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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Mittal, Shilpi Bali, Rasmeet S University Institute of Computing Chandigarh University India Chandigarh University Department of Computer Science & Engineering India

ISBN: (纸本)9798350349900

Effective communication in vehicular networks is essential for realizing the potential benefits of intelligent transportation systems, including improved safety, reduced congestion, and enhanced overall traffic management. Ongoing research and development continue to advance the capabilities and reliability of communication technologies in this domain. The available literature papers contain a number of proposed techniques for reliable communication for content distribution in vehicular networks. This paper presents the state of art for existing communication techniques along with the upcoming future trends for content distribution in vehicular networks. The motivation behind this work is to provide the audience with the required understanding of the content dissemination in various types of vehicular networks highlight with potential opportunities and challenges, and also investigating the recent studies available in reputed resources directed towards the domain of content dissemination in vehicular networks. © 2024 IEEE.

关键词： computing Connected Autonomous Vehicles HVN ITS Vehicular Communication

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Optimizing Vanet Communications: A Bibliometric Analysis of Energy-Efficient Strategies with Deep Learning

Optimizing Vanet Communications: A Bibliometric Analysis of ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Prakash, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Vehicular Ad Hoc Networks (VANETs) are important for intelligent transportation systems, permitting vehicles to communicate with one another and nearby infrastructure in order to enhance road safety, traffic management and infotainment services. But the VANET communication is a high energy consumption technology, which may become a big challenge of large-scale power efficiency being in some real scenarios where huge number vehicles are needed to implement such V2I or V2V services sustaining. This paper conducts a analysis of different transmission stratagems for energy-efficient communication strategies among vehicles in VANETs, along with the focus to incorporate deep learning accelerators into an optimized architecture framework for enhancing communication processes. The paper presents an overview of VANET architecture and communication protocols, and then reviews the techniques to save energy in a VANET including efficient mechanisms for Transmission Power Control (TPC), some energy-aware routing protocols, as well as data aggregation realizations. Next, the paper introduces deep learning models that can adaptively optimize VANET transmission by forecasting network states and updating strategies on-the-fly. This analysis emphasizes hybrid solutions where deep learning is combined with traditional communication strategies to improve the energy efficiency apparent on a system level. Finally addresses the current challenges in the field and the need for advanced AI-driven techniques. This bibliometric analysis provides a comprehensive overview of energy-efficient communication strategies in VANETs, offering insights into how deep learning can contribute to the development of more sustainable vehicular networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Characterization of Exact One-Query Quantum Algorithms for Partial Boolean Functions

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Journal of Computer science & Technology 2023年第6期38卷 1423-1430页

作者：叶泽坤李绿周 Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen UniversityGuangzhou 510006China

The query model(or black-box model)has attracted much attention from the communities of both classical and quantum ***,quantum advantages are revealed by presenting a quantum algorithm that has a better query complexity than its classical *** the history of quantum algorithms,the Deutsch algorithm and the Deutsch-Jozsa algorithm play a fundamental role and both are exact one-query quantum *** leads us to con-sider the problem:what functions can be computed by exact one-query quantum algorithms?This problem has been ad-dressed in the literature for total Boolean functions and symmetric partial Boolean functions,but is still open for general partial Boolean ***,in this paper,we continue to characterize the computational power of exact one-query quantum algorithms for general partial Boolean ***,we present several necessary and sufficient conditions for a partial Boolean function to be computed by exact one-query quantum ***,inspired by these conditions,we discover some new representative functions that can be computed by exact one-query quantum algorithms but have an essential difference from the already known ***,it is worth pointing out that before our work,the known func-tions that can be computed by exact one-query quantum algorithms are all symmetric functions and the quantum algo-rithm used is essentially the Deutsch-Jozsa algorithm,whereas the functions discovered in this paper are generally asym-metric and new algorithms to compute these functions are ***,this expands the class of functions that can be computed by exact one-query quantum algorithms.

关键词： quantum computing quantum query complexity quantum algorithm

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Enhancing High-Frequency Inverter Performance in WPT EV Charging Systems by Reducing Current Ripple Through Step Density Modulation Techniques

Enhancing High-Frequency Inverter Performance in WPT EV Char...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Franklin, J. Pongiannan, R.K. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

This paper analyses Step Density Modulation (SDM) techniques for high-frequency inverters in Wireless Power Transfer (WPT) systems for Electric Vehicle (EV) charging. Pulse Density Modulation (PDM) is widely used to control high-frequency inverters, but it struggles to manage current ripple, which can cause EMI, power transfer efficiency loss, and system degradation. In this paper, Step Density Modulation is offered as an upgrade over standard PDM to reduce effects of dead time in inverters by providing more precise pulse density and timing control. PDM modifies pulse density evenly, whereas Step Density Modulation dynamically varies pulse density in discrete stages to match the inverter's switching characteristics. Current ripple and harmonics are reduced, enhancing waveform quality and power transfer efficiency. A thorough mathematical model compares current ripple magnitude, frequency spectrum, and inverter delay time for the two modulation techniques. Detailed MATLAB models simulated on a 5 kW WPT EV charger show that Step Density Modulation outperforms PDM in reducing current ripple and improving inverter performance. Step Density Modulation reduces current ripple by 30% more than PDM, smoothing inverter output and improving system stability. This innovative modulation scheme improves WPT system efficiency and reliability for EV charging applications. © 2024 IEEE.

