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13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

作者： Ismail, Ahmad Puad Azahar, Muhammad Afiq Bin Tahir, Nooritawati Md Daud, Kamarulazhar Kasim, Nazirah Mohamat Universiti Teknologi Mara Cawangan Pulau Pinang Electrical Engineering Studies Malaysia Universiti Teknologi Mara Electrical Engineering Studies Shah Alam Malaysia

ISBN: (纸本)9798350323184

The advancement of artificial intelligence (AI) has bought many advances to human society as a whole. By using daily activities and integrating the technology from the fruits of AI, we can manage to gain further access to knowledge we can only begin to imagine. In identifying human action recognition (HAR);processing photos and videos to discern whether a human is present, then mapping the subject classified, which lastly determines the action being carried out is the objective. To achieve this, various steps are taken and careful approach is required, with the extensive amount of research, numerous troubleshooting and experimentation is required. The AI architecture has to learn from dataset collected for it to discern the identification of action properly. HAR is achieved by using Python code using real-time webcam feed. Human pose detection library known as MediaPipe Pose Detection detects human anatomy from input through joints key-points. MediaPipe algorithm that extract features in x-y-z axis with visibility (four variables) and the extracted data is trained using CNN-LSTM based on the trained and tested algorithm classifier model. The output obtained produced an RGB-skeleton and an action label on the detected subject as standing, waving, walking and sitting, has yielded good results. © 2023 IEEE.

关键词： image processing

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Empirical Assessment of Identifying Human Blood Group Based on image processing Assisted deep learning Principles

Empirical Assessment of Identifying Human Blood Group Based ...

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

作者： G. Ramkumar N. Janardhana Rao V. Nanammal SK. Shaphiya J Giri Ahmad Abdelhafiz Ali Samhan Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of MBA QIS College of Engineering and Technology Ongole Andhra Pradesh Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai Department of S&H QIS College of Engineering and Technology Ongole AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This study presents an empirical assessment of identifying human blood groups using image processing assisted by deep learning principles, specifically employing a cascaded Convolutional Neural Network (CNN) and Light Gradient Boosting Machine (LightGBM). Traditional methods of blood group identification are time-consuming and prone to errors, prompting the need for automated, more efficient systems. In this work, a CNN model was initially used to extract deep features from blood sample images, followed by a LightGBM classifier for final blood group classification. A comprehensive dataset, representing all major blood groups (A, B, AB, O, and Rh types), was collected and preprocessed for training and testing. The cascaded CNN-LightGBM approach achieved an accuracy of 92.4%, significantly outperforming baseline models, including standalone CNN (88.2%) and Random Forest (83.5%). The model also demonstrated high precision (92.1%), recall (92.0%), and F1-score (92.1%). real-world testing was conducted to validate its robustness in clinical settings. The results indicate that the proposed model is well-suited for accurate and efficient blood group identification, with low inference time, making it ideal for real-time applications. This approach has the potential to transform blood diagnostics by providing an automated, scalable, and accurate solution.

关键词： deep learning Training Accuracy Transforms Feature extraction Robustness real-time systems Convolutional neural networks Blood Testing

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deep learning-Driven Smart Signal Systems for Advanced image and Video processing in Urban Infrastructure

Deep Learning-Driven Smart Signal Systems for Advanced Image...

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Smart Technologies, Communication and Robotics (STCR)

作者： K. S. Bharaty Praveen SR Konduri Department of EEE School of Engineering Mohan Babu University Tirupati Andhra Pradesh Department of Data Science School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

The increasing integration of intelligent systems in metropolitan environments emphasizes the important role effective image and video processing performs in applications including the traffic management, the monitoring of safety, and the urban planning. When handling dynamic scenarios, that is, changes in traffic conditions, occlusions, and the need for real-time analysis, conventional signal processing methods sometimes fail. Strong and adaptable solutions able to manage both these complexity as well as others are thus much needed. This work intends to introduce a deep learning-based smart signal system designed to process real-time video feeds for uses connected to urban traffic and safety. The proposed system uses recurrent neural networks (RNNs) for temporal sequence comprehension; conversely, convolutional neural networks (CNNs) are used for spatial data analysis. Two approaches used in order to improve the performance of the model are data augmentation and transfer learning, which increase the accuracy of decision-making using multi-modal data sources, video streams, sensor inputs, environmental parameters. This helps one to get beyond the challenge of small datasets. System performance is discovered using benchmark datasets and video from actual traffic events. Better than the methods now in use, the results show object detection has 95% accuracy and anomaly detection has 92% accuracy. Promising for use in smart cities, the proposed framework exhibits scalability and flexibility over a wide range of events. Its characteristics, traffic optimization, safety monitoring, and data-driven urban planning, which comprise traffic control, safety monitoring, and data-driven urban planning, have the ability to drastically change urban transportation infrastructure management.

关键词： Accuracy Recurrent neural networks Smart cities Urban planning Object detection Streaming media Traffic control real-time systems Safety Monitoring

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deep learning, Machine learning - Digital Signal and image processing: From Theory to Application

arXiv

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arXiv 2024年

作者： Hsieh, Weiche Bi, Ziqian Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Ren, Jintao Niu, Qian Chen, Silin Liu, Ming National Tsing Hua University Taiwan Indiana University United States Kyoto University Japan AppCubic Rutgers University United States University of Wisconsin-Madison United States Purdue University United States Georgia Institute of Technology United States National Taiwan Normal University Taiwan University of Hawaii United States Xi’an Jiaotong-Liverpool University China Aarhus University Denmark Zhejiang University China

Digital Signal processing (DSP) and Digital image processing (DIP) with Machine learning (ML) and deep learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete Fourier transforms

