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International Conference on Smart Computing & Communications (ICSCC)

作者： Clarissa Octavia Tjandra Grace Shirley Cam Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with consumers and inform them of the advantages of making a deposit with the bank, which will increase the number of deposits the bank receives. Thus, the goal of this research is to determine the best customer information that affects their decision on subscribing a term deposit and providing the best model for predicting the outcome of future marketing campaign. By determining the significance of each feature, this research constructed machine learning models using methods like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) as our research contribution. According to the research's findings, out of the three machine learning models, Random Forest (RF) has the highest accuracy scores. Additionally, the feature importance model reveals that a customer's decision to make a deposit or not is greatly influenced by factors such as the age of the customer, how much does the customer has in their bank account, did the bank have contacts with longer call durations, and did it has a positive outcomes on the previous marketing campaigns. The customers who are contacted more frequently and have subscribed before are more likely to subscribe in the future marketing campaign.

关键词： Support vector machines Radio frequency Accuracy Computational modeling Oral communication Predictive models data models Real-time systems Random forests Testing

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Machine Learning Based Diabetes Detection Model for False Negative Reduction

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Biomedical Materials and Devices 2024年第1期2卷 427-443页

作者： Uddin, Md. Ashraf Islam, Md. Manowarul Talukder, Md. Alamin Hossain, Md. Al Amin Akhter, Arnisha Aryal, Sunil Muntaha, Maisha School of Information Technology Deakin University Department of Computer Science and Engineering Jagannath University

Diabetes is a chronic disease characterized by the inability of the pancreas to produce enough insulin or the body’s inability to use insulin efficiently. This disease is becoming increasingly prevalent worldwide and can result in severe complications such as blindness, kidney failure, and stroke. Early detection of diabetes can potentially save millions of lives globally, making it a crucial focus of research. In this study, we propose a machine learning model to aid in predicting diabetes. The model comprises several machine learning methods: Linear Regression (LnR), Logistic Regression (LR), k-nearest neighbor (KNN), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Prior to feeding the pre-processed data into the machine learning model for evaluation, we conducted several pre-processing steps, such as removing null values, standardizing data using normalization, and labeling data using the label encoding process. Imbalanced datasets can adversely affect the accuracy of machine learning algorithms, and we address this problem by balancing the datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. We assessed the model’s performance on two datasets and found that the random forest algorithm produced optimal results, with 97% accuracy on the diabetes dataset 2019 and 80% accuracy on the Pima Indian dataset. However, using a balanced dataset, we can significantly reduce the number of false-negative detections. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： Balance data Features selection Machine learning Pre-processed data Random forest SMOTE

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Analysis of Software Vulnerabilities Introduced in programming Submissions Across Curriculum at Two Higher Education Institutions 54

Analysis of Software Vulnerabilities Introduced in Programmi...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Sanders, Andrew Walia, Gursimran Singh Allen, Andrew School of Computer and Cyber Science Augusta University Augusta United States Georgia Southern University Department of Computer Science Statesboro United States

ISBN: (纸本)9798350351507

This full research paper describes the analysis of common software vulnerabilities that are introduced by students enrolled in four-year computing and cybersecurity majors from two different higher education institutions in Georgia. As the demand for secure coding education continues to grow, pedagogical improvements need to be made in identifying key software vulnerabilities students commit during code development (from the first programming course to the exit senior design capstone) which in turn can be analyzed to inform the pedagogical interventions focused at preparing students with skill sets for writing secure code and entering the professional workforce. While code security is emphasized throughout the computing curriculum, this research is focused on training individuals to be aware of common vulnerabilities and tailoring programming concept knowledge that has been shown to have a positive effect on code security. Existing research has mainly focused on developing vulnerability analysis tools rather than collecting data (and subsequently analyzing) regarding the types of vulnerabilities produced by students at their institutions. In this paper, we analyzed student code across different courses and reported the types of vulnerabilities produced by students in their assignment submission code from two different higher education institutions across different levels of the four-year curriculum. The reported vulnerabilities are grouped by CWE-ID, which is a standard and common way to categorize and identify software vulnerabilities. The resulting CWE-IDs are then grouped per student submission and per semester (across curriculum levels) to discover the common types of software vulnerabilities committed across cross sections of students. Our results from the analysis of vulnerabilities (ranging from CS1 courses to capstone courses) are organized around the following research questions: 1) What are the most common software vulnerabilities produced by computing maj

关键词： Students

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COCOMO II Analysis of Developing Multi-Account Partner Software for Crypto Exchange 15

COCOMO II Analysis of Developing Multi-Account Partner Softw...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Darmawan, Oscar Adam Gaol, Ford Lumban Soeparno, Haryono Arifin, Yulyani Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

The technique of calculating the work required for software development is known as software development estimation. The objective cost estimating technique for organizing and carrying out Multi Account Partner (MAP) software projects, COCOMO II, was employed in this study. The MAP software is a software architecture designed to support brokerage cryptocurrency exchanges using the order book and liquidity of established crypto exchanges. This research uses data sets from MAP project development at Indonesia Crypto Exchange Platform. It aims to create a software cost estimation model for MAP software using COCOMO II so that the resulting estimation model can be used as input or reference for estimates of subsequent MAP software development. The result estimated that MAP software finished in about four to five months, with a price range for software development of $7,441 to $8,780. Further research is needed with datasets from other crypto exchanges tested to increase cost estimation accuracy using COCOMO II. © 2023 IEEE.

