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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Al-Ghazi, Muhammad Kent Bertrand, Ryan Destra, Muhammad Dzul Qarrnayn Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

Health insurance is a useful service that can help its users gain lifesaving medical aid when they are in need. However, health insurance is also exploitable to insurance fraud through the falsification of information to increase the amount of reimbursement and cause massive loss of funds to the insurance provider. We propose the usage of machine learning to accurately determine potential health insurance fraud. The objective of conducting this research is to determine which features are the most important to determine healthcare insurance fraud. This research used a dataset provided in Kaggle titled Healthcare Provider Fraud Detection Analysis using Random Forest Classifier and Logistic Regression. The best-performing model in this test, the Logistic Regression, is then used to which features are the most important for the classification. Our research shows that the most important feature in detecting health insurance fraud is the amount of money reimbursed associated with a provider. The Logistic Regression model achieved an accuracy of 0.90, precision of 0.93, recall of 0.91, and an F1 Score of 0.90, outperforming the Random Forest model in comparative analysis. © 2024 IEEE.

关键词： Logistic regression

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DIR:Dynamic Request Interleaving for Improving the Read Performance of Aged Solid-State Drives

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Journal of computer science & Technology 2024年第1期39卷 82-98页

作者：聂世强张驰伍卫国 Department of Computer Science and Technology Xi'an Jiaotong UniversityShaanci 710049China

Triple-level cell(TLC)NAND flash is increasingly adopted to build solid-state drives(SSDs)for modern computer *** TLC NAND flash effectively improves storage density,it faces severe reliability issues;in partic-ular,the pages exhibit different raw bit error rates(RBERs).Integrating strong low-density parity-check(LDPC)code helps to improve reliability but suffers from prolonged and proportional read latency due to multiple read retries for worse *** straightforward idea is that dispersing page-size data across several pages in different types can achieve a low-er average RBER and reduce the read ***,directly implementing this simple idea into flash translation lay-er(FTL)induces the read amplification issue as one logic page residing in more than one physical page brings several read *** this paper,we propose the Dynamic Request Interleaving(DIR)technology for improving the performance of TLC NAND flash-based SSDs,in particular,the aged ones with large *** exploits the observation that the la-tency of an I/O request is determined,without considering the queuing time,by the access of the slowest device page,i.e.,the page that has the highest *** grouping consecutive logical pages that have high locality and interleaving their encoded data in different types of device pages that have different RBERs,DIR effectively reduces the number of read re-tries for LDPC with limited read *** meet the requirement of allocating hybrid page types for interleaved data,we also design a page-interleaving friendly page allocation scheme,which splits all the planes into multi-plane re-gions for storing the interleaved data and single-plane regions for storing the normal *** pages in the multi-plane re-gion can be read/written in parallel by the proposed multi-plane command and avoid the read amplification *** on the DIR scheme and the proposed page allocation scheme,we build DIR-enable FTL,which integrates the pro

关键词： triple-layer cell solid-state drive(TLC SSD) performance interleaving data unbalanced bit error rate

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Automated Adversarial-Attack Removal with SafetyNet Using ADGIT 10

Automated Adversarial-Attack Removal with SafetyNet Using AD...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Halim, Steven Anandi, Arvio Devin, Vyos Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

In the realm of artificial intelligence (AI), a notable challenge has surfaced: adversarial attacks, these attacks involve altering input data to mislead AI models. Developing defensive measures against adversarial attacks is necessary for a more reliable AI system to protect its users from potential harm. In response to the risk of adversarial attacks, this study aims to mitigate the risk of such attacks, especially in image classification tasks, by proposing Adversarial Detection Guided Input Transformation (ADGIT) as an architecture designed to handle such attacks. In this study, the author will experiment with creating such architecture and measure the quality of the proposed technique. ADGIT works by utilizing SafetyNet as an adversarial detector to detect and cleanse adversarial attacks. The author concludes that the proposed technique could improve robustness against adversarial attacks increasing consistent prediction accuracy from 43% to 60% and reconstructing adversarial input images to be more similar to their unperturbed version, with reconstructed images' PSNR score on 0.007 perturbation increased from 43.1182 to 89.0999. The technique proposed could be used as a new defensive measure and improving robustness against adversarial attacks. Although ADGIT is capable of handling adversarial samples, ADGIT has a drawback in performance speed due to the extra preprocessing step. © 2024 IEEE.

关键词： Adversarial machine learning

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MBASE: Meta-heuristic Based optimized location allocation algorithm for baSE station in IoT assist wireless sensor networks

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Multimedia Tools and Applications 2024年第18期83卷 53383-53415页

作者： Chaurasia, Soni Kumar, Kamal Department of Computer Science and Engineering National Institute of Technology Uttarakhand India Department of Computer Science and Engineering SGT University Gurgaon India

