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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Singh, Kulvinder Sharma, Tanu Yadav, Anup Lal Rani, Jaya Chandigarh University Dept Of Computer Science & Engineering India Panipat Institute Of Engineering & Technology Dept Of Computer Science & Engineering India

ISBN: (纸本)9798350306446

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. In this research, a sophisticated smart system for learning video recommendations is proposed at the intersection of multimodal data mining and data synthesis in online education. The approach aims to enhance personalized and effective learning experiences by transcending the limitations of traditional recommendation systems. Innovative data synthesis techniques are employed to expand the dataset and improve the adaptability of the system, integrating diverse data sources including text, images, and videos. The methodology involves a comprehensive analysis of learning materials using cutting-edge technologies like natural language processing, computer vision, and video content analysis to deepen the understanding of learner-content interactions. Furthermore, the challenge of hashtag recommendation in short videos is addressed by leveraging their multimodal nature, combining sentiment and content features from image, audio, and text modalities to enhance the accuracy and rationality of hashtag recommendations. This research contributes to redefining the landscape of intelligent learning video recommendations, offering a dynamic platform designed to continuously adapt to the evolving needs and preferences of learners. © 2024 IEEE.

关键词： Data mining

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Person re-identification using vision transformer and centroid triplet loss

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Multimedia Tools and Applications 2024年第29期83卷 73777-73788页

作者： Ijjina, Earnest Paul Medipelly, Rampavan Beerukuri, Santosh Kumar Vinnakota, Sowmya Nelakurthi, Vijay Chowdary Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Warangal506004 India

In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage from a different camera or the same camera at various points in time. The goal is to accurately identify a person despite variations in their appearance due to changes in pose, illumination, and occlusions. Person re-ID is a key component in video surveillance with practical applications in public safety, retail, and transportation, among others. However, it remains a difficult problem due to the inherent variability in appearance and the lack of robust features to capture the subtle differences between individuals. Even the existing Convolution Neural Networks (CNNs) based approaches for person re-ID task struggle to address the issues due to variations in pose, occlusions, and background clutter. To tackle these issues in person re-ID task, we propose an approach using Vision Transformers (ViT) with Centroid Triplet Loss (CTL). Experimental studies conducted on Market1501 and DukeMTMC datasets, yielded better results than the existing approaches, indicating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cameras

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Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

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Recent Advances in Electrical and Electronic engineering 2025年第6期18卷 735-747页

作者： Somnathe, Abhijit T. Gopinath, N. Deena, G. Anandh, R. Kumar, Santosh Tiwari, Kanchan S. Electronics and Telecommunication Smt Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Electrical and Electronics Engineering Gaya College of Engineering Bihar India Electronics and Telecommunication Modern Education Society’s Wadia College of Engineering Pune India

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Methodology: However, the Internet-of-Things (IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results: This study also contributes in two ways to the ongoing effort to investigate securityprivacy concerns in the circumstance of smart cities for healthcare purposes. Conclusion: Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented. © 2025 Bentham science Publishers.

关键词： Cyber attacks

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Optimization enabled LeNet for big data classification using MapReduce framework on COVID-19 data

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Australian Journal of Electrical and Electronics engineering 2024年第4期21卷 409-424页

作者： Patle, Bhagyashree R. Vijayarajan, V. VIT Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

The big data clustering is a requisite for generating the data in the digitalised globe. The old-fashioned clustering approaches are not large sized and highly unorganised big data. Thus, to obtain the efficiency of big data clustering, a new architecture is required. This work introduces the Jaya African Vulture Optimization Algorithm-based LeNet (JAVO-based LeNet) for big data classification on COVID-19. The big data clustering is detected by using the LeNet model, which is tuned by the JAVO algorithm. Here, the JAVO algorithm is the integration of the Jaya algorithm and AVOA algorithm. Further, using the Deep Embedded Clustering (DEC) technique for partitioning the smaller data to improve the performance of big data classification. The classification process for COVID-19 is established in the MapReduce (MR) framework. For each mapper, the pre-processing is completed based on Min-Max normalisation and the feature fusion is performed based on the Hellinger distance, which is measured with a deep residual network (DRN). Finally, the classification process for COVID-19 is done by considering the fused data acquired from each mapper with LeNet, which is tuned by the JAVO algorithm. Furthermore, the analysis of the proposed method attains the testing accuracy of 0.948, specificity of 0.939 and sensitivity of 0.950. ©, Engineers Australia.

