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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Prabha, M. Manicka Jegadeesan, S. Jayavathi, S.D. Rajkumar, G. Vinoth Jothi, J. Nirmal Krishnan, R. Santhana SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India JP College of Engineering Department of Electronics and Communication Engineering Ayikudi Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350395235

This research study explores the transformative potential of IoT-Enabled Smart Mirrors to redefine home connectivity by enabling seamless internet browsing and smart home integration. It introduces a novel model algorithm, IoT-ViSense (IVS), which leverages IoT sensor data fusion with vision-based Convolutional Neural Networks (CNN) for real-time environmental monitoring and security. A comprehensive simulation analysis of IoT-ViSense is conducted and compared against existing algorithms using pertinent simulation metrics. IoT-Enabled Smart Mirrors serve as multifunctional interfaces, integrating personalized weather updates, news, calendar schedules, and real-time compliments. They also incorporate features such as a built-in camera for capturing high-quality selfies, on-demand workout sessions facilitated by fitness experts, and the ability to browse the internet, make calls, and interact with other connected devices. The proposed IoT-ViSense (IVS) algorithm combines IoT sensor data with Vision-based CNN analysis to enhance environmental monitoring and security. It continuously processes data from IoT sensors and camera feeds, detecting anomalies and patterns in real-time. By fusing these data streams, IVS enhances security by identifying unauthorized access and suspicious activities while concurrently optimizing resource usage and ensuring comfortable living and working environments. The simulation analysis of IoT-ViSense (IVS) is compared with suitable existing algorithms, assessing its efficiency in terms of accuracy, response time, and resource optimization. Results reveal that IoT-ViSense (IVS) outperforms other algorithms, demonstrating its prowess in achieving a harmonious blend of smart home integration and real-time environmental monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

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3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images 10

A Multi-input Deep Neural Network Framework for Non-invasive...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Krithika, S. Reddy Poreddy, Ajay Kumar Balakrishna, Thunakala Kokil, Priyanka Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai601103 India Lab Department of Electronics and Communication Engineering Chennai600 127 India

ISBN: (纸本)9798350350951

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIELAB color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework's performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation. © 2024 IEEE.

关键词： Deep neural networks

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Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

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International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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Enhanced and Automatic Defect Detection in Thermography using Stacked Denoising Autoencoder 5

Enhanced and Automatic Defect Detection in Thermography usin...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Vesala, G.T. Ghali, V.S. Lakshmi, A. Vijaya Wang, Fei Yerneni, Naga Prasanthi Malla Reddy University School of Engineering Department of Computer Science Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Vijayawada India Amrita Sai Institute of Science and Technology Department of Electronics and Communication Engineering Paritala India Harbin Institute of Technology School of Mechatronics Engineering Harbin China

ISBN: (纸本)9798350376685

Active infrared thermography (AIRT) with frequency-modulated stimuli has emerged as a feasible and cost-effective non-destructive testing method for checking a variety of materials, with improved fault identification and depth resolution. However, assessing this non-stationary thermal response while reducing noise and inhomogeneous backgrounds is difficult in FMT. Conventional thermographic data analysis techniques use signal or image processing methods and supervised learning models, which are known for linear representation or feature extraction of thermographic data. This paper describes a stacked denoising autoencoder (SDAE) in frequency modulated thermography (FMT) that extracts non-linear information from temporal thermal profiles to improve flaw detection in a steel structure. The SDAE trains on temporal thermal profiles in order to generate denoised profiles at the other end. Furthermore, the latent space in the middle of the SDAE decreases dimensionality while highlighting flaws. Finally, the latent space of SDAE and local outlier factor (LOF) were coupled to form SDAE-LOF, which provided automatic fault detection in unsupervised learning passion. Experimentation results on a mild steel structure show an improvement in defect signature by reducing noise in temporal thermal profiles, and the proposed SDAE-LOF achieved an AUC of 0.81 in automatic defect detection. © 2024 IEEE.

关键词： Thermography (imaging)

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Design and Analysis of a 4x4 Antenna Array 10

Design and Analysis of a 4x4 Antenna Array

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10th International Conference on Advanced Computing and communication Systems, ICACCS 2024

作者： Bansal, Dipali Gupta, Sindhu Hak Kaur, Harleen Amity University Department of Electronics and Communication Engineering Uttar Pradesh Noida India Jamia Hamdard Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350384369

This research article explores the design and analysis of a 4x4 antenna array using MATLAB, a versatile and powerful computational tool widely employed for simulating and optimizing antenna systems. The antenna array is a fundamental component in modern wireless communication systems offering advantages in terms of enhanced gain, directivity and beam forming capabilities. The primary objective of this study is to optimize the performance of the 4x4 antenna array through MATLAB simulations considering crucial parameters such as radiation pattern, directivity and beam forming characteristics. The research leverages MATLAB's Antenna Toolbox to design the array and through a systematic simulation setup investigates the radiation pattern, optimizes directivity and assesses beam forming performance. The findings contribute valuable insights into the design and analysis of antenna arrays underscoring the significance of MATLAB as a robust tool for antenna system optimization. © 2024 IEEE.

