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检索条件"机构=Dept. of Information Engineering and Computer Science and Mathematics"

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YOLOv8 for Bangla License Plate Recognition: Advancing Real-Time Object Detection in Localized Contexts 7

YOLOv8 for Bangla License Plate Recognition: Advancing Real-...

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Saha, Utsha Ahamed, Imam Uddin Hossain, M. Iffat North Dakota State University Dept. of Computer Science FargoND United States Liverpool John Moores University School of Computer Science and Mathematics Liverpool United Kingdom University of Central Florida Dept. of Electrical and Computer Engineering OrlandoFL United States

ISBN: (纸本)9798350375886

Automatic License Plate Recognition (ALPR) systems play a crucial role in traffic management and security applications, and the development of accurate and efficient ALPR models is essential for their success. This study investigates the application of YOLOv8, a state-of-the-art object detection architecture, for recognizing Bangladeshi license plates. A comprehensive dataset, combining images from various vehicle categories and environments, was curated and augmented to train five variants of the YOLOv8 model: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv81, and YOLOv8x. The models were rigorously evaluated using metrics such as mean Average Precision (mAP), precision, recall, and F 1 -score. The results demonstrate the effectiveness of YOLOv8 in detecting and recognizing Bangladeshi license plates, with YOLOv8x achieving the highest mAP50 and mAP5095 scores of 0.96 and 0.75, respectively. The study highlights the adaptability of YOLOv8 to the unique characteristics of Bangladeshi license plates and provides insights into model selection criteria based on computational resources and performance requirements. Future research directions include expanding the dataset to cover a wider range of environments and integrating the YOLOv8 models into a comprehensive ALPR system for enhanced traffic management and security applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Aggregated Spatio-temporal MLP-Mixer for Violence Recognition in Video Clips 6

Aggregated Spatio-temporal MLP-Mixer for Violence Recognitio...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Shen, Yu-Shian Chen, Jenhui Chang Gung University Guishan Dist Dept. Computer Science & Information Engineering Taoyuan33302 Taiwan

ISBN: (纸本)9798350301953

Existing violent behavior datasets are not perfect in quantity and quality due to the difficulty of collecting. Although the state-of-the-art Transformer models had shown their capability in behavior recognition, it is unsuitable for the task of short-term behavior understanding (e.g., violent behavior recognition) due to the need for a large amount of data to achieve their best performance. Recently, a simple deep learning architecture, an all multilayer perceptron (MLP) architecture called MLP-Mixer, was proposed against Transformer in the task of a few-sample dataset to obtain competitive results. Motivated by spatio-temporal features on neurons, we invent a dual-form dataset for MLP-Mixer-based model training called aggregated spatio-temporal MLP-Mixer (ASM) to handle video understanding tasks. We show that ASM outperforms the state-of-the-art Transformer models as well as some of the best-performed convolutional neural network (CNN) approaches on three public datasets, smart-city CCTV violence detection dataset (SCVD), real-life violence situations (RLVS) dataset, and Hockey fight. Experimental results further validate our idea on short-term behavior scene understanding improvement. © 2023 IEEE.

关键词： Mixers (machinery)

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Mobile Gateways for LoRa End-Devices: Support Connectivity on the Move 29

Mobile Gateways for LoRa End-Devices: Support Connectivity o...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Moysiadis, Vasileios Skoutas, Dimitrios N. Skianis, Charalabos Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece International Hellenic University Dept. of Computer Science Greece University of Western Macedonia Dept. of Electrical and Computer Engineering Kozani Greece Kingston University Dept. of Networks and Digital Media Kingston upon Thames United Kingdom

ISBN: (纸本)9798350377644

This paper proposes a method enabling mobile LoRa gateways to support mobile end devices. The approach utilizes trilateration and similar approaches based on RSSI to estimate the position of end devices and specify the movement of the gateways to maintain contact. The evaluation of the proposed method tests various parameters such as speed and number of end devices, and it shows that it is able to retain contact even with speeds up to 12 mps. Moreover, the evaluation shows that the ADR-MIN algorithm performs better in this scenario than the default ADR. © 2024 IEEE.

