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Neural Style Transfer Using Convolution Architectures 3rd

Neural Style Transfer Using Convolution Architectures

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Afraa, Shaik Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

Neural style transfer is the concept of blending image's content with a different image's style. This technique helps us generate images that are the same as the base image but with the style of the image. Due to this technique's capacity to produce eye-catching and beautiful visuals, it has experienced substantial growth in popularity. NST, which enables the fusion of content from one image with the creative style of another, has become a fascinating approach in the fields of computer vision and artistic expression. This paper aims to examine how different neural network models and optimization techniques affect the effectiveness and attractiveness of neural style transfer. We specifically compare Adam, Adamax, SGD, RMS Prop, AdamW, Adadelta, Adagrad, and Nadam optimizers with VGG19, ResNet50, ResNet152V2, and Xception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Plant Leaf Disease Identification based on Machine and Deep Learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 IEEE.

关键词： Deep learning

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Improving the Autonomous Vehicle vision with Synthetic Data using Gen AI 3

Improving the Autonomous Vehicle Vision with Synthetic Data ...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kadiyapu, Durga Chandra Mouli Mangali, Venkata Sai Thummuru, Jaideep Reddy Sattula, Sriya Annapureddy, Sasivardhan Reddy Arunkumar, M.S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

This research focuses on addressing the challenges associated with training object detection and segmentation models for autonomous vehicles using real-world data, which is often difficult to collect, limited in diversity, and expensive to annotate. To overcome these limitations, we propose a novel framework for generating synthetic datasets using deep learning techniques and generative AI. Specifically, we utilize the Stable Diffusion models to generate synthetic images, and annotate them using a combination of the Segment Anything Model (SAM) and the CVAT tool. The annotations are further refined to produce label files, and the dataset is split into training and validation sets. For evaluation, the YOLOv8 is applied for object detection and segmentation tasks using the synthetic dataset. Additionally, the Salesforce/Blip image-captioning model is integrated to provide descriptive captions for the detected objects. Experimental results demonstrate that synthetic datasets generated by this approach not only provide coverage of complex and rare scenarios but also achieve comparable or superior performance compared to real-world datasets in object detection and segmentation tasks. These findings underscore the potential of synthetic data to serve as a cost-effective and scalable alternative to real-world data for autonomous vehicle training. © 2025 IEEE.

关键词： Adversarial machine learning

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Drones in Defense: Real-Time vision-Based Military Target Surveillance and Tracking 3

Drones in Defense: Real-Time Vision-Based Military Target Su...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chakrabarty, Sudip Chatterjee, Rajdeep Chakraborty, Sorup Roy Shuvo, Sourov Chowdhury, Rajesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India KIIT Deemed to be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331523893

In today's world, with ongoing conflicts and increasing security challenges, the use of drones in defense has become critical for real-time surveillance, precise targeting, and ensuring personnel safety. This study introduces a novel dataset of drone-captured videos tailored for military surveillance, where carefully selected frames were used to optimize training and testing. We evaluated three YOLO deep learning models - YOLOv5, YOLOv8, and YOLOv11 - and their nano and small variants to achieve a balance between detection accuracy and computational efficiency. The top-performing model, YOLOv11s, was further integrated with advanced tracking algorithms, including Byte-Track, BoT-SORT, and DeepSORT, to identify the most reliable combination for real-time tracking. Results show that YOLOv11s, paired with ByteTrack, delivers superior accuracy and efficiency in detecting and tracking enemy assets, enabling autonomous monitoring and engagement with minimal human intervention. This work demonstrates significant advancements in autonomous military surveillance. © 2025 IEEE.

关键词： Drone KIIT-MiTA Military Object Detection Tracking YOLOv11

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Transfer Learning based Deep Learning Model for the Identification of Facial Expression 3

Transfer Learning based Deep Learning Model for the Identifi...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ekambaram, Gothai Rakkiannan, Thamilselvan Natesan, P. Jayakkavin, E. Dept of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Dept Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Dept of Artificial Intelligence Kongu Engineering College Tamilnadu Perundurai India Kongu Engineering College Tamilnadu Perundurai India

ISBN: (纸本)9798331527549

The changes that occur on a person's face to communicate their internal emotional states, intentions, or social cues are known as facial expressions. The human face is a crucial tool for communicating with other sand determining their emotional states from their facial expressions. Because it is essential in human interaction, real-time facial expression analysis has become a research topic. Face reading is used in various areas, such as social interaction, emotional intelligence, computer vision, biometric security, and social intelligence. This work focuses on an overview of the various facial expression recognition methods and their advantages and disadvantages. The body's natural response to internal and external stimuli is emotional. Whether it's a common emotion, stress, anger, etc., it can cause serious harm to health. Both psychological and physiological manifestations of these responses emotions and facial expressions appear. The model is used to analyse real-time snapshots of the user's face and detect the existence of stressful information as well as convolutional neural network capable of recognizing emotions, and facial emotions, a proof of concept for the emotion detector was developed. The classification model was developed via transfer learning and fine-tuning. For deep learning process, two classifier designs were taken into account. Following a numberoftests, it was shown that a classifier built using a Separable Conv2D convolutional layer and LeakyReLU as activation method CNN performed the best in categorising emotions. The results are encouraging, and the proposed emotion detection system is minimallyintrusive as it only needs a webcam to monitor the user's facial expressions. © 2025 IEEE.