In an Internet of Things (IoT) assisted Wireless Sensor Network (WSN), the location of the Base Station (BS) remains important. BS serves as the central hub for data collection, aggregation and communication within the network. Its location significantly impacts the overall network performance and energy efficiency. However, the role and requirements of the BS may vary depending on the specific implementation and integration of IoT assist opportunistic routing protocol. Therefore, this work propose a Meta-heuristic Based optimized location Allocation algorithm for baSE station in IoT assist WSNs (MBASE). The MBASE algorithm is based on Forwarder Node (FN) selection and using meta-heuristic based opportunistic routing. The FN is selected by a decision-making approach based on meta-heuristic search strategies which offers viable options for local and global optimization problems. This work aims to find a way to achieve a trade-off in this algorithm for efficient and appropriate placement of BS either at the centre or outside of the network deployment zone. Both positions of BS are optimization problems in the design of WSNs/IoTs. MBASE algorithm is proposed for identifying the optimized location allocation for BS and is based on metaheuristic. Simulation based analysis of essential operational and functional parameters such as Message Success Rate (MSR), Packet Delivery Ratio (PDR), Throughput, and Average energy consumption (AEC) is reported in the paper. Additionally, work compares BS locations using a common neighbourhood concept to assure fairness. The evaluation shows a significant improvements in the PDR, MSR, Throughput and AEC for two different BS locations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Base stations

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Women Performance in a computer science program 42

Women Performance in a Computer Science Program

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42nd IEEE International Conference of the Chilean computer science Society, SCCC 2023

作者： Rojas, Catalina Bastarrica, Maria Cecilia University of Chile Computer Science Department Beauchef 851 Santiago Chile

ISBN: (纸本)9798350313895

Historically, women have been underrepresented in STEM fields, both in academia and industry. Gender prejudices sometimes assume that women are less competent in computer science. Chile has implemented various affirmative action initiatives to attract women to computer science programs, but there is almost no research on women's performance in these programs. To address this issue, we conducted a descriptive case study that analyzes grades obtained in all mandatory courses within a computer science program over a span of 12 years. The results reveal women have a slight significant better overall performance and that there are no significant differences in grades across most of the courses, with only three courses showing a slight advantage for women. This study intends to serve a dual purpose: guiding efforts to attract and promote women in computer science and providing the industry with valuable insights into the strengths and weaknesses of female candidates during the hiring process. © 2023 IEEE.

关键词： Education computing

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Investigation of user perception of software features for software architecture recovery in object-oriented software

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International Journal of Information Systems and Change Management 2024年第3期14卷 308-330页

作者： Kumar, Neeraj Singh, Randeep Rathee, Amit Department of Computer Science and Engineering IEC University Solan174103 India Department of Computer Science GC Barota Haryana 131301 India

A well-documented architecture can greatly improve comprehension and maintainability. However, shorter release cycles and quick delivery patterns results in negligence of architecture. In such situations, the architecture can be recovered from its current implementation based on considering dependency relations. In literature, structural and semantic dependencies are commonly used software features, and directory information along with co-change/change history information are among rarely utilised software features. But, they are found to help improve architecture recovery. Therefore, we consider investigating various features that may further improve the accuracy of existing architecture recovery techniques and evaluate their feasibility by considering them in different pairs. We compared five state-of-the-art methods under different feature subsets. We identified that two of them commonly outperform others but surprisingly with low accuracy in some evaluations. Further, we propose a new subset of features that reflects more accurate user perceptions and hence, results in improving the accuracy of architecture recovery techniques. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Software architecture

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Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity 7

Cosmetics Recommendation System Based on Ingredients Using C...

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7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Alisha Firdaus, Sekar Dwi Nurmuthia, Laksmi Imtiyaz, Raina Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365351

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare. © 2024 IEEE.

关键词： Skin care products

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Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

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Recent Advances in computer science and Communications 2024年第3期17卷 77-91页

作者： Yadav, Ram Kumar Arockiam, Daniel Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Madhya Pradesh Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach. © 2024 Bentham science Publishers.

关键词： Support vector machines

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and Technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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DRL-Based Joint Optimization of Wireless Charging and Computation Offloading for Multi-Access Edge Computing

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IEEE Transactions on Services Computing 2025年第3期18卷 1352-1367页

作者： Zhu, Xinyuan Hao, Fei Ma, Lianbo Luo, Changqing Min, Geyong Yang, Laurence T. Shaanxi Normal University School of Computer Science Xi'an710119 China Northeastern University School of Software Shenyang100819 China Virginia Commonwealth University Department of Computer Science RichmondVA23084 United States University of Exeter Department of Computer Science United Kingdom Hainan University School of Computer Science and Technology China St. Francis Xavier University Department of Computer Science Canada

Wireless-powered multi-access edge computing (WP-MEC), as a promising computing paradigm with the great potential for breaking through the power limitations of wireless devices, is facing the challenges of reliable task offloading and charging power allocation. Towards this end, we formulate a joint optimization problem of wireless charging and computation offloading in socially-aware D2D-assisted WP-MEC to maximize the utility, characterized by wireless devices' residual energy and the strength of social relationship. To address this problem, we propose a deep reinforcement learning (DRL)-based approach with hybrid actor-critic networks including three actor networks and one critic network as well as with Proximal Policy Optimization (PPO) updating policy. Further, to prevent the policy collapse, we adopt the PPO-clip algorithm which limits the update steps to enhance the stability of algorithm. The experimental results show that the proposed algorithm can achieved superior convergence performance and, meanwhile, improves the average utility efficiently compared to other baseline approaches. © 2008-2012 IEEE.

关键词： Computation offloading

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