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检索条件"任意字段=3rd International Conference on High Performance Computing in Science and Engineering, HPCSE 2017"

共 1550 条记录，以下是71-80 订阅

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SVM-based Detection of Bone Fracture using Weighed Local Binary Pattern Features

SVM-based Detection of Bone Fracture using Weighed Local Bin...

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international Symposium on Advanced computing and Communication (ISACC)

作者： N. Sathish Balaji Hemachandran. M Pawan Kumar. B Sriram Kumar. S R. Jansi Department of Electronics and Communication Engineering SRM Institute of Science and Technology India Faculty of Engineering and Technology SRM Institute of Science and Technology India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The work presented in this research outlines a new method for detecting bone fractures, which is based on a machine learning model. To achieve this goal, Support Vector Machine (SVM) classifiers are trained on a dataset of images depicting bone fractures and normal bones enabling them to categorize bone images into two classes - bone fracture and non-bone fracture images. The results have shown that the system attains a great performance of 98.42% accuracy, suggesting high correctness of fracture detection cases. Moreover, the articulated system also comprises state-of- the- art image preprocessing methods such as removal of noise, any adaptive histogram equalization, additional sharpening and edge detection of images for the purpose of improving features. Among key developments, is the introduction of a unique feature which is termed as Weighed Local Binary Pattern (WLBP) for the inquisitive information containing textures and patterns in the images. Therefore, the present study tends to provide a very strong as well as an accurate model for fracture assessment, which is likely to improve the quality of the diagnosis and the patients’ care.

关键词： Support vector machines Histograms Adaptive systems Accuracy Noise Machine learning Adaptive equalizers Bones Feature extraction Intelligent systems

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Enhancing Smart Grid Security with Quantum Cryptography: A BB84-based Framework

Enhancing Smart Grid Security with Quantum Cryptography: A B...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Shail Garg Yerukola Gayatri A. Surya Kausthub Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid.

关键词： Protocols Computer network reliability Bit error rate Smart grids Encryption Quantum key distribution Reliability Communication networks Quantum cryptography Standards

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Predictive Maintenance Analysis Using Multivariate Machine performance Data for Industrial Downtime Reduction

Predictive Maintenance Analysis Using Multivariate Machine P...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Rajeswara Reddy Saddala D. Radha V. S. Kirthika Devi Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Tool downtime prediction requires efficient monitoring because it helps maximize industrial operation quality while maintaining uninterrupted plant operations. The analysis utilizes machine learning algorithms with Decision Trees to identify machine downtime events through operational measurements which include hydraulic pressure and coolant temperature and spindle speed and vibration levels. Data normalization alongside Forward Selection feature selection methods were used on a balanced dataset to boost model performance for prediction purposes. The Decision Tree model functioned to differentiate machine defects into two discrete categories-failure and no failure-thus proving its competence in such predictive classification activities. The study conducted a comparative evaluation between Decision Trees and Random Forest along with other algorithms which included SVM and Logistic Regression and Adaboost in addition to CatBoost and XGBoost and Naive Bayes and KNN using KD-Tree and Ball-Tree variations. The experimental findings show that Decision Trees provide clear predictions coupled with high precision and fair runtimes to support predictive maintenance implementations. The research delivers fundamental findings that help improve downtime prediction methods to boost operational effectiveness.

关键词： Vibrations Temperature measurement Support vector machines Accuracy Predictive models Prediction algorithms Maintenance Decision trees Random forests Predictive maintenance

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Optimizing Renewable Energy Distribution in Smart Microgrids Using IoT and Reinforcement Learning

