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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ayushi Dwivedi Mohd Shuaib Aditya Joshi Manoj Diwakar Prabhishek Singh Amit Kumar Mishra Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of CSE Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Graphic Era Hill University Dehradun India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

In a highly dynamic society Human Activity Recognition is a widely used area for the development of systems that are capable of automatically identifying human behavior. Human activity recognition has emerged as the most powerful area for researchers because of the complex and dynamic behavior of humans. Human recognition is implemented in many systems and software due to its large usage. It not only is used today for surveillance and security purposes but also for the physiological understanding of human nature. This tells a better understanding of AI, and deep learning as it utilizes these domains widely. This study represents a hybrid deep learning model with the integration of Long short term memory networks (LSTM) and Convolutional Neural Networks (CNN). Video data is used, and it is converted into multiple frames, this model improves the accuracy of detecting human movement in diverse fields. These results highlighted the potential OF combining spatial and temporal features extraction across sectors such as healthcare, security, sports analytics.

关键词： Deep learning Accuracy Surveillance Traffic control Behavioral sciences Human activity recognition Convolutional neural networks Security Long short term memory Tuning

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A Method of Constructing and Automatically Labeling Radio Frequency Signal Training Dataset for UAV

A Method of Constructing and Automatically Labeling Radio Fr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chao Liu Ruipeng Ma Zheng Si Mingmin Chi School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application School of Cyber Science and Engineering Zhengzhou University Henan Institute of Advanced Technology Zhengzhou University

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%.

关键词： Training Time-frequency analysis Object detection Manuals Labeling Reliability Speech processing

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Research on Multi-dimensional Bilingual Teaching Model of computer Courses Supported by Artificial Intelligence 17th

Research on Multi-dimensional Bilingual Teaching Model of Co...

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17th International Conference on computer science and Education, ICCSE 2022

作者： Zhao, Changhua Mi, Chunqiao Tangbo School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of "computer Introduction" as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Cognizant Resource Balancing in Montgomery Modular Multiplication Optimization for Dsp

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SSRN 2024年

作者： Tao, Qiqi Li, Liying Zhou, Junlong Cao, Guitao Meng, Dan Shanghai Key Laboratory of Trustworthy Computing MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai200062 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

The performance of modular multiplication directly influences the efficiency of a majority of encryption ***, there are numerous FPGA-based acceleration techniques targeting modular ***, many of these implementations require substantial hardware resources or suffer from resource ***, we present a novel FPGA-based implementation of Montgomery Modular Multiplication aimed at addressing these *** design employs a suitable radix bit width and word size based on the digital signal processing (DSP) bit width rather than the conventional binary powers of *** aim to instantiate more modular multipliers using limited resources while minimizing *** also introduce a novel DSP cascade structure, called parallel grouping cascade DSP, which reduces the number of clock cycles of internal *** balance the ratio of lookup table (LUT) and DSP usage, we also use multipliers implemented in the LUT to replace some *** results, implemented on Xilinx Virtex-7 field-programmable gate array (FPGA), demonstrate more than 27\% improvement in throughput on 1024-bit modular multiplication and more than 70\% improvement on 2048-bit compared to the best previous state-of-the-art references. © 2024, The Authors. All rights reserved.

关键词： Digital signal processing

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Malaria and Pneumonia Disease Prediction Using Deep Learning

Malaria and Pneumonia Disease Prediction Using Deep Learning

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Disruptive Technologies (ICDT), International Conference on

作者： Vijendra Rai Manoj Singhal Bhoopendra Dwivedy Pallavi Jain Ritika Department of Information Technology ABES Institute of Technology Ghaziabad India Department of Information Technology GL Bajaj Institute of Technology and Management Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes.

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Bat Algorithm: A Review on Theory, Modifications and applications

Bat Algorithm: A Review on Theory, Modifications and Applica...

