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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Sneha Seth, Jyoti Hannah Jessie Rani, R. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

the emergence of the Internet of Things (IoT) gadgets and cloud-enabled networks has brought about a dramatic growth in the quantity and kinds of programs that require real-time facet computing offerings. Aspect computing is a rising location of allotted computing that methods facts toward the cease person or tool, supplying low-latency responses to operations and analytics. It enables quicker and more dependable facts-extensive applications that can be personalized to consumer alternatives and needs. This paper discusses the design and implementation of real-time edge computing offerings for IoT-primarily based cloud networks, focusing on key components of the device structure, including carrier deployment, aid management, protection, and scalability. Furthermore, the paper describes the usage of area computing for IoT programs, which include video surveillance, domestic automation, and smart town solutions. Subsequently, the paper presents a survey of cutting-edge area computing companies, summarizing their offerings, business models, and services. © 2024 IEEE.

关键词： Analytics Applications Automation Business Models computing Real-Time Services

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Enhancing 6G mmWave Beam Prediction in V2I with Class Imbalance Mitigation 35

Enhancing 6G mmWave Beam Prediction in V2I with Class Imbala...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. Ahmedabad University School of Engineering and Applied Science India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Powai Mumbai India

ISBN: (纸本)9798350362244

The increase in the use of autonomous vehicles has motivated a paradigm shift in the transportation domain as it redefines the boundaries of urban mobility by augmenting safety measures. This research paper explores an innovative approach to enhance 6G millimeter-wave (mmWave) beam prediction for vehicle-to-infrastructure (V2I) communications by using generative adversarial networks (GANs). By generating synthetic data samples effectively and balancing the real-world dataset, we improve the accuracy of beam prediction models significantly. Our proposed method of training random forests on synthetic data (RFGAN) to predict beam indices provides the solution for imbalanced class issues and significantly improves the predictive performance of mmWave beam selection, contributing to more reliable and efficient V2I communications. This work also performs comparative analysis with state-of-the-art models in top-K evaluation metrics, average power loss, and overhead savings related to adapting to the new approach. © 2024 IEEE.

关键词： Generative adversarial networks

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The Smart and Secured Communication Using Applied Cryptography Algorithms 5th

The Smart and Secured Communication Using Applied Cryptograp...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Jebakumari, S. Adlin Hemlata Ayesha, Amreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the implementation of a at ease communication device the usage of an implemented cryptography algorithm. The gadget is designed to provide comfy statistics and voice communique between endpoints using techniques consisting of encryption and digital signature. The applied cryptography set of rules includes primary components, the symmetric and the uneven algorithms. The symmetric algorithms, along with AES and DES, are used for statistics encryption, even as the uneven algorithms, which include RSA and DSA, are used for virtual signature. The proposed gadget additionally focuses on security of physical places to defend the important thing exchange by means of the use of physical access control measures which include biometric identification. The system additionally employs advanced safety strategies together with message authentication codes and protection protocols like SSL/TLS. The device is examined for robustness and safety using real communique experiments. The results confirm that the proposed machine is cozy and green in supplying comfortable communication among endpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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DaC-GANSAEBF:Divide and Conquer-Generative Adversarial Network-Squeeze and Excitation-Based Framework for Spam Email Identification

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computer Modeling in engineering & sciences 2025年第3期142卷 3181-3212页

作者： Tawfeeq Shawly Ahmed A.Alsheikhy Yahia Said Shaaban M.Shaaban Husam Lahza Aws I.Abu Eid Abdulrahman Alzahrani Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Information Technology College of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information and Communication Technology College of Computing StudiesArab Open UniversityKuwait BranchKuwait City13033Al-SafatState of Kuwait Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia

Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security *** spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data *** address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam *** framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual *** approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated *** model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the *** findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection *** framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.

