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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Khalid, Nafil Elkhiri, Hajar Oumaima, Elyazyd Elfahsi, Nouhaila Zahra, Zaglami Fatima Abdellatif, Kobbane Mohammed v University Ensias Rabat Morocco

ISBN: (纸本)9798350322972

The objective of this study is to develop a weed detection and classification technique that can be utilized by robotic weed control systems. The approach leverages deep learning-based image processing algorithms to classify plants as either crops or weeds based on their features. A categorization strategy is applied to identify weeds, which can grow both inside and outside of crop rows. Data collected from crop image wavelet analysis is initially used to differentiate between crops and weeds, and the farmer receives a phone alert. Afterwards, the farmer provides the robot with instructions to spray the necessary areas. The entire model is executed by a cutting-edge real-time object detection system, YOLOv4 (You Only Look Once). During testing, this model has shown superiority over classifier-based systems in several aspects, such as achieving higher accuracy rates and being able to evaluate the full context of the image for all predictions. For instance, the deep learning-based image processing algorithms used in this technique can differentiate between crops and weeds with higher accuracy compared to traditional classifier-based systems. Additionally, the YOLOv4 object detection system used in this study is capable of analyzing the entire image to make predictions, taking into account the context of each object. This represents a significant improvement over traditional classifier-based systems that often rely on local features to make predictions, which can lead to misclassifications © 2023 IEEE.

关键词： Crops

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MAC-Leonets-A novel optimized hybrid convolutional neural networks for the segmentation and diagnosis of Edema diseases using retinal OCT images

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JOURNAL OF INTELLIGENT & FUZZY systems 2023年第6期44卷 10605-10620页

作者： Sudha, v. Shanmugam, Sathiya Priya Anitha, D. Raja, R. Sona Coll Technol Salem Dept ECE Tamil Nadu India Panimalar Engn Coll Dept ECE Chennai Tamil Nadu India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur Tamilnadu India Muthayammal Engn Coll Autonomous Dept EEE Rasipuram Tamilnadu India

An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients' life quality. But, these are liable to more errors and less performance, which requires further improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using Lion Optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively.

关键词： Machine learning deep learning retinal optical coherance tomography images convolutional layers lion optimization algorithm

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A Comparative Analysis of image Partitioning and Compression Mechanisms 15

A Comparative Analysis of Image Partitioning and Compression...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaudhari, Sampada P. Thakur, Nileshsingh v. Dept. of Computer Science and Engineering Prof Ram Meghe College of Engineering & Management Badnera India Dept. of Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

This paper presents a comprehensive comparative analysis of image partitioning and compression mechanisms, two fundamental techniques in image processing and data compression. image partitioning involves dividing an image into smaller components, while compression aims to reduce the size of digital images while preserving visual quality. The analysis encompasses various aspects of both techniques, including methodologies, algorithms, and performance metrics. For image partitioning, segmentation methods such as block-based, region-based, and hierarchical partitioning are examined, along with their respective advantages and limitations. Additionally, the paper explores compression mechanisms such as transform coding, quantization, entropy coding, and predictive coding, highlighting their roles in achieving efficient compression. Furthermore, the comparative analysis evaluates the tradeoffs between different partitioning and compression techniques in terms of compression ratio, image quality, computational complexity, and applicability across different types of images and applications. various compression standards such as JPEG and PNG are also reviewed to provide insights into real-world implementations. Finally, the paper discusses future research directions and emerging trends in image partitioning and compression, including the integration of machine learning techniques for improved segmentation and compression performance. The findings presented in this comparative analysis can serve as a valuable resource for researchers, practitioners, and developers working in the fields of image processing, data compression, and multimedia systems. © Grenze Scientific Society, 2024.

