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Classification and Comprehension of Software Requirements Using Ensemble Learning

computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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Exploring Satisfaction Level of Customers in Restaurants by using Latent Dirichlet Allocation(LDA) Algorithm 6

Exploring Satisfaction Level of Customers in Restaurants by ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Karmakar, Sona Sivakumar, N. Pillai, Anitha S. Department of Computer Applications Hindustan Institute of Technology and Science Tamilnadu Chennai India School of Computing Sciences Hindustan Institute of Technology and Science Tamilnadu Chennai India

ISBN: (纸本)9798350398496

Social media has become an essential means for communicating the review/opinions of people around the world due to the rapid expansion and availability of the internet. Reviews and opinions are expressed both as text and audio. But text communication via networking media is overwhelming. Each and every second, a vast amount of information is produced online because of social media sites. However, online reviews on social media provides an excellent and trustworthy channel for examining the areas that require improvement and for understanding the needs of customers. This paper tries to understand the various topics that are discussed by customers about food with the aim of providing an insight to improve the area where there are negative comments. By doing this, customer retention will increase and gradually the business also. Topic modeling is done to find the hidden topic in a set of comments or customer reviews to find out on which topic customers are talking, thinking, or discussing more about restaurant services. It helps to read customers' mindsets, and accordingly, improvements can be made to increase consumer demand for a restaurant. So, a system is proposed to detect topics of textual reviews of different restaurants using the latent Dirichlet allocation (LDA) modelling approach. A probabilistic, statistical strategy for document designing called latent document analysis (LDA) identifies latent semantic topics in sizable corpora. The dataset used for this study was taken from Kaggle, which had 111, 105 reviews. First, topics from reviews were extracted using the LDA model, then data was visualised using the LDAvis tool. The experiment's findings show that the analysis obtained a successful topic division outcome especially in long-to-short text level topic classification. © 2023 IEEE.

关键词： Statistics

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Vehicle Insurance Damage Estimation Using YOLOv5 4

Vehicle Insurance Damage Estimation Using YOLOv5

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India Department of Computer Science and Engineering Gitam School of Technology Bengaluru Off-Campus Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Accident assessments for insurance companies are currently conducted manually, which can be time-consuming and prone to human error. The proposed method seeks to automate this process, aiming to save time and enhance accuracy. This paper introduces an innovative approach to analyzing object damage by leveraging the advanced YOLOv5 (You Only Look One Level) deep learning architecture. The research is meticulously crafted to address the imperative need for robust and precise detection and classification of damaged objects in images, crucial for effective assessment of structural and environmental integrity. Leveraging the unparalleled real-time object detection capabilities of YOLOv5, the framework is finely tuned and trained on a comprehensive dataset encompassing diverse scenarios of damaged objects. Through exhaustive experimentation and validation, our methodology demonstrates substantial advancements, offering unparalleled efficiency and accuracy in the domain of object damage analysis. The findings of this study hold significant implications for a broad spectrum of applications, ranging from infrastructure monitoring to disaster response, underscoring the transformative potential of deep learning in enhancing critical assessment processes. © 2024 IEEE.

关键词： Deep learning

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LEARNING A DATA-DRIVEN POLICY NETWORK FOR PRE-TRAINING AUTOMATED FEATURE ENGINEERING 11

LEARNING A DATA-DRIVEN POLICY NETWORK FOR PRE-TRAINING AUTOM...

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11th International Conference on Learning Representations, ICLR 2023

作者： Li, Liyao Wang, Haobo Zha, Liangyu Huang, Qingyi Wu, Sai Chen, Gang Zhao, Junbo College of Computer Science and Technology Zhejiang University China Institute of Computing Innovation Zhejiang University China

Feature engineering is widely acknowledged to be pivotal in tabular data analysis and *** feature engineering (AutoFE) emerged to automate this process managed by experienced data scientists and engineers *** this area, most - if not all - prior work adopted an identical framework from the neural architecture search (NAS) *** feasible, we posit that the NAS framework very much contradicts the way how human experts cope with the data since the inherent Markov decision process (MDP) setup *** point out that its data-unobserved setup consequentially results in incapability to generalize across different datasets as well as also high computational *** paper proposes a novel AutoFE framework Feature Set Data-Driven Search (FETCH), a pipeline mainly for feature generation and ***, FETCH is built on a brand-new data-driven MDP setup using the tabular dataset as the state fed into the policy ***, we posit that the crucial merit of FETCH is its transferability where the yielded policy network trained on a variety of datasets is indeed capable to enact feature engineering on unseen data, without requiring additional *** is a pioneer attempt to build a tabular data pre-training paradigm via *** experiments show that FETCH systematically surpasses the current state-of-the-art AutoFE methods and validates the transferability of AutoFE pre-training. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Markov processes

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MSynFD: Multi-hop Syntax Aware Fake News Detection 24

MSynFD: Multi-hop Syntax Aware Fake News Detection

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33rd ACM Web Conference, WWW 2024

作者： Xiao, Liang Zhang, Qi Shi, Chongyang Wang, Shoujin Naseem, Usman Hu, Liang School of Computer Science Beijing Institute of Technology Beijing China School of Computer Science Tongji University Shanghai China School of Computer Science University of Technology Sydney Sydney Australia School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400701719

The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (articles) and heavily rely on sequential modeling and global attention to extract semantic information. These existing methods fail to handle the complex, subtle twists1 in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. Specifically, we introduce a syntactical dependency graph and design a multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It extends the effect of word perception, leading to effective noise filtering and adjacent relation enhancement. Subsequently, a sequential relative position-aware Transformer is designed to capture the sequential information, together with an elaborate keyword debiasing module to mitigate the prior bias. Extensive experimental results on two public benchmark datasets verify the effectiveness and superior performance of our proposed MSynFD over state-of-the-art detection models. © 2024 ACM.

