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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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6th International conference on communication and computational technologies, ICCCT 2024

6th International conference on communication and computatio...

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6th International conference on communication and computational technologies, ICCCT 2024

ISBN: (纸本)9789819774258

The proceedings contain 39 papers. The special focus in this conference is on communication and computational technologies. The topics include: Conceptual Framework for Risk Mitigation and Monitoring in Software Organizations Based on Artificial Immune System;a Multilevel Home Fire Detection and Alert System Using Internet of Things (IoT);smart Baby Warmer with Integrated Weight Sensing;a Robust Multi-head Self-attention-Based Framework for Melanoma Detection;Domain Knowledge Based Multi-CNN Approach for Dynamic and Personalized Video Summarization;Efficient Information Retrieval: AWS Textract in Action;text Summarization Techniques for Kannada Language;Parkinson’s Detection From Gait Time Series Classification Using LSTM Tuned by Modified RSA Algorithm;human Action Recognition Using Depth Motion images and Deep learning;maximizing Portfolio Returns in Stock Market Using Deep Reinforcement Techniques;Detecting AI Generated Content: A Study of Methods and applications;A Systemic Review of machine learning Approaches for Malicious URL Detection;digital image Forgery Detection Based on Convolutional neural networks;Banana Freshness Classification: A Deep learning Approach with VGG16;greenHarvest: Data-Driven Crop Yield Prediction and Eco-Friendly Fertilizer Guidance for Sustainable Agriculture;real-Time Deep learning Based image Compression Techniques: Review;fog-Cloud Enabled Human Falls Prediction System Using a Hybrid Feature Selection Approach;a 4-Input 8-Bit Comparator with Enhanced Binary Subtraction;multivalued Dependency in Neutrosophic Database System;traffic Sign Recognition Framework Using Zero-Shot learning;machine learning Techniques to Categorize the Sentiment Analysis of Amazon Customer Reviews;alzheimer’s Disease Diagnosis Using machine learning and Deep learning Techniques;sentinel Eyes Violence Detection System;Detection of Alzheimer’s Disease from Brain MRI images Using Convolutional neural Network.

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A Review On Fusion Of Spiking neural networks And Transformersc 6

A Review On Fusion Of Spiking Neural Networks And Transforme...

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

作者： Marsi, Oumaima Ambellouis, Sebastien Mennesson, Jose Meurie, Cyril Fleury, Anthony Tatkeu, Charles Univ. Gustave Eiffel COSYS LEOST IMT Nord Europe Institut Mines-Télécom LilleF-59000 France Univ. Gustave Eiffel COSYS LEOST LilleF-59000 France Univ. Lille Centre for Digital Systems IMT Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798331506520

This paper provides a comprehensive review of the integration of Spiking neural networks (SNNs) and Transformers, combining the energy efficiency of SNNs with the high performance of Transformer architectures. By leveraging the event-driven nature of SNNs and the powerful self-attention mechanism of Transformers, this fusion aims to address the challenges of high energy consumption in deep learning while improving task accuracy, especially for complex datasets. We introduce the core concepts of SNNs and Transformers, reviewing state-of-the-art methods for their combination, including hybrid architectures. The performance of each architecture is presented thanks to both static and neuromorphic datasets, highlighting their advantages and limitations. This review also discusses the challenges of integrating self-attention into spiking architectures and outlines future research directions to further enhance model performance and energy efficiency. © 2025 IEEE.

关键词： Distribution transformers

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Sorghum Plant Counting Using Patch-Wise CNN and Histogram Equalization 6

Sorghum Plant Counting Using Patch-Wise CNN and Histogram Eq...