关键词： image compression

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Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded systems 4th

Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Kulkarni, Uday Chikkamath, Satish Kasigavi, Apoorva K. venkatesh, Smitha K. venkatesh, Siri K. Bhat, Sindhu Meena, S.M. KLE Technological University Karnataka Hubli India

ISBN: (纸本)9789819994410

One of the best methods to lessen the chance of suffering brain injuries in a two-wheeler collision is to wear a helmet, but many riders choose not to do so for a variety of reasons, including comfort, convenience, or ignorance. By automatically detecting and identifying riders who are not wearing helmets, automatic helmet detection tools can assist in overcoming these difficulties. These tools may result in fines or other consequences that may serve as an incentive for riders to wear helmets. Thousands of images and video frames can be quickly scanned and analyzed using powerful image processing algorithms employed in automatic detection systems, yielding consistent, accurate results without the possibility of human mistake. The YOLO-v5 object detection technique utilizes deep learning to detect and identify objects in a frame of an image or video. With the help of this model riders without helmet can be detected, and their license plate can be extracted. Optical character recognition (OCR) technique called Paddle OCR can precisely extract and identify text from an image or video frame. With the aid of OCR, the license plates can be recognized and can be stored. The proposed tool accurately detects the license plate of riders without helmets by bringing together these two potent models. Both the YOLO-v5 model and Paddle OCR model were trained using custom datasets with 1000 images each and obtained accuracy by 91%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Disease Detection in Potato Crop Using Deep Learning 6th

Disease Detection in Potato Crop Using Deep Learning

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6th International Conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Subba Rao, S.P.v. Ramaswamy, T. Tirukkovalluri, Samrat Akram, Wasim Department of E.C.E Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819984503

One of the main crops in India is the potato. In India, potato farming has gained enormous popularity in recent years. However, a number of diseases are making it more expensive for farmers to grow potatoes while also affecting their personal lives. Our primary objective is to use cutting-edge machine learning technology to identify potato disease from leaf images. This paper provides an image that is processed and deep learning-based automated algorithms will identify and categorize potato leaf diseases. The best method for identifying and studying these disorders is image processing. Around 2152 images of diseased and healthy potato leaves were divided into groups for this project. Our results demonstrate that machine learning outperforms all currently available tasks in potato disease identification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Computational intelligence in eye disease diagnosis: a comparative study

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第3期61卷 593-615页

作者： Kumar, S. v. Mahesh Gunasundari, R. Amrita Coll Engn & Technol Dept Elect & Commun Engn Nagercoil Tamil Nadu India Puducherry Technol Univ Erstwhile Pondicherry Engn Coll Dept Elect & Commun Engn Pondicherry India

In recent years, eye disorders are an important health issue among older people. Generally, individuals with eye diseases are unaware of the gradual growth of symptoms. Therefore, routine eye examinations are required for early diagnosis. Usually, eye disorders are identified by an ophthalmologist via a slit-lamp investigation. Slit-lamp interpretations are inadequate due to the differences in the analytical skills of the ophthalmologist, inconsistency in eye disorder analysis, and record maintenance issues. Therefore, digital images of an eye and computational intelligence (CI)-based approaches are preferred as assistive methods for eye disease diagnosis. A comparative study of CI-based decision support models for eye disorder diagnosis is presented in this paper. The CI-based decision support systems used for eye abnormalities diagnosis were grouped as anterior and retinal eye abnormalities diagnostic systems, and numerous algorithms used for diagnosing the eye abnormalities were also briefed. various eye imaging modalities, pre-processing methods such as reflection removal, contrast enhancement, region of interest segmentation methods, and public eye image databases used for CI-based eye disease diagnosis system development were also discussed in this paper. In this comparative study, the reliability of various CI-based systems used for anterior eye and retinal disorder diagnosis was compared based on the precision, sensitivity, and specificity in eye disease diagnosis. The outcomes of the comparative analysis indicate that the CI-based anterior and retinal disease diagnosis systems attained significant prediction accuracy. Hence, these CI-based diagnosis systems can be used in clinics to reduce the burden on physicians, minimize fatigue-related misdetection, and take precise clinical decisions.

