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5th International conference on Data Science, machine learning and applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. The rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional neural networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning image processing Backpropagation algorithm Convolutional neural networks (CNNs) Model structures

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Medical image Segmentation Techniques: Advances and Challenges 6

Medical Image Segmentation Techniques: Advances and Challeng...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Boulehmi, Hela Filali, Rania Linda DGET ISET de Bizerte Bizerte Tunisia

ISBN: (纸本)9798331506520

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging. © 2025 IEEE.

关键词： Convolutional neural networks

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Leaf Disease Detection and Correction 5th

Leaf Disease Detection and Correction

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5th International conference on Data Science, machine learning and applications

作者： Indrani, P. Regulwar, Ganesh B. Ahmed, Md Sohel Reddy, K. Sneha Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Pests and plant conditions provide an essential part when assessing the yield as well as the quality of plants. Nowadays plant disease identification is carried out by the deep learning and image processing. Agriculture is the backbone of Indian government. We need to increase the yield in the crop as the population is increasing so the urbanization increases which cause in decrease of cultivation land. Because of the diseases that we observe frequently in plants, the crop production is being decreased. To reduce the disease in plant we need to use some pesticides which also helps to increase in production. In the recent times, deep learning is very useful in the field of image processing. How to exercise deep learning technology to study factory conditions and pests identification has come a exploration conclusion of great company to experimenters. Now we use image processing and find out the disease in plant and also provide the required pesticide suggestions which makes detection of solution for a disease easier and helps in good yield of crop for farmers.

关键词： Leaf disease detection Plant disease detection deep learning image processing convolutional neural networks TensorFlow Keras agricultural technology crop yield pesticide recommendations preprocessing model construction training testing performance evaluation

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A concept-aware explainability method for convolutional neural networks

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machine VISION AND applications 2025年第2期36卷 1-17页

作者： Gurkan, Mustafa Kagan Arica, Nafiz Vural, Fatos T. Yarman Bahcesehir Univ TR-34353 Istanbul Turkiye Piri Reis Univ TR-34940 Istanbul Turkiye Middle East Tech Univ Geol Engn Dept TR-06800 Ankara Turkiye

Although Convolutional neural networks (CNN) outperform the classical models in a wide range of machine Vision applications, their restricted interpretability and their lack of comprehensibility in reasoning, generate many problems such as security, reliability, and safety. Consequently, there is a growing need for research to improve explainability and address their limitations. In this paper, we propose a concept-based method, called Concept-Aware Explainability (CAE) to provide a verbal explanation for the predictions of pre-trained CNN models. A new measure, called detection score mean, is introduced to quantify the relationship between the filters of the model and a set of pre-defined concepts. Based on the detection score mean values, we define sorted lists of Concept-Aware Filters (CAF) and Filter-Activating Concepts (FAC). These lists are used to generate explainability reports, where we can explain, analyze, and compare models in terms of the concepts embedded in the image. The proposed explainability method is compared to the state-of-the-art methods to explain Resnet18 and VGG16 models, pre-trained on imageNet and Places365-Standard datasets. Two popular metrics, namely, the number of unique detectors and the number of detecting filters, are used to make a quantitative comparison. Superior performances are observed for the suggested CAE, when compared to Network Dissection (NetDis) (Bau et al., in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2017), Net2Vec (Fong and Vedaldi, in: Paper presented at IEEE conference on computer vision and pattern recognition (CVPR), 2018), and CLIP-Dissect (CLIP-Dis) (Oikarinen and Weng, in: The 11th international conference on learning representations (ICLR), 2023) methods.

关键词： Convolutional neural networks Concept-based explanation Filter-concept association Model comparison via explanations

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

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5th International conference on Data Science, machine learning and applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption Computer Vision Natural Language processing Attention Mechanism Performance

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Development of Enhance-Net Deep learning Approach for Performance Boosting on Medical images 5th

Development of Enhance-Net Deep Learning Approach for Perfor...