Optimizing Renewable Energy Distribution in Smart Microgrids...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： P L Kishan Kumar Reddy Gurumoorthi Gurulakshmanan E. Sivanantham Dillibai. R Vishwa Gopalakrishnan S. Murugan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University (Has to be deemed) Guntur Andhra Pradesh India Mphasis Corporation Texas USA Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India New York University Tandon School of Engineering MetroTech Center Brooklyn NY United States Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Smart microgrids improve energy efficiency by integrating renewable energy sources, but their intermittent nature complicates steady power distribution. This research introduces an IoT-enabled Reinforcement Learning (RL) framework for optimizing renewable energy distribution in smart microgrids. IoT sensors collect real-time data on energy production, use, and environmental variables, enabling adaptive management. The RL model adaptively acquires optimum energy distribution techniques to attain load equilibrium, energy storage enhancement, and grid stability. The system uses autonomous decision-making to reduce energy waste, increase cost efficiency, and enhance resilience. The proposed system facilitates real-time demand response, optimizes power distribution, and assures effective use of distributed energy resources. Experimental findings illustrate the efficacy of RL in mitigating uncertainty in energy supply and demand. This research advances the development of intelligent, self-adaptive microgrids, enabling a more sustainable and efficient energy infrastructure. Previous studies will investigate hybrid AI models to enhance performance further.

关键词： Renewable energy sources Supply and demand Power distribution Microgrids Reinforcement learning Power system stability Real-time systems Energy efficiency Stability analysis Smart grids

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Advanced Machine Learning Models for Electrical Fault Detection and Classification in Transmission Lines

Advanced Machine Learning Models for Electrical Fault Detect...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Dugganapalli Deepika Maridinapalli Madhu Charan Sri Chakradhar Nossam P.V Manitha Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems.

关键词： Support vector machines Fault diagnosis Power transmission lines Computational modeling Fault detection Power system stability Electrical fault detection Transmission line measurements Decision trees Random forests

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Efficient Resource Management for Real-Time AI Systems in the Cloud using Reinforcement Learning

Efficient Resource Management for Real-Time AI Systems in th...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Jagadeesh Vedula Saravanakumar Baskaran Sathyanarayanan Sayiram Rajkumar Muthusamy Ketan Gupta N. Yuvaraj Digital Transformation Houston Texas USA Microsoft Corporation Seattle Washington USA IBM San Jose CA USA NTT Data Irving Texas USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The research utilizes reinforcement learning to enable dynamic resource allocation, ensuring that resources are provisioned on demand rather than relying on static allocation methods defined at design time.

关键词： Cloud computing Costs System performance Reinforcement learning Dynamic scheduling Load management Real-time systems Planning Resource management Artificial intelligence

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A Contrastive Meta-learning Approach with Isotropic Sparse Decomposition for Scalable Audio-Visual Learning 3rd

A Contrastive Meta-learning Approach with Isotropic Sparse ...

引用

3rd international conference on computing, Communication and Learning, CoCoLe 2024

作者： Dixit, Dhruv Pandey, Paritosh Jha, Raman Bhatnagar, Pranshul Satapathy, Shashank Mouli Department of Chemical Engineering BITS Pilani Hyderabad Campus Telangana Hyderabad500078 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632004 India Department of Computer Engineering Savitribai Phule Pune University Maharashtra Pune411007 India

ISBN: (纸本)9783031790409

This study investigates integrating auditory and visual data in computational models, focusing on challenges like audio-visual question answering (AVQA). Current methods often separate audio and visual processing, leading to scalability issues and high computational costs. By leveraging transformer-based architectures like Lin et al.’s LAVisH framework, we propose novel techniques to improve AVQA, especially in musical AVQA tasks. Our method combines advanced image decomposition with a contrastive meta-learning framework to enhance LAVisH for better AVQA performance. Empirical results show a 1.54% improvement for visual questions and 1.2% for audio-visual questions on the MUSIC-AVQA dataset. Furthermore, our framework demonstrates commendable performance by testing AVQA tasks on video lectures, given the problem’s complexity and lack of alternative solutions. By substituting RGB overlapping patches with isotropic sparse decomposition, we expand the number of trainable parameters, facilitating wider application across AVQA tasks beyond musical AVQAs with minimal computational overhead. This framework thus extends its utility to a broader spectrum of AVQA tasks while maintaining low computational costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Voice Over Internet Protocol: Custom SIP Server with QoS Optimization for Real-Time Communication

Voice Over Internet Protocol: Custom SIP Server with QoS Opt...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： B. V. Dinesh Krishna P. Yogendrasai S. Rajeev Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

