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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

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computer Systems science & engineering 2023年第7期46卷 1215-1231页

作者： G.Selvakumar L.S.Jayashree S.Arumugam Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004India Department of Computer Science and Engineering Nandha College of EngineeringErode638052India

Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed *** methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,*** the advantages;Microservices bring some challenges *** microservices need to be invoked one by one as a *** most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s *** results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of *** communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting *** were done for both interactive as well as non interactive workloads to test the effectiveness of the *** approach has been proved to be very effective in reducing latency in the case of long service chains.

关键词： Microservices load balancing cloud computing latency optimization netflix

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Enhancing Image Super-Resolution with GANs Featuring Enhanced Deep Super-Resolution and Attention Mechanisms 4

Enhancing Image Super-Resolution with GANs Featuring Enhance...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Kumar, G. Aditya Kativar, Aditi Kannan, A. Vellore Institute of Technology Computer Science and Engineering Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387063

Improving the quality and resolution of low- resolution digital images is an important task with far-reaching implications for a variety of applications, including medical imaging, surveillance, and content retrieval. To solve the high- resolution problem of a single image, this study proposes a new Generative Adversarial Network (GAN) model that includes an Enhanced Deep Super-Resolution (EDSR) generator. Our model includes a sensitivity function and a Squeeze-and- Excitation (SE) block in an EDSR design. These SE blocks directly model channel connections to optimize channel-specific performance responses and improve network capabilities without incurring significant computational costs. After extensive training using complex data processing methods such as rotation, rotation, and rotation, our model shows an impressive ability to enhance image details and traditional culture more accurately. Our strategy outperforms existing models both statistically and qualitatively with better signal-to- noise ratios (PSNRs) on the DIV2K dataset. These results, together with deep learning models, demonstrate the role of attentional processes in improving image resolution and lay the groundwork for further work in this field. © 2024 IEEE.

关键词： Generative adversarial networks

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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Enhancing Indian sign language recognition through data augmentation and visual transformer

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Neural Computing and Applications 2024年第24期36卷 15103-15116页

作者： Singla, Venus Bawa, Seema Singh, Jasmeet Department of Computer Science Thapar Institute of Engineering and Technology Adarsh Nagar Punjab Patiala147004 India

This paper introduces a novel approach to Indian Sign Language Recognition (ISLR) by integrating Keras, Visual Transformers (ViT), and sophisticated data augmentation techniques. Our methodology emphasizes the development of a Vision Transformer model trained on a comprehensive dataset, enhanced with both image data and keypoint information, leveraging the capabilities of the Mediapipe library. To improve the model’s ability to generalize, we applied advanced augmentation strategies, including ImageDataGenerator, among others. Through extensive experimentation, involving rigorous hyperparameter optimization across numerous epochs, we sought to determine the most effective model configuration. The results of our validation process revealed a promising evaluation loss of 0.2941 and an impressive accuracy rate of 97.52%. This integration of data augmentation techniques with ViT transformers establishes a groundbreaking framework in the field of ISLR, underscoring its significant potential for practical implementation and marking a substantial progression in the technology for sign language recognition. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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Interpretable machine learning for chiral induced symmetry breaking of spin density boosting hydrogen evolution

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Journal of Energy Chemistry 2025年第4期103卷 68-78页

作者： Xin Song Zhonghua Li Li Sheng Yang Liu School of Chemistry and Chemical Engineering Key Laboratory of Microsystems and Microstructures ManufacturingMinistry of EducationHarbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001HeilongjiangChina

The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions(HER).This study utilizes theoretical calculations and machine learning techniques to assess the HER performance of both chiral and achiral M-N-SWCNTs(M=In,Bi,and Sb)single-atom catalysts(SACs).The stability preferences of metal atoms are dependent on chirality when interacting with chiral *** HER activity of the right-handed In-N-SWCNT is 5.71 times greater than its achiral counterpart,whereas the left-handed In-N-SWCNT exhibits a 5.12-fold *** calculated hydrogen adsorption free energy for the right-handed In-N-SWCNT reaches as low as-0.02 *** enhancement is attributed to the symmetry breaking in spin density distribution,transitioning from C_(2V)in achiral SACs to C_(2)in chiral SACs,which facilitates active site transfer and enhances local spin ***-handed M-N-SWCNTs exhibit superiorα-electron separation and transport efficiency relative to left-handed variants,owing to the chiral induced spin selectivity(CISS)effect,with spin-upα-electron density reaching 3.43×10^(-3)e/Bohr^(3)at active *** learning provides deeper insights,revealing that the interplay of weak spatial electronic effects and appropriate curvature-chirality effects significantly enhances HER performance.A weaker spatial electronic effect correlates with higher HER activity,larger exchange current density,and higher turnover *** curvature-chirality effect undersco res the influence of intrinsic structures on HER *** findings offer critical insights into the role of chirality in electrocatalysis and propose innovative approaches for optimizing HER through chirality.

