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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua science and technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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Intent and URL based ML Classification for Phishing Detection

Intent and URL based ML Classification for Phishing Detectio...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Uloli, Thiruvaazhi Sadasivam, Sudha Kumaraguru College of Technology Department of Computer Science & Engineering Coimbatore India PSG College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350378177

Reports show that the number of phishing web sites is exponentially increasing and it is estimated that between 80% to 93 % of the data breaches are involving phishing attacks. With both probability of occurrence as well as impact high, it is a high risk calling for an effective solution. Linear approaches like multi-factor authentication currently practiced addressing an exponentially growing problem is inadequate. In this work, we present a solution using domain-based understanding of the attacker intent based on the attributes of information that the attacker ultimately seeks to know from the user and make it more effective by combining it with machine learning based solutions of phishing detection based on URL features. © 2024 IEEE.

关键词： Phishing

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Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest

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Multimedia Tools and Applications 2024年 1-26页

作者： Yarrarapu, Manasa Leelavathy, Narkedamilly Haritha, Dasari Department of Computer Science and Engineering Jawaharlal Nehru Technological University Andhra Pradesh Kakinada533003 India Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajahmundry533296 India Department of Computer Science and Engineering University College of Engineering Jawaharlal Nehru Technological University Andhra Pradesh Kakinada533003 India

Now-a-days, the generation of videos has increased dramatically due to the quick growth of multimedia and the internet. The need for effective ways to store, manage, and index the massive numbers of videos has become imperative due to this expansion. As a result, a method needs to be proposed that collects only the necessary data from the original recording. In computer vision, Video summarization is a significant task, and its primary goal is to give a quick summary of the video by removing irrelevant information and capturing key frames from the video. Many approaches have developed over the last several decades, using the most recent deep neural network architectures that represent the current state-of-the-art. Our method involves extracting vital key frames from the input video using the MobileNetSSD model, which is well-known for its efficient recognition and localization of objects of interest. These highlighted frames are essential in creating a detailed video summary. Furthermore, a method of temporal analysis is applied to guarantee that the summary accurately reflects the relevant events in the order in which they occurred, contributing to a coherent and meaningful representation of the information. We evaluated the proposed approach on TV Sum and SUM me video datasets, comparing the results against cutting-edge video summarization techniques. Our approach works effectively to produce clear and meaningful video summaries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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U-YOLOv3: A Model Focused on Underwater Object Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 87-102页

作者： Sarkar, Pratima De, Sourav Gurung, Sandeep Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Majhitar India Sikkim Manipal University Gangtok India Department of Computer Science and Engineering Techno International New Town Newtown Kolkata India Department of Computer Science and Engineering Cooch Behar Government Engineering College Vill-Harinchowrah West Bengal Cooch Behar736170 India

Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b

关键词： Underwater imaging

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Real-Time Object Detection on Edge Devices Using Mobile Neural Networks 2

Real-Time Object Detection on Edge Devices Using Mobile Neur...

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

作者： Jose Reena, K. Nigam, Charul Kirubasri, G. Jayachitra, S. Aeron, Anurag Suganthi, D. Vistas Department of Computer Science Tamilnadu Chennai India Institute of Information Technology and Management Department of It New Delhi India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India Dit University Dehradun School of Computing Department of Computer Science and Engineering Dehradun India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350306415

In an era when edge computing rapidly evolves, this study addresses the significant difficulty of real-time object identification on resource-constrained edge devices. We provide a unique neural network model for edge scenarios in this paper. Our model covers object recognition system accuracy, computational efficiency, and speed. Object detection methods that need processing are impractical for edge computing. This paper proposes a Mobile Neural Network tailored to the limitations. Pruning, which decreases model size by 30%, and quantization make the model efficient. On several edge devices, the model had an average accuracy of 92% on Dataset 1 and 89% on Dataset 2. The model's 40-millisecond inference time and 2.3-watt power consumption were impressive. It outperformed standard CNN models and edge-optimized algorithms like YOLOv3 and SSD MobileNet under identical conditions. This study shows that the proposed paradigm can revolutionise edge computing real-time object detection. The model is suitable for high-responsiveness, energy-efficient applications. © 2024 IEEE.

关键词： Edge Computing Mobile Neural Networks Optimization Techniques Real-Time Object Detection

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Soil Image Classification Using Transfer Learning Approach: MobileNetV2 with CNN

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SN computer science 2024年第1期5卷 199页

作者： Banoth, Ravi Kumar Murthy, B. V. Ramana Department of Computer Science and Engineering University College of Engineering Osmania University Telangana Hyderabad India Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Abids Telangana Hyderabad India

This paper presents a novel study on soil image classification, leveraging the synergistic potential of transfer learning and convolutional neural networks (CNNs). The proposed approach combines the strengths of the MobileNetV2 architecture with a customized CNN model for accurate and efficient soil type recognition. The pre-trained MobileNetV2 is used to capture generic features before fine-tuning it with a dedicated soil image dataset comprising four distinct classes: red, clay, black, and yellow soils. To enhance the model’s capacity for discerning intricate soil textures, a specially designed CNN architecture is incorporated. The model’s performance is rigorously evaluated on a dataset of 108 images, each sized at 256 × 256 pixels, achieving an exceptional accuracy rate of 100% on the test dataset. The promising results demonstrate the efficacy of the proposed methodology in soil image classification tasks, offering potential applications in precision agriculture, environmental monitoring, and land management. While these findings showcase remarkable accuracy, further investigations are recommended to assess the model’s generalization across diverse environmental conditions and an expanded range of soil image datasets. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Black soil Clay soil Convolutional neural networks (CNNs) MobileNetV2 Red soil Soil image classification Yellow soil

