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International Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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Computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Android Based Heart-rate Monitoring System using ECG Sensor 2

Android Based Heart-rate Monitoring System using ECG Sensor

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2nd International Conference on Sustainable Engineering and Creative computing, ICSECC 2020

作者： Rosalina Selina, Grace Mandala, Rila Faculty of Computing Information Technology Study Program President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175881

This research aims to help the user monitor their heart’s condition and notify other people in case of an emergency due to an abnormal heartbeat (normal heartbeat around 60-100 beats per minute). From the heart rate graph, the user could do a consultation with an expert in the nearest hospital using a map feature that will show the nearest hospital with a 1 KM radius from the user’s current location. The heart is undeniably an essential part of the human body system. The heart's missions are to pump blood to the lungs to expel the waste carbon dioxide that resulted from the respiration and pump blood that will deliver oxygen throughout the human body's circulation system. While the blood that carries oxygen being pumped throughout the human body, the heart will provide the oxygen to all cells. Body cells need to use oxygen to get the energy from food through a process called cellular respiration. Monitoring our heartbeat and pulse rate can increase people’s awareness about their heart’s health, motivating, and encouraging people to maintain a healthy eating plan and exercise. This research will be implemented ECG Sensor. The sensor will then send the data to the android application via HC-06 Bluetooth module. Monitoring the heart rate and analyzing the user’s heartbeat will be done in the android with a microcontroller device's help. © 2020 IEEE.

关键词： Electrocardiograms

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density

Classification Based on Texture Analysis for Synthetic Hazy ...

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International Conference on Electrical, Electronics and information Engineering (ICEEIE)

作者： Fresy Nugroho Puspa Miladin Safitri A. Basid Yunifa Miftachul Arif Norizan Mat Diah Firma Sahrul Bahtiar Hani Nurhayati Fachrul Kurniawan Nurizal Dwi Priandani Dodik Arwin Dermawan Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Malaysia Faculty of Science and Technology Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%.

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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Sodium-ion battery cycle life prediction using machine learning

Sodium-ion battery cycle life prediction using machine learn...

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International Conference on Green Energy, computing and Intelligent technology 2024 (GEn-CITy 2024)

作者： Linfeng Liu Filbert H. Juwono W. K. Wong Erick Purwanto Tingyan Jin Yuyang Zhen Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing People's Republic of China School of Instrument Science and Technology Beijing University of Technology Beijing People's Republic of China

ISBN: (数字)9781837242863

In this study, we explore the classification and prediction capabilities of three models—Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)—on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.

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Multi-agent System for the Area of Medical Emergencies for Health Surveillance (MAS-AMeHs) 1st

Multi-agent System for the Area of Medical Emergencies for H...

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1st International Conference on Advanced Research in Technologies, information, Innovation and Sustainability, ARTIIS 2021

作者： Bautista, Jorge Alanis, Arnulfo Patiño, Efraín Hernandez-Leal, Fabiola Master’s Program in Information Technology Department of Systems and Computing National Technology of Mexico Campus Tijuana Calzada del Tecnológico S/N Fracionamiento Tomas Aquino Tijuana B.C.22414 Mexico Department of Systems and Computing National Technology of Mexico Campus Tijuana Calzada del Tecnológico S/N Fraccionamiento Tomas Aquino Tijuana B.C.22414 Mexico School of Medicine Autonomous University of Baja California Tijuana B.C. Mexico

ISBN: (纸本)9783030902407

In this paper, it is proposed an APK that works in conjunction with an emergency triage system that may help both patients and doctors using IoT. The classification system implemented on this work reads the data provided by the user (oxygen saturation, hearth rate) on the app, this will be processed, as the results are given in real time and classified on an emergency color system (green, yellow, orange and red) the doctor would be notified instantly, allow him to be up to date with its patient’s condition and help him get a better analysis and have a mor exact diagnose, this being beneficial for both patient and doctor. © 2021, Springer Nature Switzerland AG.

关键词： Oxygen

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Mobile application to track people in covid19 monitoring and patients under covid19 supervision 2

Mobile application to track people in covid19 monitoring and...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Chrysler, A. Warnars, H.L.H.S. Utomo, W.H. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Magister Science of Information Technology Faculty of Computing President University Bekasi17550 Indonesia

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients. © Published under licence by IOP Publishing Ltd.

关键词： Mobile computing

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