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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAai Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ml)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ml techniques,only a few research studies have focused on the application of ml especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ml algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ml algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Heart Rate Variability-Based Mental Stress Detection: An Explainable Machine Learning Approach

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SN computer science 2023年第2期4卷 176页

作者： Banerjee, Jyoti Sekhar Mahmud, Mufti Brown, David Department of Computer Science and Engineering (AI & ML) Bengal Institute of Technology Kolkata India Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom

Stress may be identified by examining changes in everyone’s physiological reactions. Due to its usefulness and non-intrusive appearance, wearable devices have gained popularity in recent years. Sensors provide the possibility of continuous and real-time data gathering, which is useful for tracking one’s own stress levels. Numerous studies have shown that emotional stress has an impact on heart rate variability (HRV). Through the collection of multimodal information from the wearable sensor, our framework is able to accurately classify HRV based users’ stress levels using explainable machine learning (Xml). Sometimes, ml algorithms are referred to as black boxes. Xml is a model of ml that is designed to explain its objectives, decision-making, and reasoning to end users. End users may include users, data scientists, regulatory bodies, domain experts, executive board members, and managers who utilize machine learning with or without understanding or anybody whose choices are impacted by an ml model. The purpose of this work is to construct an Xml-enabled, uniquely adaptable system for detecting stress in individuals. The results show promising qualitative and quantifiable visual representations that may provide the physician with more detailed knowledge from the outcomes offered by the learnt Xai models, hence improving their comprehension and decision making. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Explainable machine learning Heart rate variability Human–computer interaction Multimodal dataset Stress detection Wearable sensor

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ABE Cloud Privacy Improvisation on Healthcare Systems Using Trained Neural Networking Technique

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SN computer science 2023年第5期4卷 469页

作者： Chaithra, M.H. Vagdevi, S. Department of Computer Science and Engineering REVA University and Research Scholar Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of AI and ML City Engineering College Bangalore India

Healthcare organizations deal with sensitive data and patient information shared via medical servers and Electronic Health Records (EHR). The data on cloud are unsecure as it operates on multi-user recommendations. In this paper, a machine learning technique is upgraded using Attribute Based Encryption (ABE). The proposed technique extracts datasets from pre-trained repository of eHealth datasets and extracts the feature-to-attribute relationship. The processed attributes are termed as coordinated dataset attributes as primary attribute. The proposed ABE improvisation is appended over the trained neural networking (TNN) framework for dataset optimization and re-indexing. The proposed technique is developed and implemented on Kaggle’s patient treatment classification dataset and compared with existing techniques. The proposed technique has outperformed the current techniques by incubating a machine learning technique for attribute privacy improvisation and resultant improvisation of eHealth-care organization’s stability. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ABE Attribute-based encryption Cloud privacy eHealth care Neural networks

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An Analyzation Of Data Privacy In Sharing And Storing Data In Cloud Environment

An Analyzation Of Data Privacy In Sharing And Storing Data I...

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International Conference on Electrical Energy Systems (ICEES)

作者： R.S. Subhasreevignani N. Babu D. Raji K.N. Sowmya S. Afreen Zaiba G. Reddy Abhishek Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) Siddharth Institute of Engineering & Technology Andhra Pradesh India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

The Wide spread use of cloud storage services and their demonstrated power provide the foundational underpinning for the cloud computing industry rapid development. Nevertheless, a lot of security incidents still occur at the cloud storage layer due to deliberate attacks and management negligence, resulting in significant leaks of critical data. To address the issue, we inculate AES and Cryptographic Pseudo Random Number Generator Algorithms. The proposed standard illustrates the process of dividing the data into segments which is encrypted with aes individually to ensure the strong security for data. By distributing the scattered data segments among several cloud sites, security is improved and the possibility of a single point of failure is decreased. Securing access control mechanisms makes it easy for authorized individuals to access and decode data.

