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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Customized Graph-Cut Segmentation with Superpixels for Snowfall Detection on Solar Panels 4

Customized Graph-Cut Segmentation with Superpixels for Snowf...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pavithra, V. Danisha, B.R. Divyadharshini, V. Koresh, H. James Deva Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

Snow accumulation on solar panels reduces their performance in energy generation regions. Hence certain image processing methods were developed for segmenting snow from the normal areas of solar panels. Graph Cut and Flood Fill are the popular semi-automatic method used for this application where it relies on detecting an efficient region for marking the segmentation process. Similarly, the manual intervention fails to provide consistent results in such methods. Therefore, a customized graph cut methodology is proposed in this work by combining super pixel-based feature extraction and k-means clustering. At first, the input images are preprocessed by dividing the pixels to form super pixels as a group to reduce the computational complexity without losing the meaningful information. Features like intensity, edge density, texture, and color are extracted from these super pixel groups for segmenting the snow-covered regions on a solar panel. Further, the extracted features are normalized by a k-means clustering algorithm for identifying the highest intensity regions to observe the snow-covered regions. A graph cut-based segmentation technique is applied at the end to ensure the precise boundary delineation. The work is also incorporated with several post-processing techniques for improving the segmentation mask quality. This proposed method is evaluated against Graph cut and flood fill and satisfied with an average dice similarity coefficient of 93.09% and 95.11%. © 2025 IEEE.

关键词： Image segmentation

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A Comparative Analysis on Various Modified Deep Convolution Neural Networks on Maize Plant Leaf Disease Classification 1

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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Kumar, H. R. Sunil Poornima, K.M. Department of Computer Science and Engineering PES Institute of Technology and Management Shivamoggaa India Department of Computer Science and Engineering JNN College of Engineering Shivamoggaa India

ISBN: (数字)9789819973835

ISBN: (纸本)9789819973828

Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good health is very important for maximum yield. Maize is one of the major food crops in the world and contributes major part in food security. There are various reasons for leaf to get diseases that include pathogens like virus, bacteria, pests and even adverse environmental conditions. The idea of this paper is to take data set of maize leaf images with disease like blight, common rust and gray leaf spot along with healthy leaves, train it on different modified deep convolutional neural networks (DCNNs) and use the trained models to test new leaf images. Deep convolutional neural networks have been modified as per the requirements by introducing new epochs, iterations, activation functions and learning rates. In the proposed work, ten different modified networks have been implemented starting from a simple AlexNet to a more complex network like EfficientNet-b0. In this experiment two different data set have been used, one from a standard data set from Kaggle and another is created manually by capturing images from our neighborhood farm lands at different stage of growth. Finally, a comparative study on different networks with two different data set is carried out. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Enhancing Speech Audio Emotion Recognition for Diverse Feature Analysis through MLP Classifier 1

Enhancing Speech Audio Emotion Recognition for Diverse Featu...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Vignesh Saravanan, K. Sakthi Priya, G. Muthu Kumar, K. Pon Karthika, K. Advaith, K.S. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India Mepco Schlenk Engineering College Department of Computer Science and Engineering Sivakasi India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

The proposed research presents a novel approach to Speech Audio Emotion Recognition, integrating a Multi-Layer Perceptron (MLP) classifier and advanced feature extraction methods. With the growing importance of emotion recognition in speech, particularly in human-computer interaction and sentiment analysis, the study utilizes the RAVDESS dataset and explores features like Mel Frequency Cepstral Coefficients, Mel Spectrogram, Zero-Crossing Rate, Spectral Rolloff, Spectral Flux, and Chroma Vectors. The methodology, encompassing exploratory data analysis, preprocessing, and model training, achieves a notable accuracy of 89.31% on both training and testing sets. Notably, the inclusion of features such as Spectral Roll off and Chroma Vectors proves pivotal in capturing nuanced non-linear emotion patterns. Real-time audio testing further validates the model's effectiveness, demonstrating practical applicability. The developed system not only significantly contributes to advancing Speech Audio Emotion Recognition but also holds promise for real-world applications, enhancing human-computer interaction and sentiment analysis across various domains. In summary, this research introduces a robust and accurate emotion recognition system, leveraging the MLP classifier and diverse feature sets, with potential implications for a range of real-world scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Energy-Efficient Encounter, Buffer and Contact Duration-Based Routing Protocol in Opportunistic Networks 2nd

Energy-Efficient Encounter, Buffer and Contact Duration-Bas...

