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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Bharti, Dilip Kumar Singh, Sanjay Kumar Kumar, Mukesh Kanaujia, Vishal Kumar Aeri, Manisha Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Kashi Institute Of Technology Department Of Computer Science & Applications Mirzamurad U.P Varanasi India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Abes Engineering College Computer Science And Engineering Data Science Department Ghaziabad India

ISBN: (纸本)9798350366846

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals. © 2024 IEEE.

关键词： Diffusion tensor imaging

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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

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Evaluation of convolutional neural network models’ performance for estimating mango crop yield

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第1期9卷 1-18页

作者： Neethi, M.V. Raviraj, P. Department of Computer Science and Engineering-Data Science ATME College of Engineering Affiliated to Visvesvaraya Technological University (VTU) Belagavi India Department of Computer Science and Engineering GSSS Institute of Engineering and Technology for Women Affiliated to Visvesvaraya Technological University (VTU) Belagavi India

In agriculture, crop yield estimation is essential;producers, industrialists, and consumers all benefit from knowing the early yield. Manual mango counting typically involves the utilization of human labor. Experts visually examine each sample to complete the process, which is time-consuming, difficult, and lacks precision. For commercial mango production to produce high-quality fruits from the orchard to the consumer, a quick, non-destructive, and accurate variety classification is required. Because of its effectiveness in computer vision, a convolutional neural network—one of the deep learning techniques—was chosen for this investigation. For yield prediction, a total of eight popular mango cultivars were utilized. A comparison with previously trained models was used to assess the proposed model. The performance of the classifiers was evaluated using evaluation metrics such as accuracy, loss, area under the receiver operating characteristic curve score, precision, recall, F1-score, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen’s Kappa performance measure. In terms of performance evaluation criteria, it was found that the proposed approach outperformed the pre-trained models. The suggested model achieved 98.85% accuracy in the test set, which had 800 images. This outcome demonstrates the tangible applicability of the proposed methodology for mango crop estimation. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Networks Deep Learning Early Detection Machine Learning

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Interactive Physiotherapy Game Using React Three Fiber for Hand Motion Rehabilitation

Interactive Physiotherapy Game Using React Three Fiber for H...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Yadav, Ram Kumar Soni, Ashish Kumar Sakare, Apeksha Manipal University Department of Data Science and Engineering Jaipur India G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331529833

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient's hand. Through the "Create"function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere's boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient's growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber's techniques, methods, and therapeutic potential for physical therapy. © 2025 IEEE.

关键词： Virtual environments

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ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images

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Multimedia Tools and Applications 2024年 1-21页

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © The Author(s) 2024.

关键词： Semantic Segmentation

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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A Linked List Encryption Scheme for Image Steganography without Embedding

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computer Modeling in engineering & sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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Safety-certified Receding-horizon Motion Planning and Containment Control of Autonomous Surface Vehicles via Neurodynamic Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Lyu, Guanghao Peng, Zhouhua Wang, Dan Wang, Jun School of Marine Electrical Engineering Dalian Maritime University Dalian China Department of Computer Science and the School of Data Science City University of Hong Kong Hong Kong

This paper addresses the safety-certified motion planning and containment control of under-actuated autonomous surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and moving obstacles. A three-level modular control architecture is proposed with a trajectory generation module at its planning level, an adaptive guidance module at its guidance level, and a kinetic control module at its control level. Specifically, at the planning level, a safety-certified containment trajectory generator is designed to generate safe trajectories over a rolling time window to achieve containment formation and collision avoidance with neighboring ASVs, stationary obstacles, and moving obstacles via dynamic control barrier functions and twotimescale neurodynamic optimization models. At the guidance level, an adaptive line-of-sight guidance law is developed based on a finite-time predictor to estimate unknown sideslip angles and generate guidance commands. At the control level, an optimal control law is designed based on finite-time neural predictors and control Lyapunov functions for the autonomous surface vehicle with input constraints to follow the desired guidance commands. The effectiveness and characteristics of the proposed method are demonstrated via simulations and hardware-in-theloop experiments for cooperative exploration. IEEE

关键词： Optimization

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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