关键词： Industries Access control Cloud computing Data privacy Generators Cryptography Standards

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Detection and Classification of Arrhythmia Using Hybrid Deep Learning Model

Detection and Classification of Arrhythmia Using Hybrid Deep...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Kodavati, Trinaya Rithani, M. Venkatraman, K. SyamDev, R.S. Amrita School of Computing Department of Computer Science & Engineering Chennai601103 India New Horizon College of Engineering Department of AI-ML Bangalore560087 India

ISBN: (纸本)9798350319088

Cardiovascular disorders, encompassing arrhythmias rhythms, represent noteworthy worldwide health issues that necessitate prompt identification and precise categorization to facilitate efficacious therapeutic measures. This study examines the status of the field's research at the moment, going into cardiac arrhythmias' physiological foundation, clinical ramifications, and diagnostic resources. The paper proposes a methodology that combines MobileNet V1 with Gated Recurrent Unit (GRU) for the detection and classification of Arrhythmia, which has achieved an accuracy of 99.4%, recall score of 0.98 and precision score of 0.99. The MIT-BIH Arrhythmia dataset, consisting of recordings by the BIH Arrhythmia Laboratory, is utilized for training and validation of the model. The findings demonstrate the potential of using the MobileNetV1+GRU model for detection and classification of different types of Arrhythmia, which can be applied for diagnosis in hospitals, clinics, emergency departments and screening programs. © 2023 IEEE.

关键词： Hospitals

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network 7

Deployment Schemes of In-Band Management Mode Based on Indus...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In order to properly manage and monitor their networks in industrial environments, industrial Software-Defined Networking (SDN) networks need to have an in-band administration mode. This research investigates a wide variety of alternative deployment options for in-band management mode in industrial SDN networks in order to increase the performance of the network, as well as its dependability and level of network security. We study how the performance of the network differs depending on whatever coupling of in-band management and industrial SDN architecture we choose. We analyse a range of potential pairings of in-band management with industrial SDN design. We also discuss the advantages and disadvantages of each deployment technique, providing insights into the creation and management of robust Industrial SDN networks. It provides useful recommendations for increasing the efficacy and security of particular network infrastructures, which might be valuable to both corporate groups and network administrators. The adoption of SDN technology and the installation of tailored management modes are becoming more and more significant as industrial settings continue to expand. This is of the utmost significance in order to guarantee the highest possible standards of both operational excellence and safety. © 2024 IEEE.

关键词： Deployment Schemes In-band Management Industrial SDN Network Network Control Network Monitoring Network Security

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Leveraging Deep Learning with Automated Fundus Imaging for Early Detection and Diagnosis of Eye Diseases

Leveraging Deep Learning with Automated Fundus Imaging for E...

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International Conference on computer and Communications Technologies (ICCCT)

作者： E. Priya M. Dhinesh G. Rohitkumar M. Dheena Department of Computer Science and Engineering (AI&ML) Sri Sairam Engineering College Chennai India Department of Electronics and Communication Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The most common causes of blindness and visual loss in the world are eye conditions such as diabetic retinopathy, age-related macular degeneration and glaucoma. Patients may benefit from the greatest outcomes if these illnesses are identified early and diagnosed. Fundus imaging helps in the diagnosis of eye disorders by providing detailed pictures of the optic nerve head as well as the retina. For feature extraction in normal, diabetic retinopathy, glaucoma, and macular degeneration fundus images, CNN was applied. A labeled fundus image dataset supports the training process. These datasets reverse the implications of the model not being generalizable to real world problems. Outcome is provided for a number of models including ResNet, VGG16, and designed custom CNN model. Model performance can be evaluated by means of F1-score, recall, accuracy, and precision metrics. The suggested method aims to provide an automated classification solution for eye diseases that is as scalable and effective as possible. Since early identification is vital for preventing permanent vision loss, this technique could prove beneficial in telemedicine and ophthalmology.

关键词： Glaucoma Macular degeneration Deep learning Diabetic retinopathy Accuracy Computational modeling Telemedicine Blindness Feature extraction Ensemble learning

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Virtual Machine Security Issues and Solutions When it is in Host

