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2023 International Conference on Networking, Electrical engineering, computer Science, and Technology, IConNECT 2023

作者： Sirait, Tamsir Hasudungan Lumban Gaol, Ford Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology. © 2023 IEEE.

关键词： Information management

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Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset

Evaluation of Convolutional Neural Network for Automatic Car...

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2022 International Conference on Data and Software engineering, ICoDSE 2022

作者： Fariza, Arna Asmara, Rengga Rojaby, Muhammad Oktavian Fajar Astuti, Eha Renwi Putra, Ramadhan Hardani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Department of Dentistry Surabaya Indonesia

ISBN: (数字)9798350397055

ISBN: (纸本)9798350397055

Dental caries or tooth decay is damage to the hard tissues of the teeth that can occur in the enamel, dentin, and cementum areas. Panoramic radiography is a screening tool for tactile or visual examination of the oral cavity which is useful for further diagnosis and treatment. The process of segmentation of panoramic radiographs is a difficult process because there is no homogeneity between panoramic images with one another. Noise levels, vertebral column images, and low contrast are the main challenges in image processing. This study evaluates CNN to detect caries automatically on panoramic radiographs on a small dataset. The dataset consisted of manually cropped maxillary and mandibular premolars and molars. An augmentation strategy consisting of horizontal flip, vertical flip, and affine transformation is used to produce a wider variety of images. This study compares the architecture of non-pretrained and pretrained models consisting of 3-layer CNN, 3-layer CNN with batch normalization, ResNet18, and ResNeXt50 32×4d. Evaluation was carried out on 400 training data and 76 testing data. Combination of augmentation strategies and pre-trained ResNet18 and ResNeXt50 32×4d achieves high accuracy compared to other models. © 2022 IEEE.

关键词： Image segmentation

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Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

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computer Systems Science & engineering 2023年第9期46卷 3831-3845页

作者： Mohammed Jasim Mohammed Jasim Shakir Fattah Kak Zainab Salih Ageed Subhi R.M.Zeebaree Engineering College Al-Kitab UniversityKirkukIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Engineering Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such ***-drug interaction(DDI)significantly contributed to drug administration and *** continues as the main obstacle in offering inexpensive and safe *** normally happens for patients with extensive medication,leading them to take many drugs *** may cause side effects,either mild or severe health *** reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery *** efforts were made to formulate new methods for DDI prediction to overcome this *** this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data *** the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for *** input data is preprocessed at the primary level to improve its ***,the salp swarm optimization algorithm(SSO)is used to select *** this study,the deep belief network(DBN)model is exploited to predict the DDI *** SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the *** experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

关键词： Drug-drug interaction deep learning spotted hyena optimization feature selection classification

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Diabetes Classification Problem with CatBoost Method and Optuna Gradient Boosting Optimization 6

Diabetes Classification Problem with CatBoost Method and Opt...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Kurniawan, Safar Dwi Purwono, Purwono Ma'arif, Alfian Suwarno, Iswanto Politeknik Harapan Bersama Department of Computer Engineering Tegal Indonesia Universitas Harapan Bangsa Department of Informatics Purwokerto Indonesia Universitas Ahmad Dahlan Department of Electrical Engineering Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Engineer Professional Program Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Diabetes mellitus is a dangerous global epidemic that can cause kidney failure, heart attack, blindness, and death. The main cause of this disease is too high blood sugar levels due to the irrelevant work of pancreatic beta cells. Rapid computer technology advances and implementations based on artificial intelligence have penetrated various fields, especially the health sector. Data mining and machine learning are technologies that continue to grow, are reliable, and can be used as research support tools in the medical field. This technology is considered capable of helping to automate diabetes prediction. Based on related research, our contribution to this research is to utilize the CatBoost method with Optuna optimization. In the data preprocessing stage, all 768 data sets have gone through removing outliers, normalizing features to reducing dimensions. The dataset is divided into 85\% composition for training data and 15\% for test data. At the feature selection stage with Correlation Pearson's, we found that Glucose correlated with diabetes outcome or disease because it had a value range of 0.47. The results' accuracy is considered less than optimal because it only produces an accuracy value below 80%, even after optimization with Optuna. It can be seen that there was only an increase of 7\% after optimizing with Optuna. © 2023 IEEE.

