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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Maurya, Jyoti Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819940707

Heart disease is currently regarded as the main cause of illness. Regardless of age group, heart disease is a serious condition nowadays because most individuals are not aware of their kind and level of heart disease. In this fast-paced world, it is essential to be aware of the different types of cardiac problems and the routine disease monitoring process. As per the statistics from the World Health Organization, 17.5 million deaths are because of cardiovascular disease. Manual feature engineering, on the other hand, is difficult and generally requires the ability to choose the suitable technique. To resolve these issues, IoT, machine learning models and cloud techniques, are playing a significant role in the automatic disease prediction in medical field. SVM, Naive Bayes, Decision Tree, K-Nearest Neighbor, and Artificial Neural Network are some of the machine learning techniques used in the prediction of heart diseases. In this paper, we have described various research works, related heart disease dataset, and comparison and discussion of different machine learning models for prediction of heart disease and also described the research challenges, future scope and discussed the conclusion. The main goal of the paper is to review the latest and most relevant papers to identify the benefits, drawbacks, and research gaps in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Methodologies, applications, and Challenges of Pneumonia Detection of Chest X-Ray images for COVID-19 using IoT-enabled Deep Learning

Methodologies, Applications, and Challenges of Pneumonia Det...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Verma, Garima Prakash, Shiva Madan Mohan Malaviya University of Technology Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9798350323887

There is a great advancement in the domain of Internet of medical Things (IoMT) including other domains of artificial intelligence, machine learning, deep learning which has an extensive possibility of exploring healthcare industry. The IoT devices like sensors, actuators and other devices gets connected to the internet and further they collect the data and store it to a specific location. For further processing of the data, the frameworks of machine learning, deep learning are utilized. These techniques help to get the clear insights of the patient's health data which enables to know the current health status of the patient. Recently, the Covid-19 outbreak has occurred which has influenced millions of people across the globe. This virus has taken life of many people and the infection rate of this virus is still increasing day by day. Researchers and medical staffs are exploring advanced techniques to utilize medical images of the infected person using IoMT and deep learning frameworks so that the root cause can be explored. Different techniques deep neural networks have been explored in this work to detect Covid-19 infected persons which utilizes a chest X-ray dataset. A lot of challenges are there that are being faced by the researchers to detect Covid-19 infected patients from Chest X-ray images. This exhaustive literature review presents different frameworks of deep learning architectures and a comparative study has also been done addressing the recent methodologies, datasets, issues, research gaps and so on. Further, some pre-trained models based on CNN architectures like Xception, VGG16, VGG19 and so on are also discussed. © 2023 IEEE.

关键词： Viruses

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Extracting Aspect Sentiment from Users' Book Reviews for Recommendation

Extracting Aspect Sentiment from Users' Book Reviews for Rec...

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2024 International Conference on Computational Linguistics and Natural Language Processing, CLNLP 2024

作者： Cui, Yachao Zhou, Peng Li, Kang Seng, Bingfeng Cao, Han Shaanxi Normal University School of Computer Science Xi'an China Qinghai University Department of Computer Technology and Application Xining China

ISBN: (纸本)9798350392098

When shopping for books on e-commerce platforms, other users' evaluations of books contain a large number of users' intuitive feelings, from which useful information can be mined to provide buyers with ideas for shopping. In terms of recommendation algorithms, although the existing research work can use various methods to model fine-grained user preferences to improve the recommendation effect. However, we believe there are still shortcomings. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools and are not necessarily applicable to the recommendation domain. Second, the features extracted from reviews are still potential features when incorporated into recommendation algorithms, and they cannot be fully utilized to characterize user embeddings. For this reason, this paper proposes a new user comment extraction scheme for personalized recommendation. We first use text summarization techniques in the field of NLP to preprocess and refine the comment text to reduce data noise and obtain high-quality text information. Subsequently, in order to more accurately characterize user embeddings and item features, we use advanced aspect-based sentiment analysis techniques to extract fine-grained aspectual items and corresponding sentiment polarity from the review text. Finally, experimental validation on a real book dataset is conducted to demonstrate the effectiveness of our model. © 2024 IEEE.

关键词： Embeddings

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A Review on the application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

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computers, Materials & Continua 2024年第1期78卷 197-225页

作者： Xiaozhong Yu Jinhua Zheng College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

In rice production,the prevention and management of pests and diseases have always received special *** methods require human experts,which is costly and *** to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is ***,deep learning technology has been employed to detect and identify rice diseases and *** paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing ***,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and *** study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

关键词： Deep learning rice diseases and pests image recognition object detection

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Enhancing Security in Distributed Drone-Based Litchi Fruit Recognition and Localization Systems

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computers, Materials & Continua 2025年第2期82卷 1985-1999页

作者： Liang Mao Yue Li Linlin Wang Jie Li Jiajun Tan Yang Meng Cheng Xiong Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Shenzhen Polytechnic UniversityShenzhen518055China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.