VoIP is a revolutionary technology for transmission of voice over internet protocol than from plain telephony systems. But current VoIP solutions have some important issues which are packet loss, latency, and jitter especially when the network is in motion. This work outlines the design, implementation and performance of a locally developed VoIP system which includes a SIP Server Node, Sender Node and a Receiver Node. The SIP Server Node was designed nearly from the ground up to handle session initiation, packet forwarding, and adaptive QoS control. Both Sender and Receiver Nodes had facilities to record, compress and playback audio. The system's performance was evaluated in a LAN environment, achieving significant improvements in Quality of Service (QoS) metrics: achieving low latency under 150 ms, 95 % packet recovery and achieving a good audio quality with the MOS of 4.2. Evaluating the obtained results, it can be concluded that the proposed system is efficient in handling the issues of the packet-switched network and providing high-quality real time voice communication.

关键词： Packet loss Quality of service Receivers Jitter Real-time systems Encryption Servers Security Internet telephony Optimization

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Optimized Binary Classifier For Dos Detection Using Tabu Search Method

Optimized Binary Classifier For Dos Detection Using Tabu Sea...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： Lavanya Murugan A.Syed Musthafa R.Mekala American International University Kuwait Department of Information Technology M.Kumarasamy College of Engineering Tamil Nadu India Department of Artificial Intelligence and Data Science Akshaya College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the proposed system, an optimized binary classifier for the Denial-of-Services (DoS) attacks detection using the Tabu Search Method is created. The primary approach for DoS detection, in the current era, struggles with the problem of excessive false positive errors during its analysis of the data and also the wastage of time and cost during the computation, which is why it is difficult to get real-time threat prevention ready. The goal is to overcome these drawbacks, so the proposed system has a Tabu Search-based feature selection that improves the dataset by choosing only the most important features making classification more accurate and the computational time less. Actually, the binary classifier is trained using a hybrid machine learning approach. It is based on combining Support Vectors Machine (SVM) and Decision based Tree (DT) models. The Tabu based Search algorithm caters to an optimal feature selection by escaping local minima and frequently renewing search parameters to obtain the maximum classification performance. Additionally, the system undergoes an adaptive thresholding mechanism which not only makes it more resilient to variations of the attack patterns but also enhances its robustness by boosting the classifier threshold. The experimental Results show that the proposed system is capable of accomplishing a very high level of detection accuracy, rendering false positives irrelevant and so ensuring that the processing time is brought down compared to the conventional methods. This research highlights a scalable and effective method for the detection of DoS attacks in real-time in the context of current security frameworks.

关键词： Support vector machines Adaptation models Accuracy Adaptive systems Search methods Computational modeling Feature extraction Real-time systems Telecommunication computing Testing

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Autoregressive Diffusion Networks for Trustworthy Node Identification and Optimized Routing in Wireless Sensor Networks

Autoregressive Diffusion Networks for Trustworthy Node Ident...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： M. Satheesh Kumar P. Ganesh Kumar S. Amudha T. Kavitha P. Ravikumar M. Raghini Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of IT K.L.N. College of Engineering Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Instrumentation K.L.N. College of Engineering Department of Artificial Intelligence and Data Science Velammal College of Engineering and Technology

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Finding and eliminating unwanted nodes is a foremost challenge in wireless sensor networks (WSNs). This paper proposes an Autoregressive Diffusion Network (ADN) to find and categorize the nodes in the network as affected or trustworthy. Here, the proposed ADN provides a strong foundation for finding uncommon or insecure actions by effectively recording the characteristics of the sensor over a period of time. Thus, using the capability to capture successive data. In addition, an improved routing mechanism is developed using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols. Then, the Pelican Optimisation Algorithm (POA) is used to optimize the cluster heads optimally. To further improve the routing mechanism using fitness proportional selection technique to transfer the data safely and effectively. Simulation results show that the proposed technique enhances the performance by reducing energy consumption and increasing the detection accuracy of affected nodes. The proposed LEACH + POA + ADN model shows the improved detection accuracy of 77%, false positive rate of 7.50%, average energy consumption of 8.02 J, network lifetime of 92.94 rounds, and packet delivery ratio of 92.97%, which is high compared to other benchmark models.

关键词： Wireless sensor networks Energy consumption Accuracy Protocols Simulation Clustering algorithms Routing Energy efficiency Security Reliability

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