关键词： Pulse width modulation

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Comparative Methods of Implementation for Different Question Answering Systems 5

Comparative Methods of Implementation for Different Question...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Naveen, C. Amutha, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798350385649

This research introduces an innovative Question Answering (QA) system tailored explicitly for government department inquiries regarding individuals. Harnessing the prowess of cutting-edge language models such as BERT and T5 (Text-to-Text Transfer Transformer), the system excels in understanding complex queries within diverse governmental domains. Moreover, it incorporates a specialized Knowledge Graph meticulously curated with interconnected information about people across various departments. By integrating BERT and T5 for versatile query comprehension and answer generation alongside a comprehensive People-centric Knowledge Graph, this system aims to revolutionize information retrieval within government entities. The seamless fusion of these technologies promises accurate, contextually rich responses, optimizing operational efficiency across government departments and fostering streamlined access to crucial information. © 2024 IEEE.

关键词： Knowledge graph

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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Computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT 7

An Interpretation of the Challenges and Solutions for Agricu...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Surya, M. Manohar, S. Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamilnadu India

ISBN: (纸本)9781665464086

Healthcare, the smart grid, and supply chain management are some of the industries where IoT(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, and the people around them. Internet of things achieves this by using wearable technology, embedded sensors, and automated systems. The number, types, and sizes of IoT devices are increasing. There are issues with security, authenticity, reliability, and scalability. Because of the expected growth in IoT, IoT systems must be reliable and maintainable. Blockchain has helped solve IoT network problems. Blockchain improves trust, immutability, transparency, and release of agricultural data collection, processing, and sharing. Blockchain and IOT will streamline precision farming and transform normal farms into the Internet of Smart Farms. This study describes the IoT systems have issues, and blockchain-based solutions are suggested. Additionally, it discusses current research on the degree to which blockchain technology and Internet of Things networks. IoT integration inside the blockchain issues are also covered. This article proposes to double connect IoT and blockchain using recent computing techniques. This research work intends to make data processing and storage decentralized, secure and anonymous, and seamless and fast using blockchain technology and services. © 2023 IEEE.

关键词： Cloud computing architecture

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Diabetic Foot Ulcer Classification Using Deep Learning Approach 8th

Diabetic Foot Ulcer Classification Using Deep Learning Appro...

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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Sivakamasundari, P. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

Podiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause "Diabetic Peripheral Neuropathy," a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. The basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. This work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set. © IFIP International Federation for Information Processing 2025.

关键词： Diagnosis

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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud computing

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IEEE Access 2025年 13卷 92816-92832页

作者： Slathia, Ashwin Singh Sharma, Abhiram Krishna, P.B. Anand, Saksham Rathi, Ayush Joseph, Linda Gao, Xiao-Zhi Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India University of Eastern Finland School of Computing Kuopio Finland

Cloud computing plays a crucial role in modern technology, providing scalable and on-demand computing resources. However, excessive resource use can result in higher energy demand, higher operating expenses, and a more significant adverse effect on the environment as per study by A. Berl et al. [1]. In order to forecast and maximize the energy efficiency in cloud systems, this paper presents a machine learning based methodology. Using the Cloud Efficiency Dataset from Google Cloud, key performance indicators such as CPU usage, memory consumption, network traffic, and power consumption were analyzed. Three machine learning models—Random Forest, Naïve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. Among them Random Forest model performed the best, achieving an RMSE of 0.16 and an accuracy of 96.8 %. To improve the interpretability of the model Explainable Artificial Intelligence (XAI) techniques were applied, specifically SHapley Additive exPlanations (SHAP), to evaluate feature importance. The findings demonstrated that the most important variables which influence energy efficiency are CPU usage and memory consumption. This research develops A novel algorithm based on the XAI results. Unlike static thresholding or heuristic schedulers, our SHAP-driven approach dynamically adapts VMplacement decisions with built-in interpretability. It is capable to efficiently schedule and allocateVirtual Machines on the cloud in a live environment. The proposed framework demonstrates tthe high scalability and real time applicability, making it suitable for the deployment in live cloud environments. While the integration of XAI methods like SHAP enhances interpretability, it introduces additional computational overhead—a trade-off that is justified by the improved transparency and decision making support. © 2013 IEEE.

关键词： Resource allocation

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