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5th International conference on Data Science, machine learning and applications

作者： Manoharan, Geetha Solanke, D. R. Acharjee, Purnendu Bikash Nayak, Chinmaya Kumar Sharma, Mukesh Kumar Sahu, Dillip Narayan SR Univ Sch Business Warangal Telangana India SGB Amravati Univ Dept Appl Elect Amravati 444602 Maharashtra India CHRIST Univ Pune Maharashtra India Sri Sri Univ Fac Emerging Technol Cuttack Odisha India Rungta Coll Pharmaceut Sci & Res Bhilai 490024 Chhattisgarh India Gangadhar Meher Univ Dept MCA Sambalpur Odisha India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. The A rapidly expanding area of study is the Deep learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. The musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.

关键词： Deep learning Medical image Enhance- net Dataset

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SVM-based malware classification through deep learning method 5

SVM-based malware classification through deep learning metho...

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5th International conference on Signal processing and Computer Science, SPCS 2024

作者： Shi, Jiawei Wang, Wancheng Zhang, Meng Yao, Qinbo Cheng, Shiyuan Zhan, Zhifei College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Various daily applications, including image categorization, natural language comprehension, and voice identification, heavily rely on fully connected and convolutional neural networks. When tackling classification problems, conventional deep learning architectures typically employ softmax activation functions to normalize outputs and minimize the model's cross-entropy loss. In this study, we present a novel malware classification model that integrates hybrid support vector machines with neural networks. Notably, this model substitutes the conventional softmax layer in neural networks with a support vector machine, thereby shifting the learning process towards minimizing margin losses instead of cross-entropy losses. This modification enhances the precision of malware classification for both standard machine learning algorithms and several prevalent deep learning models. To validate our proposed model, we employed the Malimg dataset, comprising malware images derived from binary malware samples. Subsequently, we trained an DL-SVM model to assign scores to each distinct malware family. Experimental outcomes revealed that the malware classification model achieved an accuracy of 96.31%, a precision rate of 97.00%, a recall rate of 97.35%, and an F1 score of 97.16%, respectively. © 2025 SPIE.

关键词： Convolutional neural networks

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Hand Gesture Recognition for Smart Television Using GRU 5th

Hand Gesture Recognition for Smart Television Using GRU

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5th International conference on Data Science, machine learning and applications

作者： Suresh, Garugu Yaswanth Lakshmi Sravani, Ravinuthala Gayatri Venkata VIT AP Univ Sch Comp Sci & Engn Inavolu AP Secretariat Amaravati Andhra Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Even with the rapid advancement of technology, using a television still requires a physical remote control. Apart from occasionally losing sight of the television remote control, we also sometimes run out of batteries. The goal is to discover an effective way of controlling television and develop 3D hand gesture-based smart television control using Gated Recurrent Unit (GRU). The results of the research show that hand gesture recognition-based interface technology is capable of performing the majority of smart TV operations. It is a comfortable and delightful experience for consumers. The existing research features static image gesture recognition with predefined models for training in addition to the existing research the proposed model features sample video dataset, Custom design with Gated Recurrent Unit. The suggested model is trained using five hand gestures. The camera positioned on the TV continually records the motions. Each gesture is associated with a certain command. The proposed model has achieved an accuracy of about 94% in recognizing the gestures.

关键词： Smart Television image processing Deep learning Convolutional neural networks Gated Recurrent Unit (GRU)

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Improved image Classification with Manifold neural networks

Improved Image Classification with Manifold Neural Networks

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Deberaldini Netto, Caio F. Wang, Zhiyang Ruiz, Luana Johns Hopkins University Baltimore United States Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States

ISBN: (纸本)9798350368741

Graph neural networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs' message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs' potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks. © 2025 IEEE.

关键词： generalization graph neural networks manifold neural networks variational autoencoders

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Heart Signal processing Using Wavelet Analysis and Deep learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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