关键词： Data mining

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Designing a Code Obfuscation Scheme for Software Protection

Designing a Code Obfuscation Scheme for Software Protection

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Azeta, Ambrose A. Awal, Abdulganiyu Azeta, Joy I. Hamunyela, Suama L. Santos, Albano dos Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Covenant University Department of Computer Science and Information Sciences Ota Nigeria Covenant University Department of Biological Sciences Ota Nigeria Namibia University of Science and Technology Department of Informatics Windhoek Namibia

ISBN: (纸本)9798331539733

Software piracy and intellectual property theft has brought about a financial decrease in revenue for the producers of software systems. Moreso, malicious end-users act as Man-At-The-End (MATE) attackers and dangerously inspect software codes either statically or dynamically to steal them. Developers need to create client applications that contain confidential information and data. As a result, large scale software firms constantly strive to secure hidden algorithms and vital programming components contained in their applications in order to have competitive advantage. The focus of this study is to design a code obfuscation scheme for software and intellectual property protection. The methodology employed includes use of programming tool and algorithms that described the key processes of the scheme. To demonstrate the strength and capability of our approach, user evaluation was conducted to arrive at the cost, potency and resilience of the scheme. Results indicate that majority of the respondents believe that the developed obfuscator is inexpensive and that it does not adversely affect memory and execution time. The current study would provide software developers the tool to obscure their intellectual property against theft. © 2024 IEEE.

关键词： Attackers intellectual property obfuscation scheme software protection

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Mobile Device Facial Beauty Prediction using Convolutional Neural Network as Makeup Reference

Mobile Device Facial Beauty Prediction using Convolutional N...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Luthfi, Muhammad Rachmadi, Reza Fuad Purnama, I Ketut Eddy Nugroho, Supeno Mardi Susiki Institut Teknologi Sepuluh Nopember Faculty of Electrical and Intellignet Informatics Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Beauty or good looks is one aspect of human attraction. Highly attractive people have an 8 to 12 % higher success in negotiations. One of the things that can be used to increase attractiveness is makeup or cosmetics. Makeup can be used alone or through the services of a Makeup Artist (MUA). MUA services are often used during important and once-in-A-lifetime events such as weddings. The difference in beauty before and after using this makeup cannot be measured but only seen and assessed manually. One application of machine learning in this field is Facial Beauty Predictions (FBP). FBP measures the value of a person's facial beauty or good looks. This paper investigated and implemented FBP on mobile devices, which makes it easier to assess anytime and anywhere. We investigated FBP models using five CNN architectures, including EfficientNetB0, VGG-16, ShuffleNet, MobileNet, and Inception. After evaluation, we choose MobileNetV2 CNN architecture as the primary model for mobile application FBP. Experiments on the FBP dataset show that MobileNetV2 achieves Pearson Correlation 0.7893 with only 10.5 MB for model file size. The model implemented for mobile devices works well in 13 different tests. © 2022 IEEE.

关键词： Forecasting

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Gray-Level Co-Occurrence Matrix and Geometric Shape for Classification of Rubber Tree Maturity 2

Gray-Level Co-Occurrence Matrix and Geometric Shape for Clas...

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2nd International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022

作者： Saputra, Wanvy Arifha Ahyati, Inayatul Ulya Yunanto, Andhik Ampuh Noor, Syamsudin Asyikin, Arifin Noor Permadi, Dimas Fanny Hebrasianto Politeknik Negeri Banjarmasin Department of Informatics Engineering Banjarmasin Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Politeknik Negeri Banjarmasin Department of Electrical Engineering Banjarmasin Indonesia Politeknik Negeri Banjarmasin Department of Smart Cities Information System Banjarmasin Indonesia Institut Teknologi Telkom Purwokerto Department of Informatics Engineering Purwokerto Indonesia

ISBN: (纸本)9781665455640

Rubber trees can be said to be mature by having a trunk circumference of more than 45 cm at the height of 130 cm from the ground. It influences the use of digital imagery to determine the classification of 'mature rubber trees' and 'immature rubber trees'. The challenge in the image of rubber trees is that they have similar colour characteristics between tree trunks and the ground, and multi-object rubber trees in one picture. We propose a method using the gray-level co-occurrence matrix (GLCM) and geometric shape for the classification of Rubber Tree Maturity. GLCM is used to measure neighbouring pixels with grey intensity, distance, and angle in solving the first characteristic problem. Geometric shapes used to solve the second characteristic problem with the help of determining a region of interest (ROI). The research results show that the proposed method was successfully carried out with the strongest evidence on the support vector machine (SVM), namely 0.800 f1-score, 0.778 precision and 0.824 recall. © 2022 IEEE.