关键词： Cluster analysis

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Assessment and Evaluation of Music Genre Classification by employing various AI Techniques 7

Assessment and Evaluation of Music Genre Classification by e...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Adithya, S. Aijith Prabhakar, U.T. Ankayarkanni, B. Anto Praveena, M.D. Padma Suresh, L. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Music genre classification, the task of automatically assigning audio recordings to specific genres, offers exciting possibilities for music recommendation systems, personalized playlists, and music analysis. This abstract explores the potential of (K-NN), a versatile Artificial Intelligence (AI) algorithm, in tackling this challenge- NN excels in its simplicity and interpretability. It classifies new music by identifying its closest neighbors within a labeled dataset based on predefined features, such as tempo, timbre, or spectral content. These neighbors then collectively "vote"on the genre of the new piece. The abstract delves into the feature engineering process, highlighting the crucial role of selecting relevant musical characteristics for accurate classification. Furthermore, the abstract discusses the impact of the "K"hyper parameter, which determines the number of neighbors considered in the voting process. Choosing the optimal "K"value balances accuracy and sensitivity to genre nuances. While K-NN offers advantages in ease of implementation and understanding, the abstract acknowledges its potential drawbacks. The storage requirements can be high for large datasets, and computational costs may increase with data size. It concludes by acknowledging the need for further research to address computational challenges and explore advanced feature engineering techniques to unlock the full potential of K-NN in this exciting domain. © 2024 IEEE.

关键词： Audio recordings

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Deep Learning Based Face Detection and Identification of Criminal Suspects

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computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.Balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

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Sentiment analysis of multi social media using machine and deep learning models: a review

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Multimedia Tools and Applications 2024年第42期83卷 90033-90051页

作者： Vasanthi, P. Madhu Viswanatham, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

The Internet has become an important origin of text information, which is then used inside a wide range of research domains. This has been regarded as a necessary foundation for institutions to obtain valuable information in addition to assessing consumers' feelings and views on a particular subject. Message clustering is the method of assigning specific tags to known categories based on one ‘s content material. It is difficult to classify the text using aspect-based sentiment. So each research on sentiment has addressed this issue, as existing research focuses on file and predominant component assessment instead of the specifics of the emotions. This survey reviewed the sentiment analysis based on both X and Facebook. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Multilevel chaotic encryption model with cyclic redundancy check for medical data

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Neural Computing and Applications 2025年第5期37卷 3581-3603页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communication through open networks is insecure and has many vulnerabilities, making it susceptible to unauthorized access and misuse. Encryption models are used to secure medical data from unauthorized access. In this work, we propose a bit-level encryption model having three phases: preprocessing, confusion, and diffusion. This model is designed for different types of medical data including patient information, clinical data, medical signals, and images of different modalities. Also, the proposed model is effectively implemented for grayscale and color images with varying aspect ratios. Preprocessing has been applied based on the type of medical data. A random permutation has been used to scramble the data values to remove the correlation, and multilevel chaotic maps are fused with the cyclic redundancy check method. A circular shift is used in the diffusion phase to increase randomness and security, providing protection against potential attacks. The CRC method is further used at the receiver side for error detection. The performance efficiency of the proposed encryption model is proved in terms of histogram analysis, information entropy, correlation analysis, signal-to-noise ratio, peak signal-to-noise ratio, number of pixels changing rate, and unified average changing intensity. The proposed bit-level encryption model therefore achieves information entropy values ranging from 7.9669 to 8.000, which is close to the desired value of 8. Correlation coefficient values of the encrypted data approach to zero or are negative, indicating minimal correlation in encrypted data. Resistance against differential attacks is demonstrated by NPCR and UACI values exceeding 0.9960 and 0.3340, respectively. The key space of the proposed model is 1096, which is substantially mor

关键词： Telemedicine

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HSALC: hard sample aware label correction for medical image classification

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Yangtao Ye, Yicheng Xie, Yanzhao Tang, Maobin Fan, Lisheng Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Medical image automatic classification has always been a research hotspot, but the existing methods suffer from the label noise problem, which either discards those samples with noisy labels or produces wrong label correction, seriously preventing the medical image classification performance improvement. To address the above problems, in this paper, we propose a hard sample aware label correction (termed as HSALC) method for medical image classification. Our HSALC mainly consists of a sample division module, a clean·hard·noisy (termed as CHN) detection module and a label noise correction module. First, in the sample division module, we design a sample division criterion based on the training difficulty and training losses to divide all samples into three preliminary subsets: clean samples, hard samples and noisy samples. Second, in the CHN detection module, we add noise to the above clean samples and repeatedly adopt the sample division criterion to effectively detect all data, which helps obtain highly reliable clean samples, hard samples and noisy samples. Finally, in the label noise correction module, in order to make full use of each available sample, we train a correction model to purify and correct the wrong labels of noisy samples as much as possible, which brings a highly purified dataset. We conduct extensive experiments on five image datasets including three medical image datasets and two natural image datasets. Experimental results demonstrate that HSALC can greatly promote classification performance on noisily labeled datasets, especially with high noise ratios. The source code of this paper is publicly available at GitHub: https://***/YYC117/HSALC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

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