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

作者： Mossi, Moctar I. Université Abdou Moumouni Laboratoire d'énergétique d'électronique d'électrotechnique et d'Informatique Industrielle Niamey Niger

ISBN: (纸本)9798331506520

Due to the increasing impact of climate change on agriculture, efficient early-stage control and yield prediction are becoming increasingly crucial. While satellite data has demonstrated significant efficiency in certain applications, deep learning solutions for plant phenotyping and UAV-based imaging have emerged as promising alternatives. Early-stage plant counting is a key strategy for yield forecasting and mitigating the risk of insufficient production. Manual crop counting, however, is time-consuming, expensive, error-prone, and labor-intensive. Automated, accurate counting offers a significant reduction in workload. This study proposes an automating plant counting method using aerial images of sorghum crops. A histogram equalization is used at the image level then a patch-wise counting is derived from dot annotations of patches using a complexity-optimized regression-based deep convolutional neural network (DCNN). The proposed approach estimates the number of plants within patches and aggregates these estimates for each image. Our findings demonstrate that this approach outperforms existing image level regression-based solutions on the same dataset, achieving a lower Mean Absolute Error (MAE) and provides similar results to more complex architectures that rely on pointwise localization while requiring fewer parameters. © 2025 IEEE.

关键词： Convolutional neural networks

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Deep learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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A Vision Transformer-Based Framework for Detection and Classification of Soybean Leaf Diseases 8

A Vision Transformer-Based Framework for Detection and Class...

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8th International conference on Data Science and machine learning applications, CDMA 2025

作者： Shahzore, Usama Akbar, Shahzad Shoukat, Ijaz Ali Rehman, Amjad Saba, Tanzila Sikder, Tanjin Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Ajman University Department of Engineering and IT Ajman United Arab Emirates

ISBN: (纸本)9798331539696

Soybean is among most cultivated oil seed crop worldwide. Leaf diseases pose a serious threat to the yield of soybean crops which could lead to huge economic losses. It is essential to diagnose these diseases for effective management and mitigation. Traditional methods involved physical inspection of leaves which often led to errors and inconsistencies. Various deep learning models were developed using Convolutional neural networks but lacked comprehensive feature extraction and attention mechanisms. This research established a methodology for detecting and classifying soybean leaf diseases using pre-trained Vision Transformer. Research was performed using the Auburn Soybean Disease image Dataset, Soybean Diseased Leaf and SoyNet. The datasets were divided into 70% training, 10% validation and 20% testing sets and evaluated using standard performance metrics. For the Auburn Soybean Disease image Dataset, the proposed model achieved an accuracy of 98.7% with precision, recall and F1-score values of 99.4%, 99.2% and 99.3% respectively. On the Soybean Diseased Leaf dataset, the proposed model achieved an accuracy of 98.9% with a 98.6% recall precision of 98.4% and an F1-score of 98.5%. For the SoyNet dataset, despite a larger and more complex dataset, the model achieved an accuracy of 97.4%, with precision, recall and F1-score values of 97.0%, 97.2% and 97.1 % respectively. The training and validation losses of model decreased consistently across epochs, indicating effective learning and classification. The proposed model will help farmers classify soybean leaf diseases with potential applications in precision agriculture and plant health monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

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Convolutional neural Network Segmentation for Satellite imagery Data to Identify Landforms Using U-Net Architecture 6th

Convolutional Neural Network Segmentation for Satellite Imag...

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6th International conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Goswami, Mitul Mohanty, Suneeta Dey, Sainath Mukherjee, Aniruddha Pattnaik, Prasant Kumar School of Computer Engineering KIIT Deemed To Be University Odisha Bhubaneswar India

ISBN: (纸本)9789819780891

This study demonstrates a novel use of the U-Net architecture in the field of semantic segmentation to detect landforms using preprocessed satellite imagery. The study applies the U-Net model for effective feature extraction by using convolutional neural network (CNN) segmentation techniques. Dropout is strategically used for regularization to improve the model’s perseverance, and the Adam optimizer is used for effective training. The study thoroughly assesses the performance of the U-Net architecture utilizing a large sample of preprocessed satellite topographical images. The model excels in semantic segmentation tasks, displaying high-resolution outputs, quick feature extraction, and flexibility to a wide range of applications. The findings highlight the U-Net architecture’s substantial contribution to the advancement of machine learning and image processing technologies. The U-Net approach, which emphasizes pixel-wise categorization and comprehensive segmentation map production, is helpful in practical applications such as autonomous driving, disaster management, and land use planning. This study not only investigates the complexities of U-Net architecture for semantic segmentation but also highlights its real-world applications in image classification, analysis, and landform identification. The study demonstrates the U-Net model’s key significance in influencing the environment of modern technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Leveraging AI for Dyslexia Detection: Exploring Multimodal Approaches with machine learning and Deep learning 4

