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2nd International Conference on computer System, information technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation 10

A Systematic Literature Review of Recent Trends and Challeng...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Arin, Ikrar Adinata Meyliana Warnars, Harco Leslie Hendric Spits Murad, Dina Fitria Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350339543

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology. © 2023 IEEE.

关键词： Decision making

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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A Machine Learning Based Funding Project Evaluation Decision Prediction

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computer Systems science & Engineering 2023年第5期45卷 2111-2124页

作者： Chuqing Zhang Jiangyuan Yao Guangwu Hu Xingcan Cao Economic Research Institute Beijing Language and Culture UniversityBeijing100083China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Computer Science Shenzhen Institute of Information TechnologyShenzhen518172China Faculty of Arts University of British ColumbiaVancouverCV6T 1Z1Canada

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human *** this paper,we apply machine learning to the field of funding allocation decision making,and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision?and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding?Since funding data is characterized by imbalanced data distribution,we propose a slacked weighted entropy decision tree(SWE-DT).We assign weight to each class with the help of slacked *** experimental results show that the SWE decision tree performs well with sensitivity of 0.87,specificity of 0.85 and average accuracy of *** also provides a satisfied classification accuracy with Area Under Curve(AUC)=*** implies that the proposed method accurately classified minority class instances and suitable to imbalanced *** adding evaluator factors into the model,sensitivity is improved by over 9%,specificity improved by nearly 8%and the average accuracy also increased by 7%.It proves the feasibility of using evaluators’characteristics as *** by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators,it enriches the literature in the field of decision making and machine learning field.

关键词： Expert evaluation prediction machine learning grant allocation

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MobileViTv2-MLP: Arabic Traffic Sign Recognition with Enhanced Lightweight Vision Transformers 17

MobileViTv2-MLP: Arabic Traffic Sign Recognition with Enhanc...

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17th International Conference on Machine Vision, ICMV 2024

作者： Alqahtani, A. Alqaysi, E. Alsarhani, W. Lee, C.P. Alsharafi, A. Alzahrani, M. Almosa, A. Computer Science Department King Khalid University Saudi Arabia King Khalid University Saudi Arabia Faculty of Information Science and Technology Multimedia University Malaysia English Department King Khalid University Saudi Arabia

ISBN: (纸本)9781510688278

Traffic Sign Recognition (TSR) plays a pivotal role in advanced driver-assistance systems (ADAS) and autonomous vehicles, serving as a fundamental element for safe and efficient road navigation. Due to a diverse array of factors such as variations in sign appearance, fluctuating lighting conditions, occlusions, and environmental influences, the task of TSR poses significant challenges. These challenges have been addressed with advanced deep learning techniques, particularly with Convolutional Neural Networks (CNNs) and other architectures proficient in handling visual data. While such approaches have shown promise, they are often limited in their ability to effectively manage the aforementioned challenges. To overcome these obstacles, we propose a novel approach called the MobileViTv2-MLP method. This approach aims to develop a resilient model specifically tailored to the task of identifying and categorizing traffic signs encountered during driving. Our methodology involves training the model on an extensive dataset comprising diverse representations of traffic signs under varying conditions, ensuring its adaptability and reliability in real-world scenarios. Through experimentation and evaluation, our refined approach has demonstrated exceptional accuracy, achieving a remarkable 99.75%. This outcome underscores the robustness and effectiveness of our proposed method in addressing the challenges inherent in TSR, thereby contributing to the advancement of intelligent transportation systems. © 2025 SPIE.

关键词： Advanced driver assistance systems

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Heart Disease Prediction with 100% Accuracy, Using Machine Learning: Performance Improvement with Features Selection and Sampling 8

Heart Disease Prediction with 100% Accuracy, Using Machine L...

