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An optimized classification of apps reviews for improving requirement engineering

Recent Advances in computer Science and Communications 2021年第5期14卷 1390-1399页

作者： Bhatia, M.P.S. Kumar, Akshi Beniwal, Rohit Division of Computer Engineering Netaji Subhas Institute of Technology Delhi India Department of Computer Science & Engineering Delhi Technological University Delhi India

Background: The App Stores, for example, Google Play and Apple Play Store provide a platform that allows users to provide feedback on the apps in the form of reviews. An app review typically includes star rating followed by a comment. Recent studies have shown that these reviews possess a vital source of information that can be used by the app developers and the vendors for improving the future versions of an app. However, in most of the cases, these reviews are present in unstructured form and extracting useful information from them requires a great effort. Objective: This article provides an optimized classification approach that automatically classifies the reviews into a bug report, feature request, and shortcoming & improvement request relevant to Requirement engineering. Methods: Our methodology used supervised Machine Learning techniques (Multinomial Naive Bayes, Linear SVC, CART Decision Tree) that have been first evaluated based on t h e conven-tional Bag of Words (BOW) feature extraction model along with other auxiliary features such as review rating, review length, review tense, and review sentiment score to automatically classify app reviews into their relevant categories, and subsequently, a feature selection method is used to provide an optimized classification approach for improving classifier performance using Particle Swarm Optimization (PSO) nature-inspired algorithm. Results: Result shows that we achieved best results with precision of 62.0 % and recall of 47.3 % with Linear SVC Machine Learning technique, which we further optimized with PSO nature-inspired algorithm, i.e., with PSO + Linear SVC, thus, resulting in a precision of 63.6 % and recall of 55.0 %. Conclusion: This optimized automatic classification improves the Requirement engineering where developer straightforwardly knows what to improve further in the concerned app. © 2021, Bentham Science Publishers. All rights reserved.

关键词： Sentiment analysis

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Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network 27

Optimizing Traffic Signal Control to Enhance Transportation ...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Kumarasamy, Vijayalakshmi K Saroj, Abhilasha Jairam Duhamel, Joseph Liang, Yu Wu, Dalei Hunter, Michael P. Sartipi, Mina University of Tennessee Department of Computer Science and Engineering Chattanooga United States Oak Ridge National Laboratory Buildings and Transportation Sciences Division United States School of Civil and Environmental Engineering Georgia Institute of Technology United States

ISBN: (纸本)9798331505929

Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan. © 2024 IEEE.

关键词： Graph neural networks

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Learning Communities from Equilibria of Nonlinear Opinion Dynamics 63

Learning Communities from Equilibria of Nonlinear Opinion Dy...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xing, Yu Bizyaeva, Anastasia Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden Cornell University Sibley School of Mechanical and Aerospace Engineering IthacaNY United States

ISBN: (纸本)9798350316339

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the intercommunity connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms. © 2024 IEEE.

关键词： Nash equilibrium

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LPCR: Information-Theory Based Low-Power Code Reordering for Serial Links in Network-on-Chip

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Adelkhani, Morteza Suvizi, Ali Arzaghi, Farzaneh Zamani, Sara Salehi, Mohammad Shafique, Muhammad University of Guilan Department of Computer Engineering Rasht Iran Amirkabir University of Technology Department of Computer Engineering Tehran Iran Division of Engineering Abu Dhabi129188 United Arab Emirates

Manycore platforms have emerged as a promising candidate to meet the increasing computation demand in high-performance computing (HPC). Networks-on-Chip (NoC) have proposed appropriate on-chip communication solutions for manycore platforms. However, the increase in the number of cores and the size of cache in a single chip raises the power dissipation of NoC, potentially impacting data communication performance. The primary source of power dissipation in NoC is the switching activity of data bits transmitted through data links. In this study, we introduce an analytical method, based on information theory, to assess data representation or coding methods from the perspectives of the switching activity of data bits and the length of codewords used for data symbols. Subsequently, we propose a lightweight yet efficient Low-Power Code Reordering (LPCR) technique, that can be integrated into existing coding methods to decrease power consumption. The comprehensive evaluations presented in this paper demonstrate that our LPCR method, reduces link power consumption through reducing the switching activity of data bits up to 13.2%, on average 8.8%, while increases the number of data bits by less than 4.9%, this decreases link energy consumption by 9.9% when applied to the coding methods which aim to minimize the number of data bits (e.g., Huffman coding). However, LPCR does not change the number of data bits when implemented on the Binary coding and, decreases the number of bits up to 12% when implemented on the Most Frequent Least Power (MFLP) method. LPCR impose a very low time and memory overhead at runtime. It needs a data array ranging from 256B for Binary to 32KB for MFLP methods and executes one extra command to read codewords from the array. © 1982-2012 IEEE.

关键词： Binary codes

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Active Project: Supporting Young Children's Computational Thinking Skills Using a Mixed-Reality Environment

Active Project: Supporting Young Children's Computational Th...