关键词： Gateways (computer networks)

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Sinhala Sign Language Learning System for Hearing Impaired Community 4

Sinhala Sign Language Learning System for Hearing Impaired C...

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4th International Informatics and Software engineering Conference, IISEC 2023

作者： Idushan, K.A.S. Dilshan, P.G.T. Jayasundera, P.S. Madhushan, K.L.B. Krishara, Jenny Wijendra, Dinuka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350318036

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-To-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users. © 2023 IEEE.

关键词： Learning systems

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A Sustainable Smart Irrigation and Crop Protection System for Bangladesh 8

A Sustainable Smart Irrigation and Crop Protection System fo...

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8th IEEE International Conference on Computational System and information Technology for Sustainable Solutions, CSITSS 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Miraz, Shafiun Jahan, Israt Hoque, Aminul Al Noman, Abdullah Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Kazla Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Uttara Model Town Dhaka Bangladesh Asian University of Bangladesh Dept. of Computer Science and Engineering Bangabandhu Road Tongabari Ashulia Dhaka Bangladesh Jahangirnagar University Dept. of Institute of Information Technology Savar Dhaka Bangladesh

ISBN: (纸本)9798331505462

Bangladesh's most crucial sector for development is agriculture. Rainfall has been irregular in recent years due to climate change. In our country, the untimely rain and hailstorms harm crops and vegetables, especially winter vegetables. Surface water is mainly used for agriculture, but its availability constantly declines. Our current key challenge is to save surface water while protecting crops from rain and hail. IoT is cutting-edge technology in many applications, such as smart homes, smart cities, and intelligent traffic management. The IoT may be implemented in so many different industries and sectors. This research work is about smart irrigation with crop protection systems in agriculture. The system's primary parts are the sensors, water pump, servo motors, embedded systems, and internet connectivity. IoT sensors like soil moisture sensors and rain sensors are used in the suggested methodology. The fields of crops have received water from a pump. A motor has covered the crops. The proposed solution is a straightforward Internet of Things setup that sends data to the Arduino after detecting rainfall and soil moisture levels. Depending on the sensor data, the Arduino sends signals to the motor and pump to irrigate the crops and cover crops as necessary, revealing the crops after rain. The system is constructed economically using bamboo, and poly and plastic nets have been utilized to protect the crops from rain and fog. The system's primary effects are increased productivity, decreased crop damage rate, and saved surface water. © 2024 IEEE.

关键词： Soil moisture

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on information Technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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Rapid Construction of Diode Models Based on Physical Mechanism and Machine Learning 12

Rapid Construction of Diode Models Based on Physical Mechani...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Lin, Kun-Wei Li, Jia-Wei Jian, Wei-Chen Jiang, Zheng-Han Lu, Hsin-Hou dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Electrical Engineering National Cheng-Kung University Tainan Taiwan

ISBN: (纸本)9798350324693

Diodes are crucial components in electronics, with applications ranging from rectification to sensing. Traditional modeling methods are time-consuming due to manual parameter extraction. In this study, two machine learning approaches for the rapid construction of diode models were developed and compared. These two methods named the Turning Point Segmentation (TPS) and Diode Operating Range Segmentation (DORS) are based on machine learning to find turning point segmentation and fixed work interval segmentation. From the experimental results, it is known that finding the turning point with machine learning has better results. In terms of MSE, the train and test sets of TPS (DORS) are 0.008 (0.011) and 0.006 (0.019), respectively. In terms of linearity, the R2-scores of the training set and test set of TPS (DORS) are 0.992 (0.979) and 0.994 (0.961), respectively. In all data simulations, the MSE and R2 of TPS (DORS) are 0.008 (0.013), and 0.993 (0.974), respectively. Both have excellent performance in terms of time consumption. This innovative technique holds promise for revolutionizing electronics engineering and advancing the role of machine learning in modeling complex systems. © 2023 IEEE.