Leveraging AI for Dyslexia Detection: Exploring Multimodal A...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Priyasri, G.R. Devi, M. Uma Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chengalpattu India

ISBN: (纸本)9798331523923

Developmental dyslexia is a learning disorder that typically begins in early childhood, making it difficult for affected individuals to read, write, and spell, even when they possess average or above-average intelligence. Children with dyslexia often experience negative emotions such as low selfesteem, frustration, and anger, highlighting the importance of early detection and intervention. Various methods have been proposed for detecting dyslexia, including reading and writing tests, eye tracking, facial image analysis, and advanced imaging techniques such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG). However, one key challenge in dyslexia detection is the reliance on small, unbalanced datasets, which can lead to poor generalization in models. This review suggests that deep learning techniques, particularly those utilizing advanced data augmentation methods, could address this issue by enhancing the model's ability to learn from limited data, improving both accuracy and robustness in real-world applications. The review focuses on the use of machine learning (ML) and Deep learning (DL) for the automatic detection of dyslexia, aiming to improve diagnostic accuracy and accessibility. It explores various ML approaches, such as decision trees and support vector machines, as well as DL architectures, including Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs), which analyze text, speech, and eye-tracking patterns. The review provides a comparison of feature extraction methods, classification techniques, and performance metrics, with an emphasis on data enhancement and interpretability. Additionally, it addresses challenges such as dataset variability, ethical concerns, and the practical application of these methods in real-world settings. The goal of this review is to provide an overview of the most current methods for dyslexia detection and propose innovative ways to incorporate ML and DL into personalized learning plans an

关键词： Electroencephalography

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SmartPosture: Real-Time Posture Detection and Feedback System Integrating IoT and machine learning 1

SmartPosture: Real-Time Posture Detection and Feedback Syste...

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1st International conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Rane, Milind Mohole, Shrihari Pahade, Yash Kurandale, Gokul Munjale, Aditya VIT E&TC Pune India

ISBN: (纸本)9798350355611

Traditional posture monitoring methods often rely on physical correction, lacking advanced technologies for real-time detection and feedback, which can result in delayed interventions. This research presents a machine learning framework designed to address this deficiency by giving instantaneous posture detection and feedback. The framework categorizes user posture into 'Good' or 'Bad' classifications, achieving an accuracy rate of 75.52%, by the use of Convolutional neural networks (CNNs) along with TensorFlow. Trained on a 2,000-image Yoga Poses Dataset and fine-tuned over 50 epochs, the model achieved optimal performance. The system integrates IoT and machine learning, providing recommendations for posture -related advice through a medical chatbot powered by techniques of NLP like Langchain and Hugging Face embeddings. Key hardware components include the flexible sensor, LCD display, vibration motor, and ESP8266 Wi-Fi module, which offer real-time alerts and monitoring. The web application, built using Flask and Streamlit, allows users to track posture data and receive corrective prompts. This innovative approach not only enhances posture monitoring but also advances smart healthcare, offering a continuously learning and adaptable solution relevant to real-world scenarios. © 2025 IEEE.

关键词： Liquid crystal displays

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Signup page using Face recognition authentication

Signup page using Face recognition authentication

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional neural networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Study of Convolutional neural networks for Medical image Segmentation 5th

A Comprehensive Study of Convolutional Neural Networks for M...

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5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Nautiyal, Kapish Augustine, Jacob Savita Rengarajan, A. Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

It affords an overview latest, the modern-day inside the field of modern-day clinical picture segmentation, after which highlights the gain of trendy CNNs in it. The paper then describes today's CNNs’ fundamentals, architectures, and education techniques. It similarly affords one-of-a-kind CNN fashions used for clinical image segmentation. It presents a comprehensive analysis of new research addressing modern-day CNNs’ utility for this assignment. In the end, the paper affords steering for destiny research guidelines in scientific photo segmentation, the use of CNNs, and an outlook for the ability of today’s this era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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