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2023 ASEE Annual Conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Hwang, Jaejin Lee, Sungchul Kim, Yanghee Zaman, Mobasshira Pokhrel, Sobhit Department of Industrial and Systems Engineering College of Engineering and Engineering Technology Northern Illinois University DeKalbIL United States Division of Computer Science and Engineering Sunmoon University Korea Republic of Department of Educational Technology Northern Illinois University DeKalbIL United States

The purpose of this study was to develop a mixed-reality environment for supporting young children's computational thinking skills and evaluate the feasibility of this system for further research steps. Seventeen young children aged from six to eleven participated in the study by using the augmented reality technologies and interacting with a social robot while solving the path-finding problem. The motion capture sensors were utilized to precisely capture individual kids' behavior. We found that individual kid's learning progress dramatically improved after experiencing a few trials, and this improvement was more apparent in the 6 to 8 years old children group compared to 9 to 11 years old group. In addition, multimodal data visualization allowed us to assess the in-depth analysis of the event when each kid struggled to solve the problem. Future study would evaluate the effectiveness of this system by comparing the intervention group and control group after a prolonged week exposure. © American Society for engineering Education, 2023.

关键词： Data visualization

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Enhancing IoT Data Security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization 8

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Anuradha, K. Alam, Irfan Nair, Deveendra Menon Narayanan Kiran, Ajmeera Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Telangana India Scai Vit Bhopal University Cyber Security and Digital Forensics Division Kothari Kalan Sehore India Mlr Institute of Technology Department of Computer Science and Engineering Hdyerabad India

ISBN: (纸本)9798350376425

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State Department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications. © 2024 IEEE.

关键词： Steganography

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First-order reversal curve analysis of superparamagnetic nanoparticles with oriented easy axis of magnetization

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Journal of the Magnetics Society of Japan 2023年第4期47卷 115-117页

作者： Sasaoka, Eisuke Yang, Chao Trisnanto, Suko Bagus Song, Zenglu Ota, Satoshi Takemura, Yasushi Division of Electrical and Computer Engineering Yokohama National University Yokohama240-8501 Japan School of Electrical Engineering Nanjing Vocational University of Industry Technology Nanjing210023 China Department of Electrical and Electronic Engineering Shizuoka University Hamamatsu432-8561 Japan

We analyzed the magnetization process of magnetic nanoparticles using first-order reversal curve (FORC) analysis and verified a superparamagnetic feature and the orientation of the easy axis of the particles. The FORCs and FORC diagram of magnetic nanoparticles dispersed in water exhibited a superparamagnetic feature. The differences in the magnetizations and distributions of the coercive force of the solid-state samples were used to identify the orientation of the easy axes of magnetization. © 2023 by the Magnetics Society of Japan.

关键词： Magnetic anisotropy

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Multiclass Brain Tumor Detection using Convolutional Neural Network and Conventional Machine Learning 2

Multiclass Brain Tumor Detection using Convolutional Neural ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Vidhya, K. Vamsi Krishna, B. Haritha, M. Rajasekaran, Arun Sekar Roopa Chandrika, R. Velusamy, Jayaraj Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security School of Computer Science and Technology Coimbatore India QIS College of Engineering and Technology Department of AIMLDS Andhra Pradesh India QIS College of Engineering and Technology Department of Mechanical Engineering Andhra Pradesh India SR University Department of Electronics and Communication Engineering Telangana India Karpagam Academy of Higher Education Department of Cyber Security Coimbatore India Nehru Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331534950

Brain tumor (BT) affect the brain and reduce the lifespan of humans. Therefore, diagnosis and treatment of illnesses in its early stage is required. The objective of this paper is to evaluate the effectiveness of the Convolutional Neural Network (CNN) and Conventional Machine Learning (CML) strategies for identifying multiple classes of brain tumor. In the research, a comparison is made between these two systems for the picture classification of brain tumor. The first step is the collection and preprocessing of images of several classes of brain tumor. The CNN and CML models are trained using the preprocessed dataset. CML makes use of traditional machine learning (ML) algorithms for the purpose of feature extraction and classification, whereas CNN relies on convolutional layers to automatically extract significant features from the images it is fed as input. Precision, accuracy, F1 score, and recall are the criteria that are utilized to evaluate methods. In terms of accuracy and overall performance, CNN is superior to CML. CNN provides a more precise classification of a variety of tumor classifications. Based on these findings, convolutional neural networks (CNN) are likely superior than machine learning for the identification of multiclass brain tumor. CNNs are superior because of their ability to directly understand intricate patterns and characteristics from photographs. This work demonstrates the usefulness of CNNs for multiclass classification in brain tumor detection systems because of their high accuracy and efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Double-Quantitative Feature Selection Approach for Multigranularity Ordered Decision Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2385-2396页

作者： Li, Wentao Deng, Chaojun Pedrycz, Witold Castillo, Oscar Zhang, Chao Zhan, Tao Southwest University College of Artificial Intelligence Chongqing400715 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Tijuana Institute of Technology Division of Graduate Studies and Research Tijuana22379 Mexico Shanxi University School of Computer and Information Technology Taiyuan030006 China Southwest University School of Mathematics and Statistics Chongqing400715 China

Double-quantitative-based granular computing implies the systematic perspective, completeness, and accuracy of rough approximation. However, most of the existing research works only focus on the case of single quantification, and there are few research study on the simultaneous computing method of double quantification. In this article, we explore feature selection with double quantification in multigranularity ordered decision systems (MG-ODSs). First, the related concepts of quantitative functions are interpreted from different viewpoints of relative and absolute quantification. Then, the multigranularity double-quantitative rough sets in an ordered decision system (ODS) from optimistic and pessimistic cases, the related properties, and three-way decisions based on the presented quantitative levels are discussed. Furthermore, the greedy algorithm for feature selection is derived. By using 12 datasets from a public repository, evaluations and comparisons are made on the parameter setting and classification accuracy. From these comparative experiments, the advantages and effectiveness of the proposed feature selection algorithm could be demonstrated over the existing approaches. © 2020 IEEE.

关键词： Data mining

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