关键词： Objective detection deep learning machine learning

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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

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computers, Materials & Continua 2024年第6期79卷 4641-4657页

作者： Jiahui Liu Lang Li Di Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys ***,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within *** challenges significantly hinder key recovery *** paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these ***-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained ***,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each ***,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct ***,an intelligent search method is proposed to identify optimal parameters,further improving attack *** were conducted on both simulated environments and real power traces collected from the SAKURA-G *** the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

关键词： Side-channel analysis correlation power analysis genetic algorithm crossover mutation

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Side-Channel Leakage Analysis of Inner Product Masking

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computers, Materials & Continua 2024年第4期79卷 1245-1262页

作者： Yuyuan Li Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of *** previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected *** this paper,we investigate the security characteristics of IPM under different *** adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate *** implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear *** observe that the security of IPMis affected by the degree and the linear component in the leakage *** addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

关键词： Side-channel analysis inner product masking mutual information nonlinear leakage

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Detecting and recognizing faces from video images using multi-deep CNN based rank-level fusion 2

Detecting and recognizing faces from video images using mult...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ghosh, Manas Sing, Jamuna Kanta RCC Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350371314

The advent of deep neural networks has provided a competitive edge in facial analysis efforts, especially the detection and recognition of faces from video images. This paper outlines a methodology wherein a face database is created from an outdoor video clip, and a rank-level fusion mechanism has been proposed for face recognition using a database that makes use of the probabilities produced by the softmax functions of multiple deep convolutional neural network (CNN) models. These probabilities are referred to as certainty indices for the potential classes to which a test sample may belong. For each of the CNN models, fuzzy ranks are obtained for a subset of classes with the top certainty indices by applying a Gaussian distribution function followed by a complementation operation. For each class, appearing in one or more subsets corresponding to deep network models, average of the certainty indices is complemented and fused with the sum of complemented fuzzy ranks. While calculating the cumulative sum of the fuzzy ranks, a penalty, is foisted to the class for not being present in any subset. This can avoid the chance of becoming an unexpected winner. The class with the lowest final ranking was identified as the class pertaining to the test sample. In addition to testing with the custom-made outdoor video-based face dataset, the projected method was evaluated with a standard surveillance indoor dataset, ChokePoint. Considering all test sets on ChokePoint dataset, the average recognition accuracy of the proposed method is 99.54%. The same for custom-made dataset is 93.96%. For both datasets, precision and macro-average scores of the proposed method in all cases are higher than the individual deep CNN models. © 2024 IEEE.

关键词： Face recognition

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TM2SP: A Transformer-based Multi-Level Spatiotemporal Feature Pyramid Network for Video Saliency Prediction

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5236-5250页

作者： Li, Chenming Liu, Shiguang Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300350 China Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

This paper proposes an end-to-end video saliency prediction network model, termed TM2SP-Net (Transformer-based Multi-level Spatiotemporal Feature Pyramid Network). Leveraging the strong encoding learning capability of Video Swin Transformer for video data, we design a Multi-level Spatiotemporal Feature Pyramid Network (MLSTFPN) that effectively detects and enriches salient regions and spatial details across different scales. In particular, a pre-trained image saliency detection encoder is employed to extract salient features from each frame, serving as prior knowledge to guide the multi-scale spatiotemporal feature fusion and decoding processes. Additionally, we introduce an Inception Gate-Controlled Fusion (IGCF) and Layered Self-Attention Aggregation Fusion (LSAF) mechanisms to efficiently merge spatiotemporal features across various stages. Finally, extensive experiments conducted on the DHF1K, Hollywood-2, UCF-Sports, and six audio-visual saliency datasets demonstrate the superiority of our method over existing state-of-the-art approaches. © 1991-2012 IEEE.

关键词： Signal encoding

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Blockchain Enabled Autonomous Vehicle Based Vehicular IoT System

Blockchain Enabled Autonomous Vehicle Based Vehicular IoT Sy...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Hemani Singh, Daya Shankar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

Blockchain technology is gaining its popularity in many areas with huge success which ultimately attracts all researchers to study this technology. And the result of that shows more and more research to be conducted. Which makes blockchain technology relevant in the field of transportation systems. It is expected that in near future their integration will achieve greater heights, as compared to other technology blockchain is still new and many more unknown things still need to be found out. The aim of this study is to find out how blockchain benefits in Autonomous vehicles. We have compared the blockchain integrated autonomous vehicle systems to get the picture of the present environment and the challenge of the future for this integration. AV's or we say intelligent transportation is the future. We are going to study the potential application of the blockchain which will help in improving this particular section of industry. Finally their limitations and challenges associated with this integration. © 2023 IEEE.

关键词： Blockchain

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