Virtual Machine Security Issues and Solutions When it is in ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Rakesh Reddy Gurrala T.Sampath Kumar K. Anuradha Department of Computer science and Engineering SR University Warangal Telangana India Department of CSE(AI&ML) CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In the current era, there are huge trending network intensive applications are running in virtual environment such as Cloud-based services and Internet servers, and machine's physical and the network resources shares by VM's running on same machine. Virtual machines (VMs) have become a cornerstone of modern computing, enabling efficient resource utilization, scalability, and flexibility in various domains such as cloud computing, data Centre's, and development environments. However, this proliferation of VMs has also introduced a range of security challenges that necessitate careful consideration and mitigation strategies. The paper provides an overview of the issues related to security and associated with virtual machines when it is in Host. It begins by delineating the fundamental concepts of virtualization technology, highlighting the layered architecture of VMs and the critical components involved. Subsequently, the paper delves into the key security concerns, categorizing them into four main areas: isolation, privilege escalation, resource contention, and vulnerability to attacks. In addition to identifying these security issues, the paper provides an analysis of existing mitigation techniques and best practices. These include measures such as hypervisor hardening, secure configuration management, network segmentation, and the use of security monitoring.

关键词： Cloud computing Virtual machine monitors Program processors Prevention and mitigation Scalability Security Proposals Servers Resource management Optimization

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Integrating DCCS-Net for High-Fidelity Watermarking and Tampering Detection in Brain Scan Images 6

Integrating DCCS-Net for High-Fidelity Watermarking and Tamp...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCmlA 2024

作者： Ahamed, Bsh. Shayeez Baskar, Radhika Nalinipriya, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Madanapalle Institute of Technology & Science Department of CSE-AI&ML Madanapalle India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331505790

This study presents a new approach that combines the Deep Cascade Compressed Sensing Network (DCCS-Net) with a watermarking and tampering detection framework to improve the accuracy and quality of medical images. The system analyzes brain scan images by incorporating watermarks and identifying any tampering, thereby guaranteeing a high level of accuracy and maintaining the structural resemblance to the original images. The essential steps involve preprocessing the image using Discrete Cosine Transform (DCT), employing compressed sensing for efficient data management, and embedding watermarks using binary quantified reference values. The evaluation metrics, namely Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), indicate that the watermarked images exhibit a PSNR of 41.906 and SSIM of 0.98458. These values suggest that there is minimal degradation in the quality of the images. The tampering detection process achieves a precision of 0.94531, effectively identifying tampered blocks. The reconstructed images exhibit minimal degradation in quality, as evidenced by a PSNR of 36.21 and SSIM of 0.9789. The results highlight the ability of DCCS-Net to effectively manage noise, minimize artifacts, and maintain intricate details, thereby improving the accuracy and reliability of diagnostics. The graphical depiction of metrics provides additional evidence to support the strength and reliability of the methodology in preserving image quality and reconstructing manipulated images, thus ensuring the trustworthiness and reliability of medical image analysis. © 2024 IEEE.

关键词： Discrete cosine transforms

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Web Defacement Identification And Detection System

Web Defacement Identification And Detection System

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： V.M. Vinayagam A. Sathish K. Sreenivasulu D. Sreenu R. Snehit Y. Abhilash Reddy Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Chittoor Andhra Pradesh Department of Computer Science and Engineering(AI&ML) Siddharth Institute of Engineering & Technology Chittoor Andhra Pradesh

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Although it has become a need in our daily lives, browsing the internet can be risky. New features and capabilities are always being added by browser vendors, which can increase the usability of websites but also increase their vulnerability to attacks. Because they aim to steal confidential data from gullible users, phishing websites pose a severe threat to internet security. In order to counter this issue, academics have created a number of methods, such as machine learning algorithms, for identifying phishing websites. By using extensive datasets of authentic and fraudulent websites, machine learning algorithms can be trained to identify patterns and traits that differentiate between the two. Then, before people become victims, these algorithms can be used to recognize and prevent phishing websites. Feature extraction is one method for detecting phishing websites using machine learning. In this method, different aspects of a website, like its content, age, and URL structure, are examined to detect phishing websites. Another approach involves using deep learning algorithms to automatically extract features and learn complex patterns in website data. Overall, machine learning-based phishing website detection techniques have shown promising results, achieving high accuracy rates and outperforming traditional rule-based methods. These strategies could become valuable weapons in the fight against online phishing attempts with more study and refinement.

关键词： Uniform resource locators Machine learning algorithms Accuracy Phishing Weapons User interfaces Feature extraction Fraud Telecommunication computing Usability

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