关键词： Email spam fraud light dual attention squeeze and excitation divide and conquer-generative adversarial network-squeeze and excitation-based framework security

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Deep RL-Based Smart Signaling Using Space-Time Vehicular Features Under C-V2X Scenario 16

Deep RL-Based Smart Signaling Using Space-Time Vehicular Fea...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Shah, Kashish D. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India LIT Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

As of recent, the majority of developing countries have faced traffic congestion issues. As a result, vehicular communication technologies such as DSRC and Cellular-V2X are standardized by the 3GPP to support the transportation community. The C-V2X standard proposed in 3GPP Re114 provides high data transmission facilities for V2V and V2I communication. In this paper, we attempt to solve the traffic congestion problem by considering the V2I communication facility as per the C-V2X standard. We propose an online learning strategy based on Deep Reinforcement Learning for smart signaling to avoid traffic congestion at the intersections. These are the key aspects to address the congestion issue. We conduct extensive near-real-time simulations in PTV VISSIM considering the Swastik Cross Road intersection at Ahmedabad, Gujarat, India, imported via OpenStreetMap. With the proposed smart signaling strategy, we found significant improvement in vehicles' average travel time and average space occupancy compared to existing traffic signaling methods. Uplifting this research and implementing such real-time strategies will significantly impact the current transportation system. © 2024 IEEE.

关键词： Cellular-V2X Deep Reinforcement Learning Intelligent Transportation Systems Smart Signaling Vehicular Communication

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Optimized deep transfer learning techniques for spine fracture detection using CT scan images

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Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part

关键词： computerized tomography

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A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

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Recent Advances in computer science and Communications 2023年第5期16卷 82-92页

作者： Reddy, Pundru Chandra Shaker Sucharitha, Yadala School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods: A unique energy-saving Distance-Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusion: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN. © 2023 Bentham science Publishers.

关键词： Cognitive radio

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6723-6738页

作者： Qin, Lutong Zhang, Lei Li, Chengrun Song, Chaoda Cheng, Dongzhou Wang, Shuoyuan Wu, Hao Song, Aiguo Nanjing Normal University School of Electrical and Automation Engineering Nanjing210023 China Shanghai200240 China Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States University of Macau Department of Computer and Information Science Taipa999078 China Yunnan University School of Information Science and Engineering Kunming650500 China Southeast University Department of Instrument Science and Engineering Nanjing210096 China

Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. © 2020 IEEE.

关键词： Budget control

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Real Time Identification of Harmful Birds in Agriculture Field using computer Vision 7

Real Time Identification of Harmful Birds in Agriculture Fie...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sudhesh, K.M. Akhila, T. Thangavel, Senthil Kumar Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidhyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India Ashland University Department of Mathematics And Computer Science OH United States

ISBN: (纸本)9798350359299

Agriculture is essential to the global economy and food security. Insect and bird attacks have now been found in recent research that could cause crop loss. Crops are prone to bird attacks throughout the early stages of crop production and are particularly sensitive from the milky stages up to maturation. Observing such birds, which are harmful to agriculture, is an expensive, time-consuming, and possibly error-prone operation. Furthermore, a vast area necessitates a huge labour force for bird scaring. The concept of automated monitoring through computer vision holds significant appeal. This is because it has the potential to facilitate the collection of comprehensive data on a much larger scale than is currently feasible. In this work, we leverage effective characteristics from images of 10 different hazardous birds, Especially in the realm of identification we employing transfer-learned deep learning models which holds promise. The resemblance of various bird features, as well as the difficulty of distinguishing them at a glimpse, has become a significant challenge. We used more than 1000 images of harmful birds from various species for model training. In experiments on two different transfer learned deep CNN models, the xceptionNet model showed the best performance with a test accuracy of 94% when compared to the relatively low memory model VGG19, which had a test accuracy of 80%.The analysis also revealed that the xception model performed better in terms of accuracy, precision, recall, and F1-score. © 2024 IEEE.

关键词： Birds

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