关键词： Contrastive Learning

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An Artificial Intelligence Based Attendance Monitoring System for Malpractice control 3rd

An Artificial Intelligence Based Attendance Monitoring Syste...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Gadey, Nishanth Pande, Sagar Dhanraj School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravati India Pune India

ISBN: (纸本)9789819743582

In all organizations, attendance monitoring is essential for monitoring student performance, and checking whether every student is present or not is not easy. Each organization takes attendance manually like calling with their registration numbers or student names and noting it in attendance records given by the school/college principal as a proof of attendance. While some consider student signature. These are the methodologies which are old, complex, and time consuming for students and teachers. This study explores how AI simplifies attendance monitoring. Several methods of attendance tracking are discussed in this study, including face detection, which may be used to identify students continually and regularly. Numerous additional approaches are also mentioned. The approaches used for Facial Biometric Framework attendance are covered in this study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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Application of computer vision Technology in User Interface Design 3rd

Application of Computer Vision Technology in User Interface ...

引用

3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Su, Jianna Department of Mathematics and Computer Engineering Ordos Institute of Applied Technology Inner Mongolia Erdos017010 China

ISBN: (纸本)9789819791231

This article explores the application of computer vision technology in user interface design, particularly through gesture recognition to enhance human–computer interaction. Gestures offer natural and versatile advantages in the process of human–computer interaction. vision-based gesture recognition technology has broad application potential in virtual environments, sign language recognition, machine control, and other fields. In the task of gesture recognition in user interface design, the recognition results are affected by some irrelevant factors, such as dynamic backgrounds and close-fitting clothing. The attention mechanism is a good way to address the above problems, as it applies weights to key areas to extract essential features, thereby improving the model's performance. Several commonly used attention mechanisms, such as spatial attention, channel attention, and mixed attention mechanisms, can only apply attention to a single-frame image, focusing on specific regions. However, gestures consist of sequences of multiple-frame images, and the recognition results are collectively influenced by multiple-frame gesture images. The aforementioned attention mechanisms cannot meet the requirements effectively. To address this issue, a joint intra-frame and inter-frame attention module has been investigated. The model is able to prevent the impact of irrelevant elements during the gesture identification process because of this module's ability to apply attention concurrently across many consecutive frames and inside a single frame. In order to further user interface design, this study employs convolutional neural networks to include gesture recognition. These results shed light on how computer vision technology may revolutionize UI design and enhance user experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Evaluation of Hybrid Encryption Method to Secure Healthcare Data 3rd

Evaluation of Hybrid Encryption Method to Secure Healthcare ...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Das, Sangjukta Singh, Maheshwari Prasad Namasudra, Suyel Department of Computer Science and Engineering National Institute of Technology Patna Bihar India Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819743582

Technological advancements have brought a new era of growth for the healthcare industry. Nowadays, the security of healthcare data and the preservation of user privacy inside smart healthcare systems are being severely compromised by cyber attackers. This work introduces an encryption method that makes use of a hybrid encryption approach by combining both asymmetric and symmetric encryption methods to improve healthcare data security. This scheme solves the key escrow problem and its effectiveness is analyzed through performance evaluations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Defensive Strategies Against CAN-Bus Infiltrations 3

Defensive Strategies Against CAN-Bus Infiltrations

引用

3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Sharma, Pratham Seema Rana, Prashant Singh Singh, Mukesh Kumar, Amit Anand, Ashima Thapar Inst. of Engg. And Technology Department of Computer Science Punjab Patiala India Thapar Inst. of Engg. And Technology Department of E & I Engg. Punjab Patiala India

ISBN: (纸本)9798331533205

The computerization of vehicles rapidly accelerated, and as vehicle networks link to external networks, automotive security becomes a critical issue. The controller Area Network (CAN) bus is the most widely used internal control network in automobiles, facing growing vulnerabilities. With the development of networked and self-driving technology, the sealing nature of automotive internal control networks is eroding, leaving the security standards inadequate by modern benchmarks. This paper aims to provide a brief overview of the defence strategies against CAN Bus infiltrations, identify current limitations to these existing strategies, and discuss the challenges of enhancing the security in automotive embedded systems. © 2025 IEEE.

关键词： Proportional control systems

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Empirical Evaluation of Real-Time Diabetic Retinopathy Disease Detection using IoT Enabled Medical Imaging Processing Strategy 3

Empirical Evaluation of Real-Time Diabetic Retinopathy Disea...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shifani, S. Agnes Saraswathy, N. Margaret Clemencia, A. Santhi, G.B. Giri, Jayant Al-Mousa, Mohammad Rasmi Saveetha University Saveetha School of Engineering Department of Ece Chennai India Department of Chemistry Thanjavur India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

Diabetic Retinopathy (DR) poses a significant risk to vision among diabetic patients, necessitating early and accurate detection for effective intervention. This research introduced a novel methodology to detect the diabetic retinopathy in an efficient way. In contrast to the existing technique called Convolutional Neural Network (CNN), the proposed approach called Hybrid Learning assisted Diabetic Retinopathy Predictor (HLDRP) allows for easy and quick implementation and offers patients direct access to diagnostic equipment. By leveraging a hybrid learning model that incorporates both supervised and unsupervised learning techniques, HLDRP enhances the accuracy and reliability of DR diagnosis. The IoT component facilitates continuous monitoring and data acquisition from patients, while the hybrid learning model analyzes this data to detect early signs of DR and predict disease progression. Experimental results indicate that HLDRP offers superior diagnostic performance compared to traditional methods, with increased sensitivity and specificity. This integrated approach not only improves early detection but also enables personalized treatment planning, potentially transforming the management of diabetic retinopathy through innovative technology and advanced analytics. Through comprehensive evaluation using a diverse dataset of retinal images, HLDRP demonstrates significant improvements in diagnostic performance, including increased sensitivity and specificity compared to traditional detection methods. The results underscore HLDRP's potential to facilitate early intervention and personalized treatment, providing a valuable tool for healthcare professionals in managing and preventing diabetic retinopathy. © 2025 IEEE.

关键词： Convolutional neural networks

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