关键词： Benchmarking

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Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

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computer Modeling in Engineering & sciences 2024年第4期139卷 633-659页

作者： Nadeem Ahmed Khalid Mohammadani Ali Kashif Bashir Marwan Omar Angel Jones Fayaz Hassan School of Electronic Science Beijing University of Post and TelecommunicationBeijing100086China Department of Computer Science Huanggang Normal UniversityHuanggangChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabad502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Information Technology and Management Illinois Institute of TechnologyChicagoUSA College of Computing Capital Technology UniversityLaurelUSA

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes *** the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against *** additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network *** testing of our solution has revealed its remarkable ability to enhance communication in ***,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and *** results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

关键词： Black hole attack IoV vehicular ad hoc network AODV routing protocol

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Patient Monitoring for Personalized Mobile Health (PMH) Based on Medical Virtual Instruments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第16期17卷 82-94页

作者： Sweetline, B. Christina Vijayakumaran, C. Samydurai, A. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu KattankulathurChennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Tamil Nadu KattankulathurChennai India

One of the newest technologies, mobile health, has the potential to support the provision of care for older adults and offer them individualised treatment. This study’s goal is to evaluate the benefits and challenges of personalised mobile health (PMH) for elderly residential care. Virtual worlds are quickly integrating into the landscape of instructional technologies. One of the most well-known of these settings is Second Life (SL). Despite the potential of SL for health professions education, there aren’t many official SL applications for this purpose, and the effectiveness of these applications hasn’t been evaluated to the fullest extent possible. Similarly, it appears that nothing is known about the use of virtual worlds for continuing medical education. In order to better grasp the fundamentals of the aid of MVIs for personal health monitoring (PHM), we were able to pinpoint the key disease regions, sensors, channels, calculations and communication protocols. The main obstacles limiting MVIs’ degree of integration into the international health care system were also identified. The analysis demonstrates that MVIs offer an excellent possibility for the creation of affordable, personalised health systems that meet the unique equipment requirements of a certain field of medicine. © 2023 by the authors of this article. Published under CC-BY.

关键词： mHealth

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Credit assignment for trained neural networks based on Koopman operator theory

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Frontiers of computer science 2024年第1期18卷 251-253页

作者： Zhen LIANG Changyuan ZHAO Wanwei LIU Bai XUE Wenjing YANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410000China Institute of Software Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410000China

1 Introduction Artificial neural networks(ANNs,also NNs)have recently emerged as leading candidate models for deep learning,popularly used in various areas[1–3].Behind the enormous success,ANNs are generally with complicated structures,there being an intricate data flow through multiple linear or nonlinear components between the input layer and the output ***,it is pressing to evaluate how much a specific component contributes to the final output,termed the Credit Assignment Problem(CAP)[4]in this paper.

关键词： layer assignment operator

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A Comparative Analysis of Transformers for Multilingual Neural Machine Translation 7

A Comparative Analysis of Transformers for Multilingual Neur...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Prasanna Kumar, R. Sudharson, S. Reddy, Avuthu Avinash Siva Jyothi Natha Reddy, B. Anvitha, Vemireddy Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350305173

Multilingual neural machine translation (NMT) has emerged as a promising solution to break down language barriers and promote cross-lingual communication. Transformer-based NMT models have gained significant attention due to their ability to learn contextual information and their effectiveness in handling long-range dependencies. The transformer model, with its self-attention and cross-attention mechanisms, fundamentally alters machine translation when compared to other neural machine translation models and traditional machine translation models. Token alignments between source and target sentences are successfully modelled by these mechanisms. This paper deals with the performance of a transformer-based multilingual NMT model that uses a shared encoder and decoder. We conducted experiments on the publicly available Multi30k dataset for translation jobs involving German, French, and Czech into English. In comparison of models, the Transformer model has performed better against the Convolutional SeqtoSeq and Attention-Based Seq2Seq models. We have evaluated our model on the metric Bilingual Evaluation Understudy (BLEU), where the transformer achieved 36.67, 48.47, and 33.66 for DE, FR, and CZ to EN translations. © 2023 IEEE.

关键词： Neural machine translation

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A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

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computer Modeling in Engineering & sciences 2024年第6期139卷 3311-3327页

作者： Shujiang Xu Ziye Wang Lianhai Wang Miodrag J.Mihaljevi′c Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan250014China Mathematical Institute The Serbian Academy of Sciences and ArtsBelgrade11000Serbia

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business ***,transaction performance and scalability has become the main challenges hindering the widespread adoption of *** to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many *** improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling ***,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected *** paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction *** crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the ***,the scheme can achieve a high-frequency transaction as well as a better blockchain *** results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

关键词： Blockchain sharding graph partitioning algorithm

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