关键词： Blockchain

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Object Oriented Fuzzy Decision Model for Insurance Claim 9

Object Oriented Fuzzy Decision Model for Insurance Claim

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Susanto, Sumiatie Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

The insurance industry faces a significant challenge concerning insurance claims, particularly due to the prevalence of fraudulent insurance claims. To address this issue, one potential solution is the implementation of a computer-based decision model. This research presents a fuzzy decision model based on object-oriented method development. The study involves seven stages (i.e. case analyzing, parameter analyzing, objects-parameters linking, detail object relation constructing, parameter exchange analyzing, OOFDM constructing, and model verifying and validating), with an object-oriented approach serving as the foundational method for constructing the model, and fuzzy logic as the primary method for assessing claim risks in proposing the best decision. The model has the capability to simulate insurance claims and offers objective decisions based on 19,611 claims data, categorizing them into two decision categories: acceptance and pending. © 2023 IEEE.

关键词： Fuzzy logic

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in Engineering & sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query

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Frontiers of computer science 2024年第1期18卷 187-198页

作者： Xingxin LI Youwen ZHU Rui XU Jian WANG Yushu ZHANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Mathematical Informatics University of TokyoTokyo 113-8654Japan Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China School of Computer Science China University of GeosciencesWuhan 430074China

Secure k-Nearest Neighbor(k-NN)query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas,such as privacy-preservingmachine elearning gand secure biometric *** solutions have been put forward to solve this challenging ***,the existing schemes still suffer from various limitations in terms of efficiency and *** this paper,we propose a new encrypt-then-index strategy for the secure k-NN query,which can simultaneously achieve sub-linear search complexity(efficiency)and support dynamical update over the encrypted database(flexibility).Specifically,we propose a novel algorithm to transform the encrypted database and encrypted query points in the *** indexing the transformed database using spatial data structures such as the R-tree index,our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted *** the best of our knowledge,the proposed strategy is the first to simultaneously provide these two *** theoretical analysis and extensive experiments,we formally prove the security and demonstrate the efficiency of our scheme.

关键词： cloud computing secure k-NN query sub-linear complexity dynamicupdate

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Developing Smart Precision Farming Using Big data and Cloud-Based Intelligent Decision Support System 10

Developing Smart Precision Farming Using Big Data and Cloud-...

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

作者： Nurcahyo, Aldian Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-Time monitoring and predictive analytics to increase agricultural production and sustainability. © 2023 IEEE.

关键词： Decision support systems

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Building Damage Assessment Using Deep Learning: Bibliometric Analysis 7

Building Damage Assessment Using Deep Learning: Bibliometric...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Sidharta, Sidharta Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Binus Graduated Program - Doctor of Computer Science Computer Science Department Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Natural catastrophes have become one of the hottest subjects as a result of the harm they have caused to human structures. In recent years, research pertaining to the discipline of building damage assessment (BDA), which seeks to study the effect of damage on structures, has grown substantially. This bibliometric study seeks to determine the state of the art in the subject of Building Damage Assessment Using Deep Learning by examining several papers. The method used (Donthu et al., 202) divides the steps of bibliometric analysis into four parts: identifying the field of research to be analyzed, identifying the methods used in bibliometric analysis, collecting research data with similar fields, and executing and analyzing the results of bibliometric analysis on the collected data. In the data collecting and analysis portion (Garza-Reyes, 2015), the identification of data search keywords, the execution of data searches, the enhancement of search results, statistical data processing, and data analysis are carried out. Several research questions pertaining to trends, influences, themes, authors, and other crucial components of doing research in the area of building damage assessment are answered using these techniques. The outcome is the mapping and resolution of all research problems, as well as the acquisition of bibliometric analysis-related data. The provided findings have been mapped into several types of visualization, including network % clustering graphs, overlay graphs, and density graphs, as well as other forms of visualization that will aid researchers in comprehending the study circumstances in the field. It is envisaged that this bibliometric study would make it simpler for scholars to do research in related topics. © 2022 IEEE.

关键词： Damage detection

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Enhancing Machine Failure Detection with Ensemble Learning and Oversampling Techniques

Enhancing Machine Failure Detection with Ensemble Learning a...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Wilson Gregory Pribadi Maria Linneke Adjie Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

In modern manufacturing, predictive maintenance plays a vital role in minimizing unexpected machine failures, thereby reducing downtime and maintenance expenses. This study explores the implementation of ensemble learning models, including Bagging, AdaBoost, Gradient Boosting, Random Forest, and XGBoost, to predict machine failures based on operational characteristics such as air temperature, process temperature, rotational speed, torque, and tool wear. To address the class imbalance commonly present in failure datasets, oversampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling) were used to improve the model’s performance. However, a couple of experiments shows that ensemble models without oversampling is better than those that uses these techniques. The Gradient Boosting model using just the oversampling performed best with an F1-score of 0.78 for class 1 and overall accuracy of 0.99 while specifically, the version without any oversampling made it to an F1 Score of even as high as 0. XGBoost and Bagging were the next best models with F1-scores of 0.76 and 0.75, respectively since then. In contrast, the F1-score of the model proposed in this study and the models using any kind of oversampling technique were lower than 0.70 of failure class. This is proof of the power of ensemble methods (especially Gradient Boosting), that as result can be deployed to forecast machine failures and encourage right preventive maintenance actions ensuring less interruptions and smoother operations.

关键词： Adaptation models Accuracy Temperature Torque Atmospheric modeling Velocity control Predictive models Boosting Ensemble learning Bagging

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