Amar Packianather, Michael S. Faculty of Science and Engineering University of Greenwich Kent United Kingdom Institute of Biotechnology and Environment Nha Trang University Nha Trang Viet Nam Institute of Mechanics Bulgarian Academy of Sciences Sofia Bulgaria SOICT Hanoi University of Science and Technology Hanoi Viet Nam Le Quy Don Technical University Hanoi Viet Nam College of Engineering Universiti Teknologi MARA Selangor Malaysia ARTC Agency for Science Technology and Research Singapore Institute of Engineering and Technology Thu Dau Mot University Binh Duong Viet Nam Computer Science and Engineering Indian Institute of Technology Dhanbad India Department of Design Indian Institute of Technology Kanpur India School of Engineering Cardiff University Cardiff United Kingdom

There has been significant effort and a growing need to develop innovative and cost-effective solutions for real-time monitoring and operation of value chains and supply chains, especially to enhance the predictability and optimisation of complex production systems for a better adaptation to disruptions and market fluctuations as well as improved sustainability. This is particularly important when taking into account the impacts and emerging advancements of smart agriculture, smart manufacturing, Digital Twins, and Industry 5.0, where data-driven solutions and AI-enabled decision-making play an important role for improving real-time monitoring, quality control and management, and operational efficiency. This study presents a conceptual framework for integrating Digital Twins into a smart agriculture platform, focusing on the real-time monitoring and operation of the coffee value chain and supply chain, to demonstrate the potential of Digital Twins in advancing smart agriculture and digital supply chains. © 2024, Cefin Publishing House. All rights reserved.

关键词： Smart manufacturing

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Improving Surveillance Object Detection with Adaptive Omni-Attention over Both Inter-frame and Intra-frame Context 1

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16th Asian Conference on computer Vision, ACCV 2022

作者： Yu, Tingting Chen, Chen Zhou, Yichao Hu, Xiyuan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783031262845

ISBN: (纸本)9783031262838

Surveillance object detection is a challenging and practical sub-branch of object detection. Factors such as lighting variations, smaller objects, and motion blur in video frames affect detection results, but on the other hand, the temporal information and stable background of a surveillance video are major advantages that does not exist in generic object detection. In this paper, we propose an adaptive omni-attention model for surveillance object detection, which effectively and efficiently integrates inter-frame contextual information to improve the detection of low-quality frames and intra-frame attention to suppress false positive detections in the background regions. In addition, the training of the proposed network can converge quickly with less epochs because during multi-frame fusion stage, the pre-trained weights of the single-frame network can be used to update simultaneously in reverse in both single-frame and multi-frame feature maps. The experimental results on the UA-DETRAC and the UAVDT datasets have demonstrated the promising performance of our proposed detector in both accuracy and speed. (Code is available at https://***/Yubzsz/Omni-Attention-VOD.) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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A secure two-party PIR for privacy applications

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International Journal of Intelligent Information and Database Systems 2022年第4期15卷 450-467页

作者： Bhat, Radhakrishna Reddy, N.V. Subba Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The major drawback of existing information retrieval schemes in preserving user privacy is that they either exhibit computationally bounded privacy with intractability assumptions or perfect privacy with high bandwidth utilisation. Today, the essential requirement is to have a bandwidth efficient perfect privacy preserving information retrieval scheme in order to provide effective and guaranteed service to the information retrieving user. Therefore, in this paper, we have constructed a new single database perfect privacy preserving private block retrieval scheme called as ‘pepperPBR’ using quadratic residuosity as the underlying primitive where private block retrieval (PBR) is a natural extension to private information retrieval (PIR). In this paper, user generates O(6k) bit query and server generates O(2ku + o(n)) bit response where n is the database size, o(n) is the non-trivial server communication cost, u is the number of database blocks, k is the security parameter. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Information retrieval

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Alzheimer's disease detection using Multi Atlas Segmentation based on Temporal Group Sparse Regression

Alzheimer's disease detection using Multi Atlas Segmentation...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Velkumar, K. Chokkalingam, Bala Subramanian Solairaj, A. Vignesh, L.S. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhunagar India Nadar Saraswathi College of Engineering and Technology Theni India Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Nadar Saraswathi College of Engineering and Technology Deapartment of Ai&ds Theni India

ISBN: (纸本)9798350317060

Imaging genomics stands as a valuable approach for identifying potential genetic factors associated with Alzheimer's disease (AD) within genomic and imaging datasets. Most existing techniques in imaging genomics employ a linear model to investigate the connection between quantitative traits (QTs) derived from brain imaging and genetic data (single nucleotide polymorphisms). This method often fails to consider the connections between clusters of Quantitative Traits (QTs) and Single Nucleotide Polymorphisms (SNPs), as well as the complex interactions which involves longitudinal imaging quantitative traits and Single Nucleotide Polymorphisms. In order to tackle these constraints, we introduce a fresh approach named Temporal Group Sparsity Regression and Additive Model (TGSRAM). This pioneering technique strives to reveal associations between longitudinal imaging QTs and SNPs, with the objective of pinpointing potential biomarkers for Alzheimer's Disease (AD). Our approach seeks to offer solutions to these challenges. Furthermore, we introduce an alternative methodology termed multi-atlas-based morphometry in our study. This methodology assesses morphometric representations of the same image across numerous atlas spaces. By generating representations from various atlases, we harness supplementary insights to distinguish between different groups and mitigate the adverse impacts stemming from registration errors. In practice, each subject under investigation undergoes registration to multiple atlases, from which adaptive regional features are extracted. Subsequently, a correlation and relevance-based approach is employed to collectively select features from diverse atlases. This is followed by the ultimate classification step using the hybrid support vector machine (HSVM). © 2023 IEEE.

关键词： Genome

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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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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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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