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International Conference of computer Science and information Technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Tracking Players in a Badminton Court by Two Cameras

arXiv

引用

arXiv 2023年

作者： Chou, Young-Ching Zhang, Shen-Ru Chen, Bo-Wei Chen, Hong-Qi Lin, Cheng-Kuan Tseng, Yu-Chee Department of Computer Science National Yang Ming Chiao Tung University Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taiwan

This study proposes a simple method for multi-object tracking (MOT) of players in a badminton court. We leverage two off-the-shelf cameras, one on the top of the court and the other on the side of the court. The one on the top is to track players’ trajectories, while the one on the side is to analyze the pixel features of players. By computing the correlations between adjacent frames and engaging the information of the two cameras, MOT of badminton players is obtained. This two-camera approach addresses the challenge of player occlusion and overlapping in a badminton court, providing player trajectory tracking and multi-angle analysis. The presented system offers insights into the positions and movements of badminton players, thus serving as a coaching or self-training tool for badminton players to improve their gaming strategies. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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South jakarta's air quality using PM 2.5 data at the beginning of COVID-19 Restriction 6

South jakarta's air quality using PM 2.5 data at the beginni...

引用

6th International Conference on information Management and Technology, ICIMTech 2021

作者： Rahutomo, Reza Purwandari, Kartika Hidayat, Alam Ahmad Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

As soon as the lockdown was established in Jakarta, Indonesia, people experienced a change in Jakarta's air quality. This research was conducted to answer several challenges such as: where to find Indonesia's air quality open data platforms, how to handle the acquired data with SQL technology, and how to validate whether the air quality of Jakarta was significantly changed from a period to another or not. With particulate matter 2.5 (PM 2.5) data acquired by The United States Diplomatic Post in South Jakarta, a data visualization was generated to describe the fluctuation of PM 2.5 from February 29th to June 29th. By conducting a two-sample t-test, a significant change in South Jakarta's air quality was revealed. The test concluded that South Jakarta's air quality got worse even after a lockdown was established. © 2021 IEEE.

关键词： COVID-19

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Smart cities in indonesia: A retrospective of the publication journey 6

Smart cities in indonesia: A retrospective of the publicatio...

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6th International Conference on information Management and Technology, ICIMTech 2021

作者： Purnomo, Agung Madyatmadja, Evaristus Didik Sianipar, Corinthias P. M. Nindito, Hendro Sano, Albert Verasius Dian Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Kyoto University Department of Global Ecology Kyoto606-8502 Japan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Research on smart cities in Indonesia continues to develop but is limited to one field and/or one year. From the retrospective journey of bibliometric analysis, this study purposes to visually research mapping and research trends in international publications on smart cities in Indonesia. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 518 scientific documents published from 2013 to 2020. According to the research, Universitas Indonesia, and Suhono Harso Supangkat had the most active affiliated organization and individual scientists in smart cities publications from Indonesia. computer Science, and IOP Conference Series Earth and Environmental Science were the most studied and disseminated outlets of smart cities publication. There were three category maps of collaborative researchers. Based on the identification of a collection of knowledge accumulated from over seven years of publication, this research proposes a grouping of smart cities publication themes in Indonesia: Government, Living, information management, Intelligent system, Mobility, and Environment, abbreviated as GLIIME publication themes. © 2021 IEEE.

关键词： Mapping

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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International Seminar on Application for Technology of information and Communication (ISemantic)

作者： Jasmine Iswarini Dyah Prabandari Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant.

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Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports

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International Journal of Transportation Science and Technology 2023年第4期12卷 1038-1051页

作者： Parisa Hosseini Seyedalireza Khoshsirat Mohammad Jalayer Subasish Das Huaguo Zhou Department of Civil and Environmental Engineering Center for Research and Education in Advanced Transportation Engineering Systems(CREATEs)Rowan University201 Mullica Hill RoadGlassboroNJ 08028USA Department of Computer&Information Sciences University of Delaware18 Amstel AveNewarkDE 19716USA Civil Engineering Program Ingram School of EngineeringTexas State UniversityRFM 5202601 University DriveSan MarcosTX 78666USA Harbert Center Department of Civil and Environmental EngineeringAuburn UniversityAuburnAL 36849USA

Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road *** being rare,crashes occurring due to WWD are more severe than other types of *** order to analyze WWD crashes,there is a need to obtain WWD incidents or crash ***,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash *** often involves large man-hours to review hardcopy of crash narratives in the police ***,it may cause an overestimation or underestimation of WWD crash *** fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning ***,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language *** this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD ***-validation and different performance metrics were used to evaluate the performance of each classification *** indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.

关键词： Wrong-Way Driving Crashes Crash Report Narratives Text Mining Text Classification

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

引用

arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Dental Disease Detection based on CNN for Panoramic Dental Radiographs

Dental Disease Detection based on CNN for Panoramic Dental R...

