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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Asadi, Faisal Elwirehardja, Gregorius Natanael Trinugroho, Joko Pebrianto Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia. © 2023 IEEE.

关键词： Medical informatics

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： Machine learning

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Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source computer Vision Library).The system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Machine Learning Approach for Single Nucleotide Polymorphism Selection in Genetic Testing Results 8

Machine Learning Approach for Single Nucleotide Polymorphism...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Isnan, Mahmud Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Lactose intolerance is a type of digestive problem that may threaten the population because milk and dairy products compose of nutrients that are essential for human body. Genetic tests possess a great potential to detect lactose intolerance as it can be used in children and even infants. However, a new approach to analyze the genetic test results is needed to elucidate the Single Nucleotide Polymorphisms (SNPs) that are related to lactose intolerance. In this work, we utilized the machine learning based feature selection to select the SNPs associated with lactose tolerance trait from genotyping samples of direct-to-customer (DTCG genetic tests, obtained from the public database. Recursive Feature Elimination (RFE) with XGBoost model was used to perform feature selection. We also compared three different models, such as XGBoost, support vector machine (SVM), and random forest (RF) for training the selected features. Our findings revealed that 20 SNPs (out of 3501) were chosen, with rs4394668 as the most important variables (F-score 0.009). Furthermore, when compared to the RF and SVM models, the XGBoost model had the highest accuracy (0.87). Further studies should be undertaken to elucidate how the selected SNPs may lead to the lactose intolerance trait. © 2023 The Authors. Published by Elsevier B.V.

关键词： feature selection genetic test lactose intolerance SNP

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Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6723-6738页

作者： Qin, Lutong Zhang, Lei Li, Chengrun Song, Chaoda Cheng, Dongzhou Wang, Shuoyuan Wu, Hao Song, Aiguo Nanjing Normal University School of Electrical and Automation Engineering Nanjing210023 China Shanghai200240 China Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States University of Macau Department of Computer and Information Science Taipa999078 China Yunnan University School of Information Science and Engineering Kunming650500 China Southeast University Department of Instrument Science and Engineering Nanjing210096 China

Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. © 2020 IEEE.

关键词： Budget control

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ML) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ML algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Application of Machine Learning in Mental Diseases Diagnosis 25

Application of Machine Learning in Mental Diseases Diagnosis

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alsawi, Rahaf Alazaidah, Raed Samara, Ghassan Alzoui, Haneen Alidmat, Omar Elsoud, Esraa Abu Talat Khouj, Mohammed Katrawi, Anwar Alluwaici, Mo'Ath Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient's way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person's mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method. © 2024 IEEE.

关键词： Adversarial machine learning

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The Implementation of Integration of ML Algorithm for the Design of Stability System for Grid System

The Implementation of Integration of ML Algorithm for the De...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Chandani, Priyanka Vijay, Sandip Soumya, K. Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science Bangalore India

ISBN: (纸本)9798350357769

A modern decentralized electric grid is a groundbreaking system that integrates demand response effortlessly and doesn't need major infrastructure changes. Within the decentralized domain, users independently control their power consumption according to the frequency of the grid. This is made possible by the use of reasonably priced devices like smart meters, which allow grid frequency to be measured from almost anywhere. Different data-level resampling strategies have been used to address the problem of data imbalance, while data normalization approaches have been used to reduce biased behavior among characteristics. The findings clearly show that in terms of the performance of classifiers, a balanced dataset performs better than an unbalanced one. Specifically, for unbalanced datasets, oversampling approaches are more effective than under sampling ones. With a precision level of 94.7 percent, the XGBoost algorithm is the best performer within the range of deep learning algorithms that are taken into consideration. Remarkably, XGBoost's accuracy forecast rises to 96.8% when paired with random oversampling. This improved model manages the volatility of renewable energy supplies and maximizes their use by accurately forecasting frequency variations in decentralized power networks. This model's predictive capacities have great potential to support the stability of distributed electricity grids, which will improve the distribution and administration of energy on a larger scale. © 2023 IEEE.

关键词： data Normalization data Resampling Decentralized Power Grid Demand Response and Classifiers Performance Energy Policy Grid Stability Machine Learning Renewable Energy Integration Renewable Sources Smart Meters Stability Prediction XGBoost Algorithm

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Out-of-Distribution Detection with a Single Unconditional Diffusion Model 38

Out-of-Distribution Detection with a Single Unconditional Di...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Heng, Alvin Thiery, Alexandre H. Soh, Harold Department of Computer Science National University of Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Smart Systems Institute National University of Singapore Singapore

Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. However, such approaches require a new model to be trained for each inlier dataset. This paper explores whether a single model can perform OOD detection across diverse tasks. To that end, we introduce Diffusion Paths (DiffPath), which uses a single diffusion model originally trained to perform unconditional generation for OOD detection. We introduce a novel technique of measuring the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. Extensive experiments show that with a single model, DiffPath is competitive with prior work using individual models on a variety of OOD tasks involving different distributions. Our code is publicly available at https://***/clear-nus/diffpath. © 2024 Neural information processing systems foundation. All rights reserved.

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The Significance of Utilizing the dependencies among Labels in Multi Label Classification 2

The Significance of Utilizing the dependencies among Labels ...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzoubi, Haneen Alzyoud, Mazen Alazaidah, Raed Al-Shanableh, Najah Khafajah, Hayel Almatarneh, Sattam Al-al-Bayu University Department of Computer Science Mafraq Jordan Ai Zarqa University Department of Data Science Zarqa Jordan

ISBN: (纸本)9798350373363

Multi-Label Classification (MLC) is a general type of classification that attracted scholars in the last few years. It imposes a high challenge since the problem search space of MLC is very large and follows an exponential function of growth. Moreover, the accuracy of classification in MLC is still very low when compared to the accuracy of Single Label Classification (SLC). Consequently, many scholars and researchers proposed to utilize and exploit the dependencies among class labels in to minimize the size of the problem search space of MLC, and hence, improve the accuracy of the classification task. Unfortunately, very few studies address this issue and attempt to discover the benefits of utilizing and exploiting the dependencies among labels in the domain of MLC. Therefore, this research attempts to identify the significance of discovering and utilizing these dependencies, with respect to three evaluation metrics designed specifically for MLC, and considering four different multi label datasets. The results revealed the clear significance of discovering and utilizing high order dependencies among labels in MLC, especially with high cardinality datasets. © 2023 IEEE.

关键词： Classification (of information)

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