关键词： Human eye Anterior eye Retina Ophthalmology Eye diseases Digital imaging Segmentation Computational intelligence Classification

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Recent advances in automatic feature detection and classification of fruits including with a special emphasis on Watermelon (Citrillus lanatus): A review

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NEUROCOMPUTING 2023年第1期526卷 62-79页

作者： -Hernandez, Danilo Caceres Gutierrez, Ricardo Kung, Kelvin Rodriguez, Juan Lao, Oscar Contreras, Kenji Jo, Kang-Hyun Sanchez-Galan, Javier E. Univ Tecnol Panama Fac Ingn Electr Campus Victor Levi Sasso Panama City Panama Univ Tecnol Panama Fac Ingn Sistemas Computac Campus Victor Levi Sasso Panama City Panama Univ Ulsan Grad Sch Elect Engn Intelligent Syst Lab Address One Ulsan South Korea Secretaria Nacl Ciencia Tecnol & Innovac SENACYT Sistema Nacl Invest SNI Panama City Panama

This document provides an overview of advances in the task of automatic feature detection and classifi-cation of fruits, with and special interest in watermelon (Citrillus lanatus). The review was written with the objective to highlight the wealth of knowledge that exist in the application of analytical, smart and sensing image recognition techniques commonly used in agro-industry, and the computational approaches used to make the classification possible. Also, an specific interest is put in the contributions made in the development of automatic recognition systems geared towards for watermelon using images, acoustic and spectroscopy methodologies. The importance of this document is that it provides a conceptual summary of the methods for the automatic recognition of fruits, including machine learning and evolutionary computational algorithms to analyze their sensed data. In conclusion this is a first step into recognizing the challenges and opportunities that can be addressed in this field to augment the vis-ibility of these methods and to further modernize the agro-industrial sector.(c) 2023 Elsevier B.v. All rights reserved.

关键词： Agro-industry Fruits Watermelon Machine learning image processing Feature recognition

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Classification and Localization of Objects Using Faster RCNN 6th

Classification and Localization of Objects Using Faster RCNN

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6th International Conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Dakey, Bhavya Sree Ramani, G. Shabber, Md. Jogu, Sirisha Javvaji, Divya Sree Jangam, Hasini Department of CSE B V Raju Institute of Technology Narsapur Telangana Medak502313 India

ISBN: (纸本)9789819984503

On the basis of modern technological advancements, novel and sophisticated algorithms have been devised. The progression in object detection technologies, similar Fast and Faster RCNN algorithms, have resulted in reduced detection time of objects/entities, coupled with high precision levels. The latest study examines the efficiency of a newly proposed algorithm that detects RPN and Fast RCNN. The input ROIs for the RCNN network, using the RPN’s region proposals, a potential way to unify the components is by creating a single network that facilitates communication or transfer of convolutional features to recognize a specific object within each image. By employing a unified network, the requirement to obtain Region of Interest (ROI) from an external network is eliminated making the process cost-effective. The efficiency and accuracy of object detection have been crucial topics around the development of computer vision systems. With the foundation of deep learning techniques, there has been significant progress in the precision of object detection. The proposed project incorporates classification and localization for object detection. Precisely, the system takes an image as input and produces an output that consists of bounding boxes for each object present in the image, along with corresponding information about the class of each object contained within the respective bounding box. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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MedSeg: A Medical image Analyzer

MedSeg: A Medical Image Analyzer

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Control Instrumentation System Conference (CISCON) - Guiding Tomorrow - Emerging Trends in Control, Instrumentation, and systems Engineering

作者： Mehta, Shlok Pancholi, Keyur Pandey, Sakshi Dhage, Dr Sudhir Sardar Patel Inst Technol Comp Engn Dept Mumbai Maharashtra India

ISBN: (纸本)9798350375480;9798350375497

This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced image processing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception v3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.

关键词： Machine learning Medical image analysis Chest X-Rays prediction imageNET Inceptionv3 Disease prediction

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An Efficient ODR for Computer Night vision Using Multi-Scale Retinex Network-Aided Enhanced images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Charles Prabu, v. Pandiaraja, P. Sathiyamoorthi, v. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. Computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once version 7 (Yolov7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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