关键词： MATLAB

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Improving Driver Safety: A Deep Transfer Learning Framework Optimized with Jaya Algorithm for Fatigue Detection 3

Improving Driver Safety: A Deep Transfer Learning Framework ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Veluchamy, S. Pasupuleti, Harini Saravanan, S. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Chennai India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

Detecting driver drowsiness to prevent car accidents is extremely important, leading to the demand for dependable monitoring systems. Recent studies are concentrating on utilizing cutting-edge technologies, particularly in driver-state monitoring, to address the continuous challenge of identifying effective characteristics for detecting drowsiness in visual data. This research provides a novel method for detecting driver drowsiness based on computer vision. The proposed system initiates by isolating the facial area in input images and extracting deep features using well-established models such as AlexNet, GoogLeNet, and MobileV2Net. To further enhance the system's accuracy, features from these models are optimally combined using the Jaya optimization technique. The experimental findings emphasize the effectiveness of this framework, achieving a noteworthy accuracy of 96.27%, and showcasing its potential to improve driver safety through better drowsiness detection. © 2024 IEEE.

关键词： Transfer learning

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Energy optimization mechanism using metastable-smart grid (MSSG) 4

Energy optimization mechanism using metastable-smart grid (M...

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4th National Conference on communication Systems, NCOCS 2022

作者： Kannan, S. Pugazhendiran, P. Rishi Keshav, N. Arul Deva, A. Suresh Kumar, K. Ananth Kumar, T. Electrical and Electronics Engineering IFET College of Engineering Tamilnadu India Electronics and Communication Engineering IFET College of Engineering Tamilnadu India Computer Science and Engineering IFET College of Engineering Tamilnadu India

The Smart Grid model uses energy from renewable sources and utilities to manage energy in a way that ensures uninterrupted power supply for users. In order to operate independently from the main power grid, a DC micro grid is a small power system that generates and uses its own DC electricity. Solar panels, fuel cells, and wind turbines are the most typical power sources for DC micro grids, with batteries used to store any extra energy. The independence of the power grid is one feature of a DC micro grid that promotes increased lifetime. Building owners have more latitude to pursue their sustainability goals since they have control over the production, delivery, and consumption of power. Super-twisting provides a dependable method and an effective instrument for the control of uncertain nonlinear systems by addressing the basic flaws of conventional sliding mode control, notably large control effort and chattering. Fractional order controllers provide greater design freedom than conventional integer order controllers. The purpose of this work was to create a metastable-smart grid(MSSG) user end model that could dynamically manage and optimize energy produced by renewable energy sources and the utility to ensure that customers always have access to electricity. Controlling these many power sources using MSSG and delivering energy in accordance with each user's unique power plan is the responsibility of the central control unit of the distribution grid. The supply is only switched to solar by the central control unit if a user's power consumption falls below a predetermined threshold. Giving consumers with less purchasing power more clout as a result. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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Optimized Cascade and Elman Neural Network for CT Image-Based Brain Stroke Classification 1

Optimized Cascade and Elman Neural Network for CT Image-Base...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Papana, Krishna Reddy Nagakishore Bhavanam, S. Acharya Nagarjuna University Department of Electronics and Communication Engineering Andhra Pradesh Guntur India Department of Computer Science and Engineering Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350365337

The brain is an intricate organ that regulates a variety of bodily activities. Brain stroke happens when the artery that supplies blood to the brain is either blocked or breaks. The main objective is to propose image analysis techniques for identifying the brain stroke lesion. Thus, this provides an efficient solution for detecting and classifying stroke lesions present in CT brain images. This paper developed an Optimized Cascade and Elman Neural Network (OCENN) for the classification of brain stroke from the CT images. Initially, the CT images of brain stroke are gathered from online sources. After that, the noise removal of the image is obtained by using a wiener filter. The brain stroke lesions are segmented by using expectation-maximization (EM). After that, the important features are extracted. Finally, the OCENN is used to classify the brain stroke. To justify the performance of the proposed methodology, it will be implemented and performances will be evaluated. For comparison purposes, it will be compared with the conventional techniques with various parameters of accuracy, precision, recall, sensitivity, specificity, F _Measure, and kappa. The result will show that the proposed methodology will achieve efficient outcomes in system performance and enhance the accuracy of 98.60% in brain stroke detection. © 2024 IEEE.

关键词： computerized tomography

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