关键词： Symmetry breaking Machine learning Spin density CISS DFT Hydrogen evolution reaction

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A Novel and Optimized Collaborative Diversity-Driven Routing Mechanism in MANETs

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SN computer science 2024年第1期5卷 1-12页

作者： Malyadri, Neelam Ramakrishna, M. Department of Computer Science and Engineering Vemana Institute of Technology VTU Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering Vemana Institute of Technology Karnataka Bengaluru India

Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In this research, we propose a novel routing mechanism called collaborative diversity-driven routing (CollabNet) that integrates collaborative diversity to enhance the routing process in ad hoc networks. CollabNet utilizes a collaborative approach to address the challenges posed by dynamic network environments. CollabNet incorporates collaborative power control, adaptive power allocation, and energy-efficient routing to automatically adjust the dynamic power of nodes. By promoting collaborative behavior and optimizing power levels. It fosters collaborative behavior among nodes by establishing cooperation groups and leveraging multiple relay nodes. Through collaborative multipath diversity CollabNet facilitates the transmission of packets toward the destination via multiple paths within the transmission range. This approach increases the probability of successful packet delivery by establishing redundant connections. By promoting collaboration among nodes, CollabNet improves the reliability and connectivity of wireless ad hoc networks. The mechanism leverages the strength of collaborative diversity to enhance the overall performance of routing protocols. Extensive simulations demonstrate that CollabNet outperforms traditional routing protocols in terms of network connectivity, reliability, and efficiency. The proposed mechanism offers a promising solution for efficient routing in MANETs contributing to the advancement of wireless communication in dynamic ad hoc network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Collaborative diversity Collaborative diversity-driven routing MANETs Routing protocols Wireless ad hoc networks

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Occlusion robust sign language recognition system for indian sign language using CNN and pose features

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Multimedia Tools and Applications 2024年第36期83卷 84141-84160页

作者： Das, Soumen Biswas, Saroj Kr. Purkayastha, Biswajit Computer Science Engineering National Institute of Technology Silchar NIT Road Assam Silchar788010 India

The Sign Language Recognition System (SLRS) is a cutting-edge technology that aims to enhance communication accessibility for the deaf community in India by replacing the traditional approach of using a human interpreter. However, the existing SLRS for Indian Sign Language (ISL) do not focus on some major problems including occlusion, similar hand gesture, multi viewing angle problem and inefficiency due to extracting features from a large sequence of frame that contains redundant and unnecessary information. Therefore, in this research paper an occlusion robust SLRS named Multi Featured Deep Network (MF-DNet) is proposed for recognizing ISL words. The suggested MF-DNet uses a histogram difference based keyframe selection technique to remove redundant frames. To resolve occlusion, similar hand gesture, and multi viewing angle problem the suggested MF-DNet incorporates pose features with Convolution Neural Network (CNN) features. For classification the proposed system uses Bi Directional Long Shor Term Memory (BiLSTM) network, which is compared with different classifier such as LSTM, ConvLSTM and stacked LSTM networks. The proposed SLRS achieved an average classification accuracy of 96.88% on the ISL dataset, 99.06% on the benchmark LSA64 dataset and 99.85% on the WLASL dataset. The results obtained from the MF-DNet is compared with some of the existing SLRS where the proposed method outperformed the existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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High Dynamic Range Imaging for Dynamic Scenes Based on Multi-Level Spike Camera

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5394-5406页

作者： Zhu, Zhenkun Xiong, Ruiqin Zhao, Jing Zhao, Rui Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking University School of Computer Science Beijing100871 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the current design is that each spike only represents the arrival of a fixed amount of photons. It can not deal with strong light areas in which the amount of accumulated photons reaches the pre-specified threshold multiple times within a single readout interval. In this paper, we propose a new spike camera model of high-speed imaging for high dynamic range scenarios. In this scheme, each pixel accumulates the incoming photons persistently and generates a new type of spike stream in which each spike symbol may be associated with different levels, indicating the arrival of different amounts of photons since the last readout. This enables the camera to support dynamic scenes with wider dynamic range. To achieve this, we propose a two-level buffer mechanism, one for photon accumulation and one for spike-firing encoding. We use a register to hold the number of spike-firings which has not been read out yet. At each readout time, the major part in the counter is read out via a carefully designed exponential encoding and the counter is updated. Such encoding and readout strategy enables a very efficient expansion of the dynamic range using a small number of encoding bits. Furthermore, we propose an image reconstruction scheme for the proposed camera, utilizing both spike intervals and spike levels to recover the light intensity. We incorporate Mamba and propose a temporal-spatial selective scan mechanism to extract temporal-spatial correlation within spike streams. We employ a pyramid adaptive filtering and alignment module to achieve coarse-to-fine feature alignment. Experimental results show that the proposed scheme can achieve better imaging quality and outperform the existing spike camera in high dynamic range scenarios. © 1991-2012 IEEE.

关键词： Adaptive filtering

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Performance Evaluation of EfficientNet Models in Grape Leaf Disease Classification 1

Performance Evaluation of EfficientNet Models in Grape Leaf ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Subramanya, S.G. Parkavi, A. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Grape farming is a globally significant agricultural practice, but grapevines frequently encounter viral, fungal, and bacterial infections that compromise crop quality and yield. Conventional disease detection methods are labor-intensive and require significant expertise. This study investigates deep learning techniques, focusing on EfficientNet models, to automate grape leaf disease identification and classification. The transfer learning approach was applied to train and fine-tune EfficientNet models on a publicly available dataset with four classes: black rot, leaf blight, esca, and healthy leaves. A model performance was measured through accuracy and loss metrics. EfficientNetB0 achieved accuracy of 92.45% and 96.73%, and loss of 0.197 and 0.098, on training and validation data, respectively. However, EfficientNetB5 and EfficientNetB7 gave validation accuracies of 96.07% and 96.39%, respectively. The accuracy and loss trends demonstrate that EfficientNetB0 outperforms the other two counterparts. This approach highlights the potential of EfficientNetB0 for accurate, efficient grape disease detection and classification. © 2025 IEEE.

关键词： Plant diseases

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