关键词： Machine learning

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Predict of the Hidden Drug Interactions Based on Similarity Utilizing the Knowledge Graph

Predict of the Hidden Drug Interactions Based on Similarity ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Elfiky, Mariam Gamal Ghoniemy, Samy Eldin, Ahmed Sharaf University of Hertfordshire hosted by Gaf Dept. of Information Technology Management for Business Cairo Egypt Galala University Faculty of Computer Science and Engineering Cairo Egypt Sinai University Faculty of Information Technology and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Drug-drug interactions (DDIs) can significantly impact treatment outcomes by causing adverse effects or reducing therapeutic efficacy. Whereas many DDIs are already known, there are numerous hidden interactions. Therefore, this study tries to predict unknown interactions based on drug structure and target information from the Drug Bank dataset. This paper trains the random forest model integrated with a knowledge graph on a dataset containing 922 unique drugs and 731,196 drug interactions. To illustrate the complex interactions between these drugs, we created a knowledge graph with Neo4j. This graph integrates information gathered from the dataset and extracts relevant data using Cypher queries. The inference engine was created according to the model's predictions on unknown DDIs. The random forest model, with an F1-score of 0.82, achieved a recall of 0.99 and a precision of 0.70. Also, 34,580 potential DDIs were extracted from the sample dataset of drugs. By embedding the knowledge network, our inference engine significantly enhanced predicting the hidden interactions and was found strong in the balance of precision and recall. Our research is applicable to the enhancement of drug safety and effectiveness via mitigating potential DDIs by identifying them. © 2024 IEEE.

关键词： Knowledge graph

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Innovative Approaches to Dental Age Estimation: A Comprehensive Review of CNN Technologies and Ethical Issue 5

Innovative Approaches to Dental Age Estimation: A Comprehens...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shyni, I. Domilin Sheeja Herobin Rani, C. Sheeba, I. Rexiline Mahizha, S.I. Dmi Engineering College Dept. Of Computer Science and Engineering Aralvoimozhi India St.Xavier's Catholic College of Engineering Dept. Of Electronics and Communication Engineering Nagercoil India Sathyabama Institute of Technology and Science Dept. Of Electronics and Communication Engineering Chennai India St.Xavier's Catholic College of Engineering Dept. Of Information Technology Nagercoil India

ISBN: (纸本)9798350389609

Estimating dental age is crucial in multiple disciplines, such as forensic science, orthodontics, and pediatric dentistry. Traditional methods, while effective, often involve manual processes and subjective judgment. Recent advancements in Convolutional Neural Networks (CNNs) have introduced innovative approaches that promise greater accuracy and efficiency. This comprehensive review examines the latest CNN technologies applied to dental age estimation, highlighting their methodologies, performance, and advancements. Key aspects covered include the development and optimization of CNN architectures, the role of data preprocessing and augmentation, and the impact of these technologies on predictive accuracy. Additionally, the review addresses critical ethical issues associated with the implementation of CNNs in dental age estimation, such as data privacy, potential biases, and the implications of automated decision-making in sensitive contexts. By integrating insights from current research and practical applications, this review provides a thorough understanding of both the technological innovations and ethical considerations driving progress in determining dental age. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploration of the Impact of Remote work and Online Education on Visual Health during the COVID-19 using Machine Learning Approaches 14

Exploration of the Impact of Remote work and Online Educatio...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shanmukha Aditya, G. Kruthika, B. Venkata Sneha, G. Gupta, Deepa Smitha, T.V. Manek, Asha S. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Mathematics Bengaluru India T. John Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

The COVID-19 pandemic has been associated with several manifestations that affect an individual's visual health. To address these concerns, we propose a classification model to predict the impact of COVID-19 on vision. The model is built on a dataset corresponding to visual symptoms and other factors. The proposed classification model will classify people into three categories: those with no impact on vision, those with mild-to-moderate impact, and those with severe impact. Some machine learning algorithms, such as logistic regression, decision trees, random forests, etc., were applied by us to build the model to identify the optimal algorithm for the task. The results obtained from the model show that it has high accuracy, precision, and recall. The model can predict the severity of visual symptoms in people due to COVID-19 with an accuracy of over 84%. The study findings are entirely focused on the E-learning or online learning caused due to COVID-19 pandemic and their impact on vision of people from different age groups. © 2023 IEEE.

关键词： COVID-19

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