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International Conference on ICT For Smart Society (ICISS)

作者： Sri Rahayu Pulung Nurtantio Andono Pujiono Ahmad Zainul Fanani Department of Informatics Engineering Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia Computer Science Department Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts.

关键词： Accuracy Laplace equations Image analysis Image edge detection Noise Prediction algorithms Entropy Computational efficiency Particle swarm optimization Optimization

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Nitinol Based heat Engine for Solar Energy Harvesting

Nitinol Based heat Engine for Solar Energy Harvesting

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International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Shivam Nitin Satpute Sameer Ajay Sake Pavitra Praveen Sardesai Parth Sandip Sawant Apurv Vaibhav Sarode Varad Gangadharrao Sadegaonkar Marek Iwaniec Ramesh Narina Anita Dombale Nishant S. Kulkarni Mechanical Engineering Department Vishwakarma Institute of Technology Pune India Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science & Technology Krakow Poland

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

The most abundantly available renewable energy source on the earth is solar energy. Conventionally, this energy is converted into electric energy with the help of photovoltaic cells. The efficiency of this commercially available technology is around 5-25%. This article deals with the investigation of an innovative energy harvesting system that uses a Shape Memory Alloy (SMA) to utilize solar heat for generating electric power. SMA shows significant deflection when heated and this characteristic is used to develop a SMA based heat engine. This work proposes a heat engine where a SMA wire is used to convert solar radiation to perform mechanical work. Further, the mechanical work is used to drive a rotary electric generator to produce electric energy. The SMA used for this purpose is Nitinol wire, which absorbs heat from the solar radiation and transforms the same initially to mechanical motion, which later is used to produce electric power. Initially, the SMA element is curved in shape, and it shows a significant deflection when it receives the heat from solar radiation. This deflection of the SMA element is used to operate a rotary electric generator to produce electric energy. The theoretical calculations for balance of the energy flow from incident solar radiations to output electric energy is discussed. The developed prototype shows around 4.5% efficiency with an average power of 2.4 mW.

关键词： Heat engines Solar energy Electric generators Voltage Transforms Power systems Wire

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Semantic Search Engine, Thai Traditional Medicine Knowledge Graph Application

Semantic Search Engine, Thai Traditional Medicine Knowledge ...

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13th International Electrical engineering Congress, iEECON 2025

作者： Sakulpakdee, Natthinan Narischat, Nattavee Srisomboon, Kanabadee Lee, Wilaiporn Pipanmekaporn, Luepol Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Apply Science Department of Computer and Information Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

In this paper, a semantic information retrieval framework is presented to improve the precision of search results by concentrating on the context of concepts is presented. Instead of the keyword matching technique, the graph theory is used for leveraging the relationships and meanings of search terms. Information is represented as a knowledge graph for semantic searching performed by the inference engine and results are passed to the NLP technique for interpreting entity, relationship and property to their relevancy query. In the current system, we focused on healthcare system to helping the foundation recommendations of Thai Traditional Medicine. Once Thai Traditional Medicine contains a numerous of words which have the same meaning, the traditional keyword based cannot retrieve all relevant document. In our proposed semantic architecture, the query is performed an context-based understanding for retrieving all relevant document, therefore, it can retrieve the same number of documents for the queries which have the same meaning. © 2025 IEEE.

关键词： Online searching

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Deep Learning Approaches for Precision Agriculture: Weed and Cotton Crop Classification Using YOLO and Faster RCNN

Deep Learning Approaches for Precision Agriculture: Weed and...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Asad Majeed Fatima Faridoon Muhammad Irfan Ahmar Rashid Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi Pakistan Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learning-based object detection models - YOLO and Faster R-CNN - for automated weed detection in cotton fields. We developed and annotated a custom dataset comprising field images captured under various environmental conditions, labelled in both COCO and YOLO formats using Label Studio. Performance evaluation revealed the YOLO model’s superior capabilities, achieving a mAP50 of 0.775, precision scores of 0.859 for cotton and 0.656 for weed detection, and recall rates of 0.849 and 0.543 for cotton and weed respectively on the test dataset. In comparison, Faster R-CNN showed lower performance with AP scores of 0.708 for cotton and 0.269 for weed detection, particularly struggling with objects of varying sizes. The YOLO model maintained consistent performance across both validation and test datasets, with validation metrics showing mAP50 of 0.982 and mAP50-95 of 0.829. These results establish YOLO’s effectiveness as a reliable tool for automated weed detection in cotton fields, offering a practical solution for precision agriculture applications. The developed model demonstrates the potential for integration into automated crop management systems, contributing to more efficient and targeted weed control strategies.

