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Digital Twin (DT) Smart City for Air Quality Management

Procedia computer science 2023年 227卷 524-533页

作者： Dedy Ariansyah Mahmud Isnan Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale.

关键词： Digital Twin Smart City Mixed Reality Augmented Reality

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Data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors

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Procedia computer science 2023年 216卷 284-292页

作者： Faisal Asadi Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia.

关键词： typhoid fever salmonella typhi endemic disease meteorological factors

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks 25

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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25th IEEE Wireless Communications and Networking Conference, WCNC 2024

作者： Zhao, Songhan Long, Yusi Gu, Bo Luong, Nguyen Cong Lyu, Bin Gong, Shimin School Of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus China Guangdong Provincial Key Laboratory Of Fire Science And Intelligent Emergency Technology China Phenikaa University Faculty Of Computer Science Viet Nam School Of Communications And Information Engineering Nanjing University Of Posts And Telecommunications China

ISBN: (纸本)9798350303582

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode. © 2024 IEEE.

关键词： Antennas

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm 15

Psychological Stress Classification Using Extreme Gradient B...

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15th International Conference on information and Communication technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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Cross-Layer Hidden Markov Analysis for Intrusion Detection

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computers, Materials & Continua 2022年第2期70卷 3685-3700页

作者： K.Venkatachalam P.Prabu B.Saravana Balaji Byeong-Gwon Kang Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Information Technology Lebanese French UniversityErbil44001KRIraq Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link *** the issues above,route failure is prevalent in ad hoc mobile cloud computing networks,which increases energy consumption and delay and reduces *** issues may affect several interconnected nodes in an ad hoc mobile cloud computing *** address these weaknesses,which raise many concerns about privacy and security,this study formulated clustering-based storage and search optimization approaches using cross-layer *** proposed approaches were formed by cross-layer analysis based on intrusion detection ***,the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing ***,delay,energy consumption,network lifetime,and link accomplishment are highly addressed by the proposed *** hidden Markov model is used to maintain the data transition and distributions in the *** data communication network,like ad hoc mobile cloud computing,faces security and confidentiality ***,the main security issues in this article are addressed using the storage and search optimization ***,the new algorithm developed helps detect intruders through intelligent cross layer analysis with theMarkov *** proposed model was simulated in Network Simulator 3,and the outcomes were compared with those of prevailing methods for evaluating parameters,like accuracy,end-to-end delay,energy consumption,network lifetime,packet delivery ratio,and throughput.

关键词： Data transition end-to-end delay energy consumption flexibility hidden Markov model intrusion detection link optimization packet delivery ratio privacy security searching throughput

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Long short-Term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control 7

Long short-Term memory (LSTM) model-based reinforcement lear...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Wijaya, Santo Heryadi, Yaya Arifin, Yulyani Suparta, Wayan Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta55281 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： Model predictive control

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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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Informatics Engineering, science & technology (INCITEST), International Conference on

作者： Masmur Tarigan Ford Lumban Gaol Tuga Mauritsius Widodo Budiharto Computer Science Department / Computer Science Graduate Program Esa Unggul University/Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Department of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process.

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Sujan Shrestha Yoji Taniguchi Tetsuo Tanaka Course of Information and computer sciences Graduate school of Engineering Kanagawa Institute of Tehnology Atsugi Japan Department of Information Artificial Intelligence and Data Science Daiichi Institute of Technology Tokyo Japan Dept. of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology Atsugi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence; however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset.

关键词： Deep learning Recurrent neural networks Accuracy Surveillance Benchmark testing Feature extraction Convolutional neural networks Security Informatics Long short term memory

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Alita, Debby Ahmad, Imam Jafar Rumandan, Rhaishudin Erkamim, Moh Widyasmoro Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Institut Agama Islam Negeri Ambon Islamic Education Management Study Program Ambon Indonesia Universitas Tunas Pembangunan Smart City Information Systems Study Program Surakarta Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data. © 2024 IEEE.

关键词： Classification Data Mining Decision Tree Stunting

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