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Development of an image processing based Student Engagement Prediction in Online Classes by using deep learning Strategy

Development of an Image Processing based Student Engagement ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： ParabathiniBulah Pushpa Rani I. Rajesh I. Saidulu Ala H. Jaber R Chadge Siva Sundaram Department of IT QIS College of Engineering and Technology Ongole Department of CSE Knowledge Institute of Technology Salem College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering Faculty of engineering Zarqa university Zarqa Jordan Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Maharashtra Faculty of Science and Technology Spectrum International University College Selangor Malaysia

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Online education has rapidly evolved, and with it, the need for effective monitoring of student engagement. This study presents an innovative approach for predicting student engagement in online classes using a deep learning strategy that combines Inception modules and an Artificial Neural Network (ANN). The system processes real-time video streams and static images to identify and classify student engagement levels, including attentive, distracted, and confused states. A comprehensive dataset of 10,000 samples was collected from online class recordings, carefully annotated and augmented through techniques such as rotation, scaling, and brightness adjustment. The pre-trained Inception model was fine-tuned on this dataset to extract high-level discriminative features, which were then fed into a multi-layer ANN for classification. The integrated model achieved an overall accuracy of 92.5%, with precision and recall values of 90.2% and 93.0%, respectively. Detailed experiments reveal that the model performs consistently across varied classroom scenarios, offering reliable real-time predictions. Performance metrics such as F 1 score (91.6) and ROC-AUC (0.95) further substantiate the system's effectiveness. The system's design not only ensures robust performance but also adheres to ethical guidelines regarding data collection and privacy. By providing educators with actionable insights, this research paves the way for responsive teaching strategies and improved student monitoring in online environments. The promising accuracy values and the efficient processing time of 25 milliseconds per image underline the practical potential of the proposed system. Future developments will explore multi-modal data integration and advanced temporal analysis to further enhance engagement prediction.

关键词： deep learning Accuracy Education Artificial neural networks Streaming media Feature extraction real-time systems Monitoring System analysis and design Streams

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image processing and deep learning Based Road Object Detection System for Safe Transportation 15

Image Processing and Deep Learning Based Road Object Detecti...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Prito, Rizvee Hassan Hossain, Md. Shafayat Islam, Md. Shajibul Meem, Mehzabin Ali, Md. Nawab Yousuf East West University Computer Science and Engineering Dept Dhaka Bangladesh

ISBN: (纸本)9798350370249

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface. © 2024 IEEE.

关键词： Traffic signs

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A Comparative Analysis of image processing and deep learning Techniques for License Plate Recognition in Difficult Environments

A Comparative Analysis of Image Processing and Deep Learning...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Tejaswi S Tina Babu Tejaswi K Department of Computer Science Seshadripuram Degree College Mysuru India Department of Computer Science and Engineering School of Engineering Alliance University Bengaluru India Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions.

关键词： deep learning Support vector machines YOLO Accuracy Lighting Computer architecture real-time systems Vectors License plate recognition Meteorology

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Cotton Plant Disease Detection and Prevention Using deep learning and image processing

Cotton Plant Disease Detection and Prevention Using Deep Lea...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruthvija Kesani Jaya Prakash Sunkavalli Mahamamad Rafi Shaik Kusuma Yadala Dept. of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

One of the most important aspects of the agricultural economy is the production of cotton, which is threatened by diseases that lower crop quality and yield. Conventional techniques for diagnosing diseases are frequently subjective and labor-intensive. This paper presents a novel method for the automatic detection and prevention of cotton plant diseases that makes use of deep learning techniques. A convolutional neural network (CNN) model is trained using a dataset that includes various photos of both healthy and sick cotton plants. The suggested model offers a dependable and time-efficient solution by exhibiting high accuracy in differentiating between different diseases. Moreover, proactive disease detection is made possible by the integration of real-time monitoring systems, such as drones fitted with high-resolution cameras. Early detection lessens the need for broadspectrum antibiotics by enabling the prompt application of preventive measures, such as targeted therapies. Finally, we conduct a comprehensive computational analysis of eight cutting-edge object detection algorithms on the cotton plant dataset to identify diseases on the leaves and seven cutting-edge classification algorithms on the cotton plant datasets to determine if a leaf has a disease or not. computed results indicate that it has a high degree of object detection accuracy.

关键词： deep learning Plant diseases image processing Plants (biology) Transfer learning Object detection Classification algorithms

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A Novel Human Face Expression Recognition Based on image processing Assisted Convoluted deep learning Methodology

A Novel Human Face Expression Recognition Based on Image Pro...

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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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image processing based image to Cartoon Generation: Reducing complexity of large computation arising from deep learning

Image Processing based Image to Cartoon Generation: Reducing...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Shruti Kumari Shrivastava Ruchi Gajjar Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad Gujarat India

This paper proposes an approach to convert real life images into cartoon images using image processing. The cartoon images have sharp edges, reduced colour quantity compared to the original image, and smooth colour regions. With the rapid advancement in artificial intelligence, recently deep learning methods have been developed for image to cartoon generation. Most of these methods perform extremely huge computations and require large datasets and are time consuming, unlike traditional image processing which involves direct manipulation on the input images. In this paper, we have developed an image processing based method for image to cartoon generation. Here, we perform parallel operations of enhancing the edges and quantizing the colour. The edges are extracted and dilated to highlight them in the output colour image. For colour quantization, the colours are assigned based on proposed formulation on separate colour channels. Later, these images are combined and the highlighted edges are added to generate the cartoon image. The generated images are compared with existing image processing approaches and deep learning based methods. From the experimental results, it is evident that the proposed approach generates high quality cartoon images which are visually appealing, have superior contrast and are able to preserve the contextual information at lower comnutational cost.

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