关键词： YOLO Precision agriculture Deep learning Productivity Accuracy Crops Data models Cotton Reliability Water resources

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Defect Detection of Agricultural Commodities using Image Processing and Artificial Neural Networks

Defect Detection of Agricultural Commodities using Image Pro...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Suprijono, Herwin Kartikadarma, Etika Yusianto, Rindra Marimin, Marimin Dian Nuswantoro University Faculty of Engineering Department of Electrical Engineering Semarang53192 Indonesia Dian Nuswantoro University Study Program of Information Technology Faculty of Computer Science Semarang53192 Indonesia Dian Nuswantoro University Faculty of Engineering Department of Industrial Engineering Semarang53192 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology West Java Bogor16002 Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Production losses of agricultural commodities on agricultural land due to product defects depend on the level of pest and disease attacks. Defects cause the product not to be harvested or rejected by the market. Data from the Ministry of Agriculture of the Republic of Indonesia in 2021 shows the percentage of defective products for this commodity is 5%. This study aims to detect defects in agricultural products using image processing and artificial neural networks. We used the screening threshold method with potato research samples from Dieng, Indonesia. The study results for spot defects showed that from 100 training data, there were five incorrect identification data, so the accuracy of the training process was 95%. At the same time, the hole defect accuracy is better, which is 97%. It shows that defects in agricultural commodities can be detected using this method. For further research, the development of agricultural product sorting can combine sorting techniques with this method. © 2022 IEEE.

关键词： Defects

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Study of Photonic Platforms for On-Chip Refractive Index Sensing

Study of Photonic Platforms for On-Chip Refractive Index Sen...

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Photonics North

作者： Raghi El Shamy Mohamed A. Swillam Xun Li Department of Electrical and Computer Engineering Faculty of Engineering McMaster University Hamilton Canada Department of Physics School of Science and Engineering The American University in Cairo New Cairo Egypt

ISBN: (数字)9798350366365

ISBN: (纸本)9798350366372

Here we present a rigorous study for the different integrated photonic platforms that can be used for on-chip refractive index (RI) sensing. The study includes the widespread silicon photonics platform, the silicon nitride platform and the silica platform. We compare these three platforms according to five parameters that determine the performance and reliability of the RI sensor. Finally, we conclude with the optimum platform for on-chip RI sensing according to the available technology and the aimed application.

关键词： Refractive index Silicon nitride Silicon photonics System-on-chip Sensors Reliability

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Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis

Predictive Modeling of Portuguese Student Performance: Compa...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the 'Student Performance in Portuguese Secondary education' dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction. © 2024 IEEE.

关键词： Contrastive Learning

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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A Comprehensive Review of Bio-Medical Portable Devices for Blood Coagulation Detection Device

A Comprehensive Review of Bio-Medical Portable Devices for B...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Joy Bore Trishul Dhale Praveen Kumar Gauri S. Deshmukh Department of Computer Science and Medical Engineering Faculty of Engineering and Technology DMIHER (DU) Wardha Maharashtra Department of Applied Chemistry Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The paper provides a synoptic view of portable biomedical point-of-care devices for blood coagulation detection, emphasising the state-of-the-art technology adopted and its use in the medical industry. These devices give fast and precise results at the point of care and are indispensable for diagnosing coagulation and perceiving thrombotic states. It brought into focus the issues of monitoring blood coagulation across different areas of practice, such as the ICU, operative and perioperative services, and ambulatory care. This paper also highlights new trends in the development of sensors, miniaturisation and the integration of digital health solutions. Further, it explains how these devices are advantageous and disadvantageous in terms of speed, sample requirement, standardisation, calibration, and user-friendliness. Last but not least, the regulation and market uptake issue is discussed, focusing on how portable coagulation detection devices can revolutionise patient care and access to healthcare. Finally, the authors state that the paper should inspire further research to develop new solutions and collaboration to address obstacles and underpin advancements in the field.

关键词： Industries Point of care Collaboration Coagulation Transforms Sensors Electronic healthcare Calibration Medical diagnostic imaging Testing

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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