引用

IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Manar El Bagoury Mirna Al-Shetairy Omar Ahmed Samah S Mehanny Tassneem Hamdy Gihan Omar Marwan Akram Rana Mohamed Hebatullah Amr Walaa Gad Bioinformatics Program Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Oral Biology Department Faculty of Dentistry Cairo and Galala Universities Cairo Egypt Oral and Maxillofacial Radiology Department Faculty of Dentistry Cairo University Cairo Egypt Oral Radiology Department Faculty of Oral and Dental Medicine Future University in Egypt Cairo Egypt Information Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Panoramic dental radiography is one of the examination tools frequently performed in dental clinics and hospitals as it captures a wide range of the dental region and its surroundings, but due to the complex structure of the dental area and lack of time, dentists focus on tiny parts of the images. With the help of Machine Learning, dentists can detect dental defects and anomalies faster and more accurately. In this study, a data set of 733 panoramic radiographs of adult patients was collected from the faculty of Oral and Dental Medicine, Future University, Egypt. Using a combination of pre-trained CNN architecture and a finetuned network, the collected panoramic radiographs were used to determine the optimal architecture. The purpose of this research was to develop a computer-aided detection system based on a finetuned CNN algorithm, and to evaluate the accuracy and usefulness of this system for the detection of proximal and occlusal caries, interdental and inter radicular alveolar bone loss, periapical lesions, and impacted teeth in panoramic radiographs. Different object detection models were tested to detect selected dental diseases that were found in the panoramic radiographs, and after a few experiments, the YOLOv5 architecture was proved to be the most optimal after achieving 0.61 mAP@0.5 and 0.28 mAP@ [0.5-0.95] for the total six classes. This is the best-achieved results to our knowledge.

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Advancing Intellectual Property Rights through Digital Knowledge Collaboration Model: A Soft systems Methodology Approach

Advancing Intellectual Property Rights through Digital Knowl...

引用

International Conference on Informatics and Computing (ICIC)

作者： Novi Sofia Fitriasari Dana Indra Sensuse Deden Sumirat Hidayat Sofiyanti Indriasari Faculty of Computer Science Universitas Indonesia Depok Indonesia Marine Information Systems Study Program Universitas Pendidikan Indonesia Bandung Indonesia National Research and Innovation Agency (BRIN) Central Jakarta Bandung Indonesia College Vocational Studies IPB University Bogor Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Intellectual Property Rights are fundamental to drive technological progress and economic growth. However, based on the 2022 Global Innovation Index (GII), Indonesia experienced a decline in the patent innovation output index by 58%. which indicates problems in the utilization and management of IPR. Universities as knowledge generators are essential in increasing patents in Indonesia but have yet to be fully optimized. This study aims to develop a digital knowledge collaboration platform model. This study produces a digital knowledge collaboration model using the Soft systems Methodology (SSM) approach. This study produces four main features of the digital knowledge collaboration model: Knowledge Sharing, Collaborative Research, Intellectual Property Management, and Legal and Regulatory Support. The main contribution of this study is to produce a conceptual model of digital knowledge collaboration and use case diagrams that universities can use as the primary foundation in developing a digital knowledge collaboration platform that lecturers, external partners, and the government can utilize.

关键词： Technological innovation Patents Law Computational modeling Government Collaboration Intellectual property Generators Indexes Informatics

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Queue Management Priority-Based Traffic Engineering Method 9

Queue Management Priority-Based Traffic Engineering Method

引用

9th IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2022

作者： Lemeshko, Oleksandr Yeremenko, Oleksandra Persikov, Anatoliy Yevdokymenko, Maryna Mersni, Amal Harkusha, Serhii Kharkiv National University of Radio Electronics V.V. Popovskyy Department of Infocommunication Engineering Kharkiv Ukraine International University of Sarajevo Department of Engineering Computer Sciences and Engineering Program Sarajevo Bosnia and Herzegovina Poltava University of Economics and Trade Department of Document Studies and Information Activities in Economic Systems Poltava Ukraine

ISBN: (纸本)9798350398915

The work proposes the improvement of queue management priority-based Traffic Engineering method. It is based on the interaction prediction principle to coordinate decisions at various levels. The lower level of calculations is responsible, firstly, for the distribution and aggregation of packet flows between the macro queues and sub-queues organized on the router interface (congestion management task), and, secondly, for the balanced distribution of interface bandwidth among sub-queues, weighted relative to their priorities (resource allocation task). The upper level of the method's calculations is accountable for distributing the interface's bandwidth between macro queues by performing the iterative procedure. The numerical research results of the proposed two-level queue management method confirmed its effectiveness in ensuring high scalability, balanced priority- based distribution of packet flows and interface bandwidth between the macro queues and sub-queues organized on it. The method demonstrated high coordination procedure convergence and the final quality of centralized calculations. © 2022 IEEE.

关键词： Bandwidth

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