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8th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Sabah, Fahad Chen, Yuwen Yang, Zhen Raheem, Abdul Azam, Muhammad Sarwar, Raheem Beijing University of Technology Faculty of Information Technology Beijing100124 China Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Manchester Metropolitan University OTEHM Manchester United Kingdom

ISBN: (纸本)9798350317923

Heart diseases are commonly diagnosed by using the technique of;angiography which is time consuming procedure. Therefore, researchers have been encouraged to find out the alternatives like;machine learning (ML). In this research work, we present a method to predict the heart disease using correlation based on feature subset selection and sampling. Different ML algorithms have been used in this research to model and cross validate. The dataset consists of 14 features and 1000 records of heart disease patients. Two sampling techniques;Oversampling and Synthetic Minority Optimization Technique (SMOTE) have been used. In case of oversampling, the Random Forest (RF) algorithm achieved highest prediction accuracy i.e., 99.20%. Proposed model with SMOTE improved the prediction accuracy of all algorithms to 100% except k-nn, which is also improved 18.16%. In this study the features selection has been made on the basis of correlation, in future we plan to tap in other feature selection methodologies. Furthermore in this study cross validation have been used to address the issue of over-fitting, this work can be extended by using other techniques like;Federated Learning to address the subjected problem. © 2023 IEEE.

关键词： Feature Selection

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Experiments to Review Literature on Topic Trends in technology Development in Educational information Systems 7

Experiments to Review Literature on Topic Trends in Technolo...

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7th International Conference on information technology, information Systems and Electrical Engineering, ICITISEE 2023

作者： Santiko, Irfan Soeprobowati, Tri Retnaningsih Surarso, Bayu Universitas Amikom Purwokerto Faculty of Computer Science Informatic Departement Indonesia School of Post Graduate Diponegoro University Information System Departement Semarang Indonesia School of Post Graduate Diponegoro University Faculty of Science and Mathematic Semarang Indonesia

ISBN: (纸本)9798350382266

The smart campus is a phenomenon that occurs in the industrial era 4.0. Many people understand that a smart campus is a campus where all resources can work and produce learning with high effectiveness and maximum efficiency. Everyone talks about elements of information technology that play a role in the sustainability of smart campuses. However, what actually makes the definition of a smart campus itself is not defined with certainty. This is because the world of education itself is experiencing major disruption. This causes innovation in the world of education, especially learning, to pay less attention to the actual aspects of education. This means that education has relied too much on technology which will actually cause a shift in the essence of education itself. In this paper, we will review how effective and efficient learning concepts actually are using information technology. The method used is an analysis of the literature review with an experimental survey and bibliometric analysis. As a result, it will be known that information technology that is in accordance with the essence of education is a matter that does not only focus on technical aspects but also needs to pay attention to aspects of methods, materials, and media. So that the results that are known will provide insight into the development of learning models on an ongoing basis according to the true essence of education. © 2023 IEEE.

关键词： Learning systems

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Acquiring Automation and Control Data in The Manufacturing Industry: A Systematic Review 8

Acquiring Automation and Control Data in The Manufacturing I...

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

作者： Sama, Andi Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Faculty of Computer Science Universitas Indonesia Depok16424 Indonesia

Industry 4.0 has driven the need for information technology (IT) & Operational technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure. © 2023 The Authors. Published by Elsevier B.V.

关键词： Data Acquisition Industry 4.0 IT/OT convergence Manufacturing MES Operational technology PLC SCADA

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Bioacoustics Signal Classification Using Hybrid Feature Space with Machine Learning 15

Bioacoustics Signal Classification Using Hybrid Feature Spac...

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15th International Conference on computer and Automation Engineering, ICCAE 2023

作者： Haider, Usman Hanif, Muhammad Kobayashi, Hiroki Parajuli, Laxmi Kumar Shimotoku, Daisuke Rashid, Ahmar Safeer, Sonia Gik Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Pakistan The University of Tokyo Information Technology Center Tokyo Japan

ISBN: (纸本)9798350396225

The vocal sounds emitted by animals and birds possess distinctive signatures. They are vital for acoustic monitoring to extract useful ecological data and track biodiversity. Recently, automated bioacoustics classification drew attention from the research community due to its diverse application. To that aim, we present a novel classification method for acoustic data by fusing optimally selected signal features. The proposed method extracts the distinctive statistical features, calculated using coefficients of Discrete Wavelet Transform (DWT), and fuses them with the descriptive features estimated using the Mel-Frequency Cepstral Coefficients (MFCC). The combined feature set is then passed to different machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) for sound classification of different animals and birds. The evaluation results show that the proposed method improves the classification accuracy and achieved high precision on all classifiers. © 2023 IEEE.

关键词： Birds

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