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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Tamilselvi Venkata Siva Prasad Ch M. Varuna Kumari R. Sai Lavanya Nandurkar Y Mohammad Rasmi Al-Mousa Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of ECE Jeppiaar Engineering College Rajiv Gandhi salai Chennai Department of S&H QIS College of Engineering and Technology Ongole Prakasam District Andhra Pradesh India Department of MCA QIS College of Engineering and Technology Ongole Prakasam District Andhra Pradesh India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Cyber Security College of Information Technology Zarqa University zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Facial expression recognition has become a critical component in applications involving human-computer interaction, security systems, and behavioral analysis. This paper presents a novel approach to human face expression recognition using a convoluted deep learning methodology supported by image processing techniques. The system is designed to accurately classify seven fundamental emotions—Anger, Disgust, Fear, Happy, Sadness, Surprise, and Neutral—using the FER2013 dataset. The methodology includes preprocessing steps such as noise reduction, grayscale conversion, and face registration. Feature extraction is performed using Gabor filters and Local Binary Patterns (LBP), while Convolutional Neural Networks (CNNs) are employed to learn deep hierarchical features. The system was optimized through hyperparameter tuning, achieving an overall accuracy of 96.29% on the test set. Precision, recall, and F1-scores for each emotion also exceeded 95%, with “Happy” and “Surprise” emotions showing the best performance. The results indicate that the model is effective for real-time applications requiring reliable emotion detection. The paper concludes with discussions on model performance, challenges in classifying similar emotions, and recommendations for future improvements.

关键词： deep learning Emotion recognition image recognition Face recognition Computational modeling Feature extraction real-time systems Security Reliability Tuning

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A deep learning Approach for Fruit Recognition and Calorie Estimation Through image processing

A Deep Learning Approach for Fruit Recognition and Calorie E...

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International Conference on Computing and Networking Technology (ICCNT)

作者： N Kaushik Y Yaswanth Khaleel Ahmed Shaik Department Of ECE V R Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Maintaining a healthy diet can be difficult in our fast-paced lives as we don’t tend to have much time and knowledge on estimating calories. This paper proposes a deep learning approach to address this challenge by focusing on fruit consumption. By processing the input image, the system can determine the fruit type and estimates the calorie content of the fruits consumed. This opens door for users to make informed dietary choices and manage their overall calorie intake within the limited set of fruits. Furthermore, the approach can be expanded to encompass a wider variety of food types by installing additional datasets, thereby enabling comprehensive calorie estimation for a balanced diet.

关键词： deep learning YOLO image segmentation Computer vision Machine learning algorithms image recognition Neural networks Estimation Focusing Servers

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Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic

Experimental Analysis of Malarial Parasites Infection by usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： N Meenakshisundaram Sajiv G Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the “Malaria Parasite image - Malaria Species” dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment.

关键词： deep learning Accuracy Attention mechanisms Malaria Parasitic diseases Sensitivity and specificity Metadata Robustness real-time systems Integrated circuit modeling

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Terahertz image processing - Resolution Enhancement and Object Segmentationusing W-Net deep learning

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SSRN 2024年

作者： Mondal, Shyamal Jampani, Kashyap Raj, Akshay R. Sethi, Abhijit Chowdhury, Dibakar Roy Dept. of Applied Physics Defence Institute of Advanced Technology Maharashtra Pune411025 India Dept. of Physics Mahindra Ecole Centrale University Hyderabad500043 India

Terahertz images typically suffer from poor image quality and do not allow traditional machine learning methods to detect and identify objects. In this work, an image segmentation algorithm called W-Net is deployed for the first time, to segment concealed objects from the poor-quality Terahertz image samples. This involves developing an image enhancement and segmentation system for terahertz images. The proposed system comprises of two stages:in the first stage, the resolution of low-resolution THz images has been enhanced by training a U-Net deep learning model. In the latter stage, another U-Net model is trained to segment the enhanced images using corresponding masks to identify regions of interest. The proposed system is evaluated using a dataset of low-resolution terahertzimages with its respective high-resolution images and masks. The system performed well with an accuracy, precision, receiver operating characteristic and F1 scores of 99.1%, 0.9143, 0.9977 and 0.9146, respectively for enhancement. Moreover, values of the same evaluation metrics deduced as 99.9%, 0.9851, 0.9999 and 0.9923, respectively for segmentation. Therefore, the demonstrated scheme is expected to identify and classify objects hidden in real-time terahertz images and improve the quality of the same to enable accurate segmentation for applications like security screening, biomedical applications, quality and health monitoring in food industry, etc. © 2024, The Authors. All rights reserved.