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Analysis of Watermarked Video Optimization and Training Based on Classification Using Deep Learning Techniques

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SN computer science 2024年第2期5卷 220页

作者： Muthulakshmi, K. Valarmathi, K. Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

The exchange of multimedia data is secured using watermarking. Typically, embedding a watermark involves optimising scheme parameters, frequently by using meta-heuristic optimization methods. Despite the fact that DNN have achieved significant advancements in field of multimedia representation, training neural methods takes a lot of time and data. This research propose novel method in analysing watermarked video optimization and training based on DL. Here input is collected as watermarked input video and optimize and train the watermarked input video data with enhanced security using swarm optimization-based clustering fuzzy greedy trust analysis. then the optimized video has been trained using variational temporal-based u-net convolutional encoder neural networks (VTU-net ENN). Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for various watermarked dataset. Accuracy attained by proposed technique is 92%, precision attained by proposed technique is 75%, recall attained is 62%, F-1 score attained 55%, RMSE obtained is 53%, NSE attained is 48% by proposed technique. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Optimization and training Securing multimedia Watermarked video

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Skin Cancer Detection Using Deep Learning 10

Skin Cancer Detection Using Deep Learning

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mahalakshmi, A. Padmavathy, C. Priya, N. Priyanka, G. Babu, Lidiya Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353778

Skin cancer is a significant global health concern that requires early detection and accurate diagnosis for effective treatment. Traditionally, dermatologists with specialized training have been responsible for diagnosing skin cancer. However, the emergence of deep learning models, particularly Convolutional Neural Networks (CNNs), offers a promising approach for utilizing dermatoscopic images in the early identification and categorization of skin cancer. The HAM10000 dataset, comprising a vast library of high-quality dermatoscopic images displaying a variety of skin lesions, significantly contributes to advancing skin cancer diagnosis. This research leverages the HAM10000 dataset to develop and evaluate a CNN model tailored for accurate skin cancer classification. The suggested CNN model is an advanced deep learning architecture adept at image classification tasks, particularly in recognizing various forms of skin cancer. It consists of multiple layers of dense neural networks, pooling, and convolution designed to extract detailed characteristics from skin lesion images. To ensure comprehensive representation of various skin lesions and maximize performance, the training dataset is extensively oversampled. This oversampling technique enhances the model's ability to generalize across different lesion types, thereby improving classification accuracy. Furthermore, the Adam optimizer refines the model's learning process by effectively adjusting its parameters during training, leading to increased accuracy. By training the model for more than one hundred epochs with a batch size of 323, it learns intricate patterns and distinguishing features within skin lesion photos, which enhances its ability to classify skin cancer accurately. These advancements in deep learning-based skin cancer categorization represent a significant step towards leveraging artificial intelligence to improve early diagnosis and detection. Such innovations have the potential to support medical profe

关键词： Convolutional neural networks

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A Detailed Review of Current AI Solutions for Enhancing Security in Internet of Things Applications

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computers, Materials & Continua 2025年第6期83卷 3713-3752页

作者： Arshiya Sajid Ansari Ghadir Altuwaijri Fahad Alodhyani Moulay Ibrahim El-Khalil Ghembaza Shahabas Manakunnath Devasam Paramb Mohammad Sajid Mohammadi Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim56447Saudi Arabia

IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication,processing,and real-time monitoring across diverse *** to their heterogeneous nature and constrained resources,as well as the growing trend of using smart gadgets,there are privacy and security issues that are not adequately managed by conventional *** review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT *** intersection of AI technologies,including ML,and blockchain,with IoT privacy and security is systematically examined,focusing on their efficacy in addressing core security *** methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in *** reviewed solutions are categorized based on their ability to tackle specific security *** review highlights key advancements,evaluates their practical applications,and identifies prevailing research gaps and *** findings indicate that AI solutions,particularly those leveraging ML and blockchain,offerpromising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data *** paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.

关键词： Security in IoT applications privacy-preserving blockchain AI-driven security mechanisms

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A Hybrid Deep Learning Approach for Automatic Melanoma Diagnosis: Integrating Convolutional Neural Networks with Vision Transformers 4

A Hybrid Deep Learning Approach for Automatic Melanoma Diagn...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Priyanka, G. Mahalakshmi, A. Priya, N. Sivaranjani, N. Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan college of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The most lethal type of skin lesion is melanoma. The likelihood of survival for melanoma is significantly increased by early detection. Nevertheless, a number of characteristics, such as diminished contrast between the skin and abrasion and visual similarities between non-melanoma and melanoma, make it challenging to identify melanoma with accuracy. The novel melanoma recognition method described in this study consists of three primary phases: preprocessing, segmentation and classification. To maximize image quality and get the data ready for segmentation, the preprocessing step included denoising techniques, grayscale conversion, Gaussian thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE). Three widely used edge detection algorithms-Canny, Sobel, and Prewitt-were applied to the segmentation process in order to identify lesion boundaries and extract features unique to melanoma. Performance criteria such as Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) were used to assess the accuracy of the segmentation. Based on the comparison analysis, Sobel was found to be the best algorithm for correctly identifying melanoma lesions. The outcomes showed how well the preprocessing methods worked to enhance image quality and how well the segmentation algorithms identified melanoma lesions. This research's subsequent phase will concentrate on classifying images using deep learning models. The segmented images are used to train deep learning model namely Convolutional Neural Networks with Vision Transformer. Superior performance is obtained in terms of accuracy, precision, recall, and F1 score when denoising, segmentation, and classification techniques are combined. This highlights the potential of deep learning and image processing fusion in improving skin disease recognition and performance of classification systems. © 2024 IEEE.

关键词： Dermatology

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