关键词： Support vector machines

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Implementation of Augmented Reality in Medical Education 24

Implementation of Augmented Reality in Medical Education

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24th International Electronics Symposium, IES 2022

作者： Syahry, Alfis Basuki, Dwi Kurnia Sukaridhoto, Sritrusta Budiarti, Rizqi Putri Nourma Hanifati, Kirana Muntahir, Ivan Politeknik Elektronika Negeri Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

The Covid-19 pandemic has hit Indonesia since the beginning of 2020 and is still continuing. The education sector has experienced a considerable impact, face-to-face learning must be replaced with all-online activities. Many problems arise from online learning, such as inadequate infrastructure, an ineffective learning system and difficulties in understanding the material. Medical education has an impact on the current situation because most of the learning uses infrastructure and direct practice, it is necessary to have alternative learning methods that can improve the quality of learning. Currently, video conference media technology can assist learning activities so that instructors can still explain material to students through virtual media. For medical students who need practice and infrastructure facilities, this technology is still not enough to support their learning media. The idea came up 'Application of Learning Module in Health Field based on Extended Reality (sub chapter: Augmented Reality)' This application combines Virtual Reality, Augmented Reality, and Mixed Reality. Augmented reality is a technology that is suitable to be implemented in medical learning because students do not need to prepare anything to perform simulations, just devices and internet networks. For feeding and drinking to the elderly, some items are needed to start the practicum, with this augmented reality there is no need to prepare all of these items, and also reduce contact with other people during a pandemic like this. Users can connect to each other in different devices and environments according to their respective devices. In this application, Augmented Reality acts as a technology that combines the real and virtual worlds by adding virtual objects into the real world. Users can use devices that have cameras such as cellphones or laptops so they can see virtual objects. This virtual object can be seen by all users and each user is able to interact with it in real time. As fo

关键词： Medical education

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Classes versus Communities: Outlier Detection and Removal in Tabular Datasets via Social Network Analysis (ClaCO) 14

Classes versus Communities: Outlier Detection and Removal in...

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Ucer, Serkan Ozyer, Tansel Alhajj, Reda Ankara Turkey Ankara Medipol University Department of Computer Engineering Ankara Turkey Istanbul Medipol University Department of Computer Engineering Istanbul Turkey University of Southern Denmark Department of Health Informatics Odense Denmark

ISBN: (纸本)9781665456616

In this research, we introduce a model to detect inconsistent & anomalous samples in tabular labeled datasets which are used in machine learning classification tasks, frequently. Our model, abbreviated as the ClaCO (Classes vs. Communities: SNA for Outlier Detection), first converts tabular data with labels into an attributed and labeled undirected network graph. Following the enrichment of the graph, it analyses the edge structure of the individual egonets, in terms of the class and community belongings, by introducing a new SNA metric named as 'the Consistency Score of a Node-CSoN'. Through an exhaustive analysis of the ego network of a node, CSoN tries to exhibit consistency of a node by examining the similarity of its immediate neighbors in terms of shared class and/or shared community belongings. To prove the efficiency of the proposed ClaCO, we employed it as a subsidiary method for detecting anomalous samples in the train part in the traditional ML classification task. With the help of this new consistency score, the least CSoN scored set of nodes flagged as outliers and removed from the training dataset, and remaining part fed into the ML model to see the effect on classification performance with the 'whole' dataset through competing outlier detection methods. We have shown this outlier detection model as an efficient method since it improves classification performance both on the whole dataset and reduced datasets with competing outlier detection methods, over several known both real-life and synthetic datasets. © 2022 IEEE.

关键词： Anomaly detection

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