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Design Analysis of Stroke Risk Prediction Model Employing the Hybrid Structure Implementation of Deep Transfer Learning System

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Journal of The Institution of Engineers (India): Series B 2024年第5期105卷 1197-1206页

作者： Ahammad, Shaik Hasane Pande, Sandeep Dwarkanath Rajesh, V. Inthiyaz, Syed Priya, P. Poorna Krishna, M. Rama Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram522302 India MIT Academy of Engineering Alandi Pune India Department of ECE Dadi Institute of Engineering and Technology Anakapalle Visakhapatnam India Department of ECE Andhra Loyola Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Saveetha Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Saveetha University Tamilnadu Chennai602105 India

Stroke has been given the highest priority among all widespread diseases which effectuate mortality or interminable debility in aging individuals over the globe. The study aims to progress the conventionally developed stroke risk design and predict the risk level based on the dataset collected from various sources concerning weighing factors. Moreover, the study considers employing artificial intelligence attributed to the comparative analysis of risk estimated with the probability of occurring in 10 years. A framework of Deep learning can outperform all the conventional models even with the dependency on big data. Because of the stringent confidentiality safety policy in healthcare systems, the data related to stroke is typically delivered among numerous hospitals in insignificant parts. It is a proven state-of-the-art technology for modeling stroke risk prediction. Nevertheless, the instances of data with positive and negative values undergo life-threatening and imbalanced cases. Therefore, a novel technique of Hybrid Deep Transfer Learning-based Stroke Risk Prediction arrangement for the potential to develop the configuration of multiple associated sources (such as chronic diseases, external stroke data, such as diabetes and hypertension). Extensive investigation has been conducted for the proposed framework in both synthetic and real-world circumstances. Finally, outcomes demonstrate the ability of real-world implementation, including several clinics facilitated with 5G/B5G substructures. © The Institution of Engineers (India) 2024.

关键词： Learning systems

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Deep Learning Analysis for Skin Cancer Detection

Deep Learning Analysis for Skin Cancer Detection

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International Conference on VLSI, Signal Processing, Power electronics, IoT, communication and Embedded Systems, VSPICE-2022

作者： Singh, Chandra Nischitha Shetty, Shailesh S. Bekal, Anush Bhat, Sandeep Badiger, Manjunatha Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Mangaluru India Department of Electronics & Communication Engineering Shree Devi Institute of Technology Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Mangaluru India

ISBN: (纸本)9789819944439

In the present world, skin cancer is the most widely recognized reason for death among people. Skin malignancy is an unusual development of skin cells. Frequently created on the body part exposed to the sunlight;however, it can happen on any place on the body. The majority of the skin malignancy is treatable at the beginning phase. So an early and quick identification of skin disease can spare the patient's life. With the new innovation, early identification of skin malignancy is conceivable at the introductory stage utilizing picture handling. The skin cancer detection using image processing is based on the detection of skin cancer types at its earliest stage. There are many types of skin cancers found. It is difficult to identify the type of skin cancer at the earlier stage, manual identification can often be time consuming and inaccurate. Doctors are able to identify the symptoms of skin cancer but are unable to identify the type of skin cancer in the initial stage. So the doctors will wait until it gets blotted but by that time the disease will become out of control. So a software is developed to help the Skin Cancer Detection at its earliest stage by passing valid input images. So this chapter explains about a method to identify and classify the skin cancer using images using Convolutional Neural Network (CNN) algorithm. The accuracy obtained by using the proposed method is 89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Disease control

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MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1139-1150页

作者： Qian Hu Jiayi Ma Yuan Gao Junjun Jiang Yixuan Yuan Electronic Information School Wuhan UniversityWuhan 430072China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.

关键词： Deep folding iteration

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Enhancing DNS Attack Classification with Convolutional Neural Networks and Gated Recurrent Units 15

Enhancing DNS Attack Classification with Convolutional Neura...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Prasad, Sanjana Sharma, Ishu Arun, S. Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

ISBN: (纸本)9798350370249

DNS attacks can be caused by various methods such as Rebinding attacks, Cryptojacking and Domain Generation Algorithm. Classifying DNS attacks in a timely manner and taking necessary action to mitigate the issue is important. This research study describes the hybrid model of Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) that is used to improve the detection or categorization of DNS attacks. A dataset called CIRA-CIC-DoHBrw-2020 is used to train the suggested hybrid mode. According to experimental data, the LSTM prototype outperformed CNN with respect to identifying or categorizing DNS attacks. The CNN-LSTM and CNN-GRU hybrid models have the ability to improve the model's accuracy. CNN was used for feature extraction and LSTM for temporal modeling in the CNN-LSTM hybrid model. The suggested CNN-GRU hybrid model, on the other hand, obtains the greatest accuracy of 95.16%, demonstrating the efficacy of integrating GRU with CNN to effectively recognize or categorize both temporal and geographical characteristics of DNS network data. The CNN-GRU model's enhanced performance demonstrates how well merging different neural network architectures—like CNNs and GRUs—may enhance convergence and lower error rates. © 2024 IEEE.

