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Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

computer Modeling in engineering & sciences 2021年第4期127卷 99-116页

作者： Mohammed Lafi Bilal Hawashin Shadi AlZu’bi Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Information Systems Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan

Most software systems have different stakeholders with a variety of *** process of collecting requirements from a large number of stakeholders is vital but *** propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific *** use natural language processing techniques to get the stakeholder response that represents most other stakeholders’*** study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering *** approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing *** then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest *** experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.

关键词： software requirements requirements elicitation natural language processing

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Optimizing Generative AI Applications: A Comparative Study of Effective Prompting Techniques

Optimizing Generative AI Applications: A Comparative Study o...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Shivya Jyoti Moulik Tejpal Jothi K R Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Generative Artificial Intelligence (GenAI) has emerged as a pivotal technology across various industries, driving advancements in automation, decision-making, and content generation. This paper investigates the efficacy of the prompt engineering methods such as zero-shot, one-shot, and few-shot prompting—in optimizing GenAI systems for diverse applications. Through a comprehensive literature review and an empirical survey involving 13 use cases such as chatbots, content creation, and medical decision support, we evaluate the performance of these prompting methods. The findings reveal that few-shot prompting excels in complex tasks, while zero-shot and one-shot prompting are more effective for simpler tasks. These insights provide practical guidance for leveraging GenAI across different domains, contributing to the advancement of AI-driven solutions.

关键词： Surveys Pervasive computing Industries Decision support systems Social networking (online) Large language models Chatbots Prompt engineering Human resource management Systematic literature review

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Wireless modulation classification based on Radon transform and convolutional neural networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

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Parkinson’s Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations

arXiv

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arXiv 2024年

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

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computers, Materials & Continua 2023年第8期76卷 1537-1554页

作者： Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or *** the number of labels is limited to one,the task becomes single-label text *** Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical *** recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so *** paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model *** presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic *** achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible ***,the ELM model is utilized for the representation and recognition of the Arabic *** last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier *** experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

关键词： Arabic corpus dragonfly algorithm machine learning text mining extreme learning machine

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Enhancing Retrieval Augmented Generation systems Using AI Models and Graph Databases

Enhancing Retrieval Augmented Generation Systems Using AI Mo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Souvik Mukherjee Soosan Shabnam Sara Hasan D. Ajitha Department of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper outlines the process of generating a Neo4j graph database powered by Language Models (LLMs). The primary goal is to extract structured information from unstructured data, including user profiles, paper briefs, and Slack messages, and convert them into Cypher queries. The data is then ingested into Neo4j to build a graph database that captures relationships between users, paper, technologies, and messages. A pipeline was developed to automate the process, ensuring accurate entity and relationship extraction using predefined templates. This approach allows for efficient data representation and supports consultancy in managing large datasets by generating insightful visualizations and querying capabilities.

关键词： Ciphers Accuracy Databases Pipelines Retrieval augmented generation Loading Data visualization Transforms Paper technology Data mining

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods 38

On Partial Optimal Transport: Revising the Infeasibility of ...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Keeping Behavioral Programs Alive: Specifying and Executing Liveness Requirements

arXiv

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arXiv 2024年

作者： Yaacov, Tom Elyasaf, Achiya Weiss, Gera Computer Science Department Ben-Gurion University of the Negev Software and Information Systems Engineering Department Ben-Gurion University of the Negev

One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules that specify what the system may, must, and must not do. By that, each module can enforce a single system requirement, including negative specifications such as "don’t do X after Y." The existing BP protocol, however, allows only the enforcement of safety requirements and does not support the execution of liveness properties such as "do X at least three times." To model liveness requirements in BP directly and independently, we propose idioms for tagging states with "must-finish," indicating that tasks are yet to be completed. We show that this idiom allows a direct specification of known requirements patterns from the literature. We also offer semantics and two execution mechanisms, one based on a translation to Büchi automata and the other based on a Markov decision process (MDP). The latter approach offers the possibility of utilizing deep reinforcement learning (DRL) algorithms, which bear the potential to handle large software systems effectively. This paper presents a qualitative and quantitative assessment of the proposed approach using a proof-of-concept tool. A formal analysis of the MDP-based execution mechanism is given in an appendix. © 2024, CC BY.

关键词： Markov processes

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