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Intelligent Cybernetics Technology & applications (ICICyTA), International Conference on

作者： Hakam Singh Deden Witarsyah Alok Misra Raj Gaurang Tiwari Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Paradesh India Information System Departement School of Industrial and System Engineering Telkom University Bandung Indonesia School of Computer Application Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Due to its many applications in a variety of domains, including clustering, cloud computing, and power systems, metaheuristic algorithms have attracted a lot of interest from the scholarly community. By using their self-explorative and adaptive processes, these algorithms provide certain benefits over conventional methods. One well-known metaheuristic approach is effective in resolving optimization problems with constraints and without constraints. This technique was inspired by nature, notably the echolocation ability of bat species. The Bat Algorithm (BA) makes use of a powerful search mechanism to provide the best results when compared to other algorithms of the same class. The theoretical underpinnings of the BA are explored in depth in this research, along with several changes and advancements that have improved its functionality. These enhancements include adjustments to search tactics as well as other developments. The report also thoroughly examines how the BA may be used across a variety of fields, demonstrating its competence and adaptability. The BA has been effectively used in several real-world applications, from cloud computing to clustering, and each one is carefully investigated. The study also offers a thoughtful examination of the BA's possible advancements and future possibilities. It also entails investigating novel modifications and tactics that might improve its effectiveness in handling challenging optimization issues. This study seeks to direct practitioners and academics toward more effective and efficient metaheuristic algorithms, thereby stimulating improvements in optimization approaches. It does this by summarising the present state of the BA and imagining its future development.

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Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S‑Boxes

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Cybersecurity 2021年第1期4卷 541-560页

作者： Huizhong Li Guang Yang Jingdian Ming Yongbin Zhou Chengbin Jin Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China School of Cyber Security Nanjing University of Science and TechnologyNanjing 210094China Department of Software Engineering and Software Evaluation Beijing Institute of Computer Technology and ApplicationBeijing 100039China

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic *** most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are reVisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),***,the practical effectiveness of these metrics remains still *** the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these *** of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other ***,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the nonprofiled attacks and profiled attacks,*** experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC *** also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled ***,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different ***,all of them are invalid in this scenario.

关键词： Side-channel attacks NIST lightweight cryptography S-Boxes Transparency order Confusion coefficient

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Deep Denoising Improved Super-Resolution Framework Based on Convolutional Neural Network

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SSRN 2023年

作者： Mishra, Pawan Kumar Patni, Jagdish Chandra Saxena, Sharad Rathod, Arun Pratap Singh Department of Computer Engineering and Application GLA University Uttar Pradesh Mathura India Department of Computer Science School of Engineering and Technology Jain University Bangalore India Thapar Institute of Engineering and Technology Punjab Patiala India Department of Electronics and Communication Engineering Graphic Era Uttarakhand Dehradun India

The best method currently being employed in computer vision is super-resolution. This method is used to create high-resolution images from a single low-resolution image with the goals of boosting the frequency component, retrieving lost data, and removing downsampling and noises produced during the image capture process. The fundamental objective of the super-resolution technique is to create a high resolution image by combining non-repetition information inside one or more low-resolution frames. Numerous methods have been put forth in the past where numerous photos are used as low resolution images of the same scene with various transformations. Multi-image super resolution is the term used for this. By using deep learning method high resolution image can be reconstructed very easily. In this research, a deep neural network has been proposed to reconstruct the high resolution image from low resolution image very effectively. Proposed method is name as DDSR i.e. Deep Denoising Super Resolution. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Born-Again Takagi-Sugeno-Kang Fuzzy Classifier with Decoupled Fuzzy Dark Knowledge Distillation

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SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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Dual Graph Inference Network for Weakly Supervised Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Jia Peng, Bo Wu, Xi Southwest Jiaotong University School of Computing and Artificial Intelligence Sichuan Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Chengdu University of Information Technology School of Computer Science Sichuan Chengdu610225 China

Establishing global contextual relationships between objects is crucial for weakly supervised semantic segmentation (WSSS) tasks that lack pixel-level labels. Limited by the efficiency of convolutional operations in capturing long-range dependencies with a limited receptive field and to bridge the gap between image-level annotations and pixel-level labels, we propose a Dual Graph Reasoning Mapping (DGRM) module. When integrated into a convolutional network, it conducts contextual graph reasoning on both spatial and interaction spaces of visual features. The first component of this graph reasoning module involves incorporating commonsense knowledge extracted from an external knowledge base into visual features to promote global contextual reasoning for visual graphs. The second component focuses on reasoning in the projected interaction space, utilizing abstracted object class attributes from high-level visual features to establish dependencies among channels in a potential low-dimensional space. Moreover, to capture correspondences at different semantic levels, we model the feature maps in a pyramid-like structure for graph reasoning at various levels. Extensive experiments on popular datasets, such as PASCAL VOC 2012 and MS COCO 2014, demonstrate the superiority of our approach. Our code is provided at https://***/JIAZHANG666/DGRM. © 2025 IEEE.

关键词： Semantic Segmentation

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