关键词： Convolutional Neural Networks DNS Security Gated Recurrent Units Infrastructure Security Network Security Network Traffic Classification

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems Science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Optimized Deep Learning Classification Model for Intelligent Edge devices

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Journal of engineering Science and Technology Review 2024年第3期17卷 88-94页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Electronics and Communication Engineering REVA University Karnataka Bangalore560064 India

Deep learning models enable state-of-the-art accuracy in computer vision applications. However, the deeper, computationally expensive, and densely connected architecture of deep neural networks (DNN) have limitations for deploying the model on resource-constraint embedded IoT devices. We propose an efficient neural network compression framework that performs filter pruning, fine-tuning and 8-bit quantization to reduce computational complexity, inference time, and memory footprint. Furthermore, reducing the bit widths of activation and weights helps design a compact deployment model on resource-limited IoT devices such as smartphones. The proposed system is evaluated extensively on the CIFAR-10 dataset for Resnet34 and VGG16 models. In addition, we examine the efficacy of a larger model. The result shows that pruning followed by quantization compresses the neural network and compared to the baseline model, achieved an accuracy of 78.01% for Resnet34 and 82.34% for Vgg16 after pruning and quantization which is © 2024 School of Science, DUTH. All rights reserved.

关键词： Object recognition

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Integrating the Internet of Things to Protect Electric Vehicle Control Systems from Cyber Attacks

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 433-440页

作者： Bhukya, Baddu Naik Venkataiah, V. Kuchibhatla, S. Mani Koteswari, S. Kumari, R.V.S. Lakshmi Raju, Yallapragada Ravi Department of Electrical and Electronics Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Computer Science Engineering CMR College of Engineering & Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering ACE Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Kakinada India Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam India Department of Computer Science and Technology Madanapalle Institute of Technology and Science Andhra Pradesh India

The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based on communication infrastructure, provides real-time cyber-physical device monitoring, control, and sensing, such as electric autos. Traditional communication infrastructure's vulnerability to cyberattacks hampered IoT's capacity to investigate these potential applications. This study proposes an algorithm for monitoring and controlling electric vehicles through the Internet of Things communication network to prevent fake data injection attacks. A state-space model depicts a fully autonomous electric car equipped with a vision system. Intelligent sensors and actuators from the Internet of Things are utilized to monitor and change system conditions, compensating for the large distance between the electric car and the control center. Sensing data is delivered from the vehicle to the central command center via an insecure, attack-prone communication path. The mean square error approach is used to derive states in the most effective state estimation system for comprehending and showing autos. A semi-definite programming approach is used to create an optimal control algorithm to manage the vehicle states. The simulation results show how precisely and successfully the proposed algorithms can foresee and regulate a vehicle's state. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Internet of things

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A Multicriteria Aware Multipath Routing Method to Increase the QoS for Future 6G Networks 13

A Multicriteria Aware Multipath Routing Method to Increase t...

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13th IEEE International Conference on communication Systems and Network Technologies, CSNT 2024

作者： Tilwari, Valmik Sharma, Debashree Solanki, Sourabh Chakraborty, Nilotpal Maduranga, M.W.P. Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India National Institute of Technology Department of Electronics & Communication Engineering Warangal India Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798350305463

The optimal solution of creating a communications network in isolated places is still a challenging task. Establishing end-to-end connectivity via mobile ad-hoc networks (MANETs) is a better solution for the above issue. However, the limitation of the mobility of nodes and battery resources between the overlay and mobile ad-hoc network protocols is still a challenge for trustable wireless connections. Therefore, this paper presented a routing scheme named Mobility and Energy consumption aware multipath routing (MEMR) in mobile ad-hoc networks. The proposed routing scheme provides reliable communication with stable route and high lifetime routes to support efficient data transmission in the network. For this purpose, it uses a multicriteria decision-making approach to select the intermediate nodes with the highest energy and lowest mobility for selecting the optimal route. The obtained results indicate that the proposed routing method can significantly improve the overall network performance and deliver packets with acceptable throughput. At the same time, it reduces the energy consumption of the nodes compared with the conventional MP-OLSRv2 routing protocol. © 2024 IEEE.

关键词： Energy utilization

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Comparative Evaluation of Marker-Controlled Method and Gradient Distance Transformation through Watershed Segmentation 9

Comparative Evaluation of Marker-Controlled Method and Gradi...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Choudhary, Nilam Rathore, Pramod Singh Kumar, Dinesh Swami Keshvanand Institute of Technology Management & Gramothan Department of Computer Science & Engineering Jaipur India Manipal University Jaipur Department of Computer And Communication Engineering Jaipur India Swami Keshvanand Institute of Technology Management & Gramothan Department of Electronics And Communication Engineering Jaipur India

ISBN: (纸本)9798350394474

Image segmentation is a fundamental process in computer vision and image analysis, with applications in various fields. By dividing digital images into distinct regions or objects based on their characteristics, such as color, intensity, or texture, image segmentation allows for more efficient object recognition, accurate medical image analysis, and improved computer vision algorithms. This paper aims to provide a comprehensive examination of one popular method for image segmentation, the watershed transformation, and discuss its limitations and potential remedies. By understanding the challenges and possible solutions in digital image segmentation, researchers and practitioners can enhance the accuracy and effectiveness of their image analysis techniques. © 2024 IEEE.

关键词： Image segmentation

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