关键词： image enhancement

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SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Lee, Kyungsu Lee, Haeyun Hwang, Jae Youn Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Daegu Gyeongbuk Inst Sci & Technol DGIST Dept Elect Engn & Comp Sci Interdisciplinary Studies Artificial Intelligence Daegu 42988 South Korea

deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks, deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks. To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on anew indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems.

关键词： deep learning Semi-supervised learning Efficient learning Scene recognition Indoor localization

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Coffee Bean Defects Automatic Classification realtime Application Adopting deep learning

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IEEE ACCESS 2024年 12卷 126503-126517页

作者： Thai, Hong-Danh Ko, Han-Jong Huh, Jun-Ho Natl Korea Maritime & Ocean Univ Dept Data Informat Pusan 49112 South Korea Natl Korea Maritime & Ocean Univ Dept Interdisciplinary Major Ocean Renewable Energ Pusan 49112 South Korea Korea Natl Open Univ Dept Agr Sci Seoul 03087 South Korea Natl Korea Maritime & Ocean Univ Dept Data Sci Pusan 49112 South Korea

The coffee industry contributes to the economic restructuring of many countries, often associated with a closed process from production to consumption. The green coffee bean grading standard provided by the Specialty Coffee Association (SCA) is one of the best methods for grading coffee beans. Traditionally, the assessment of quality and classification of coffee beans relies on visual examination, which demands significant time and effort and is easily inaccurate. deep learning technology, characterized by precision, velocity, and veracity, can be adopted to empower the reduction of human labor and improve the productivity, quality, and efficiency of these tasks. Therefore, this paper aims to address these issues by implementing deep learning to classify coffee bean quality in real time by integrating the system with a cloud-based solution. First, image processing and data augmentation techniques are employed to handle the coffee bean image data. Subsequently, the model is trained using YOLOv8, a framework for object recognition, and OpenCV, an open-source image processing technology, to classify coffee beans. Finally, an application is developed for real-time video and image-streaming coffee bean recognition using React Native, NodeJS, and Python. The experimental results provide empirical evidence that our system enhances accuracy and efficiency in the tasks of classifying coffee bean quality in nine distinct varieties of coffee beans, with the time required reduced to a mere 1 to 3 seconds. Our system can be a useful solution for coffee producers, processors, and traders without relying on stationary equipment, especially in large farms or warehouses.

关键词： deep learning Classification algorithms real-time systems Accuracy Nearest neighbor methods image processing Crops Defect detection YOLO Cloud computing Economics Coffee bean defects quality classification computer vision YOLOv8 OpenCV cloud-based application deep learning

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Application of entertainment interactive robots based on deep learning in referee assistance mode in sports competitions

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Li, Xiaolong Shandong Univ Polit Sci & Law Student Affairs Off Jinan 250014 Shandong Peoples R China

Interactive robots are intelligent auxiliary tools that can monitor sports events in real-time and provide entertainment information services. This article studies the application of entertainment interactive robots based on deep learning in the referee assistance mode of sports competitions. The system first uses a camera to capture real-time images of volleyball matches, then preprocesses the images using image processing algorithms, and uses deep learning algorithms to recognize and track the balls and players in the images. By training the model, the system can accurately determine key information such as player actions. deep learning technology is used to train interactive entertainment robots to identify and analyze key decision events in games. Through image recognition, action analysis and rule matching, the robot monitors the game process in real time and determines the referee's possible errors in the decision. The system generates the penalty results based on the penalty rules and competition rules, and displays them to the referee and audience through display screens or sound prompts. After experimental verification, the volleyball match referee judgment assistance system based on image processing and deep learning has performed excellently in terms of accuracy and speed. Compared to manual referees, the system can identify and track the ball and players more quickly, reduce the possibility of misjudgments, and improve the fairness of the game.