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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Dutta, Amit Borah, Satya Jyoti Singh, Jagdeep Department of Computer Science and Engineering NERIST Nirjuli Itanagar India Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India

ISBN: (纸本)9789819736003

Opportunistic network (OppNet) is a type of advanced wireless mobile network that has evolved from Mobile Ad-hoc Networks (MANETs) and belongs to the category of Delay Tolerant Network (DTN). The unique features that make OppNet different from other wireless networks are its intermittent connectivity, unstable topology, no dedicated path to the destination from the source, and high node mobility. This further makes the process of data transmission a challenging task. For this a method called "store-carry-forward" is used in which the battery-operated intermediate nodes store the message, discover a suitable relay node, and forward the message to that node. This further leads to the depletion of the battery power or energy of the node in due course of time. In this context, there is a necessity to develop routing models by taking the current energy status of a node into consideration. In this paper, the authors proposed an energy-efficient version of a popular routing mechanism EBC, termed E-EBC where the selection of the next suitable node depends upon the battery power (referred to as Energy) of that node. This model is then compared with existing benchmark protocols- Epidemic, Prophet, EDR, E-Prophet, E-EDR, and EBC. Simulation results (using ONE Simulator) depict that the proposed model performs much better than that of the benchmark models respectively by around 42.73, 41.46, 58.19, 32.03, 65.99, and 44.77% in terms of average latency. The proposed protocol E-EBC is also compared against messages dropped by varying the TTL, Buffer Size, and number of nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Delay tolerant networks

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing COVID-19 Detection with YOLOv5 Object Detection Algorithm in Chest X-ray Images 3

Enhancing COVID-19 Detection with YOLOv5 Object Detection Al...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Duraisamy, Premkumar Arunkumar, D. Niranjani, V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381931

In the ongoing fight against COVID-19, accurate diagnostic tools are crucial. This study focuses on the detection of COVID-19, primarily by analyzing Chest X-ray images to identify and understand the disease. We introduce a new approach that utilizes the YOLOv5 (You Only Look Once) object detection algorithm. YOLOv5 is known for its ability to quickly identify objects, and we have fine-tuned it to perform effectively with X-ray images. The result is a model that excels at identifying intricate COVID-19-related patterns in X-ray images, achieving a high accuracy score of 98%. By carefully adjusting and training the model with specific data, our method achieves even higher accuracy. This modification makes YOLOv5 a powerful tool for automating the detection of COVID-19, offering hope for early detection and timely action in the ongoing global fight against the pandemic. © 2024 IEEE.

关键词： COVID-19

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Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach 2

Eco-friendly Smart Sensor Adjustment using Deep Reinforcemen...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

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A comprehensive analysis of threat vectors in software-defined networking

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Multimedia Tools and Applications 2024年第39期83卷 86791-86832页

作者： Dahiya, Seema Sehrawat, Harkesh Kharb, Seema Siwach, Vikas University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Rohtak India Department of Computer Science and Engineering SRM University Sonepat Sonipat India

In recent years, cyber-attacks have become more frequent and advanced, targeting critical infrastructure, businesses, homes, and government agencies. Detecting and preventing these attacks at the earliest stage possible is crucial to avoid serious harm, including data breaches. Researchers and experts in cybersecurity are looking to Software-Defined Networking (SDN) technologies as a solution to enhance real-time defense against cyber-attacks. SDN revolutionizes traditional networking by offering unprecedented flexibility and control over network resources, making it possible to adapt quickly to emerging threats. SDN provides logically centralized network control by separating the control plane from the data plane. This enables network programming and can block network activity when malicious movement is spotted. This paper presents a comprehensive analysis of threat vectors in SDN. It examines the various ways in which SDN networks are vulnerable to cyber-attacks, including network infrastructure, application layer, and SDN controller. The paper also evaluates the effectiveness of existing security measures and proposes future research directions to enhance SDN security. Overall, the paper highlights the potential of SDN as a powerful tool in the fight against cybercrime and emphasizes the importance of continued research and development to improve SDN security. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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