关键词： deep learning Interactive robot for entertainment Sports competitions Referee auxiliary mode

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TrashIntelAI- Garbage Collection Automation using deep learning and image processing

TrashIntelAI- Garbage Collection Automation using Deep Learn...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Ranjana Jadhav Aditya Mhaske Aditya Nawale Mokshit Oswal Sumit Mulhar Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India

Garbage collection in urban areas has become a major challenge due to the increase in trash production. New technologies, including the application of deep learning and image processing methods, have been created to solve this problem. In this study, we present an automated garbage collection system that recognizes and categorizes rubbish in real-time using computer vision and machine learning methods. A deep learning model classifies the garbage into several categories, and the system also contains an image processing pipeline that pre-processes incoming photos and extracts relevant data. We executed experiments on a real-world dataset to assess the performance of the suggested solution. The outcomes demonstrate that the suggested approach classifies garbage with a high degree of accuracy. The system can recognize and categorize various waste categories, including recyclables, organic garbage, and non-recyclables, which can then be collected individually for proper disposal. This is made possible by the application of deep learning and image processing algorithms. The suggested approach can potentially increase trash disposal efficiency, reduce waste sent to landfills, and promote a cleaner and healthier environment in metropolitan settings. The burden of garbage collection staff can be reduced, and the danger of harm can be reduced, with real-time monitoring and automated waste collection.

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Determination of License Plate Using deep learning and image processing 6th

Determination of License Plate Using Deep Learning and Image...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Tyagi, S.S. Rathore, Riti Rathore, Vaibhav Rai, Shruti Rohila, Shruti Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9789819933143

Automatic license plate recognition (ALPR) provides a way to extract the vehicle’s number plates using the computer vision technology. To achieve higher accuracy rates, ALPR is widely used across the world in traffic controlling, automated toll collected system and in society parking, etc. Any ALPR system consists of following processes, viz. video recording as an input, identifying that object is moving or not, differentiating between vehicles and non-vehicles, localization of license plate of vehicles, extractions of characters of license plate, and recognition of segmented characters. In the real world, due to changing weather conditions, polluted environments, different intensities of headlight, etc., it is very difficult to capture an input image. The ALPR processes become more difficult if the license plates of each vehicle do not follow the rules and protocols set by the corresponding Motor Vehicles Department. If the license plate is broken, tilted, dirty, or missing some characters, then all these difficulties add up to generate an efficient ALPR system. Traditional LPR methods extract characters by one-by-one characters segmentation which is very time consuming, but our method provides image preprocessing, machine learning, and neural network techniques which makes it time saving and achieves greater accuracy rates. Neural network technique consists of image preprocessing, contour extraction, rotation, distortion, and character segmentation processes. Experimentally, we can verify our proposed method on a public dataset, and the results state that our method overcomes the drawbacks of previous methods and it achieves accuracies of more than 97% for different types of license plate datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Video recording

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Detection of Employee Fatigue Based on image processing Using deep learning Model

Detection of Employee Fatigue Based on Image Processing Usin...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Michael Ethan I Gede Putra Kusuma Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Fatigue in workplace is a common thing shared by all employees. Continuous exposure of fatigue could lead to negative productivity for companies. Current research on fatigue detection mostly focused to detect fatigues from a single 2D image data, meanwhile research on fatigue detection using each frames from video data is needed for better detection for fatigue process. The main advantage of using each timesteps of frames from video data which makes the model is able to predict long term dependency of fatigue person. To solve this problem, a deep learning model is proposed that could detect employee fatigue based on image processing using video data. Three models are used to train data using Convolutional Neural Network (CNN), which are time Distributed CNN LSTM, 3D CNN, and 3D CNN LSTM. Out of those three models, the best model to detect fatigue person from video data is time Distributed CNN Model with F-1 Score value of 0.77 and Accuracy Score of 0.76 for testing data. The model that gives best inference time is also time Distributed CNN Model with the average value of 382 miliseconds to detect fatigues from 58 testing data with each of the data has duration of 3 seconds and 12 frames.

关键词： deep learning Three-dimensional displays image processing Computational modeling Distributed databases Fatigue Data models Convolutional neural networks Long short term memory Testing

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