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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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A Review of Satellite Image Analysis Tools 4th

A Review of Satellite Image Analysis Tools

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Department of Computer Science and Engineering Graphics Era Hill University Dehradun India Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta

ISBN: (纸本)9789819994410

The rapid growth of urban areas, population, and pollution presents a significant environmental challenge. Addressing these challenges requires innovative analytical approaches and data sources. This study focuses on the three-scale phenomena of the environmental system, physical environment, and regional surroundings, considering their implications on a global network scale. Remote sensing data, with its easy accessibility, offers new toolkits and methods to improve the global environment. It provides self-consistent measurements of vital physical attributes and surpasses traditional data sources regarding accuracy, efficiency, and coverage of larger geographical areas. This study aims to identify and analyze appropriate satellite image analysis tools for healthcare. The study objectives include studying available tools, analyzing their features, categorizing tools into open-source and commercial categories, and providing recommendations. This study emphasizes feature extraction and image enhancement parameters, which are crucial for image analysis. Further, this study provides a functional overview of various commercial and open-source satellite image analysis tools and suggests suitable tools for healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geographic information systems

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Inspecting Vanet from Various Critical Angles – a Systematic Review

SSRN

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SSRN 2023年

作者： Karabulut, Muhammet Ali Shah, A.F.M. Shahen Ilhan, Haci Pathan, Al-Sakib Khan Atiquzzaman, And Mohammed Department of Electrical Electronics Engineering Faculty of Engineering and Architecture Kafkas University Kars Turkey Department of Electronics and Communications Engineering Faculty of Electrical & Electronics Engineering Yildiz Technical University Istanbul Turkey Computer Science and Engineering Department School of Science & Engineering United International University Dhaka Bangladesh Department of Computer Science School of Computer Science University of Oklahoma Norman United States

Due to its strategic significance in the area of smart transportation to facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, Vehicular Ad hoc Networks (VANETs) have grown very popular in recent years. The rapid increase in the number of vehicles on the road has also led to the development of heterogeneous, large-scale, and highly dynamic VANETs, which introduce difficulty to fulfill the stringent requirements, which include low latency, high mobility, top security, and massive connectivity of the 5G network. Research works in VANETs mainly focus on message transmission within strict delay requirements based on different applications, data privacy and security. In this respect, a number of studies have been carried out by the researchers that propose models and solutions linked to the enhancement of VANET from several angles, including applications, quality of service (QoS), security, physical layer fading, artificial intelligence (AI) techniques, Medium Access Control (MAC), and routing protocols. These factors serve as the driving force for this study's thorough examination of VANETs, which includes information on specific applications, QoS, channel fading, MAC protocols, channel access mechanisms, routing protocols, security, and difficulties. None of the surveys in existence today address all critical aspects of VANET in a single survey. In this paper, a complete taxonomy of VANETs has been provided based on various issues. First, an overview of VANET is presented with different applications. Then, QoS in VANETs and different proposed MAC protocols are discussed. Channel fading and access mechanisms are presented. After that, routing protocols, security, and clustering in VANETs are provided. AI approaches proposed in VANET are also summarized. Finally, a discussion of future research direction for all aspects is presented. This article might be used as a guide or a point of reference while designing and creating applications, n

关键词： Vehicular ad hoc networks

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Automated Access Control via License Plate Recognition using Neocognitron Neural Network 36

Automated Access Control via License Plate Recognition using...

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36th International Conference on Information Technologies, InfoTech 2022

作者： Rothkrantz, Leon Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Intelligent Systems Delft2628 CD Netherlands

ISBN: (数字)9781665468701

ISBN: (纸本)9781665468701

In 1979 Fukushima developed a hierarchical, multilayered neural network called Neocognitron and used it for the automatic recognition of handwritten Japanese symbols. We combined the Neocognitron classifier with a special image and segment processor and applied the system in 2001 for automatic recognition of license plates in laboratory experiments. In this paper we report about a special image acquisition module and a postprocessor. We tested the system in real life conditions in an application of automated access control. © 2022 IEEE.

关键词： Image segmentation

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High-Level Deep Features and Convolutional Neural Network for Evaluating the Classification Performance of File Cluster Types 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Ali, Rabei Raad Dawd, Lahib Nidhal Mostafa, Salama A. Rachmawanto, Eko Hari Jubair, Mohammed Ahmed National University of Science and Technology Thi-Qar Nasiriyah Iraq Computer Techniques Engineering Department Dijlah University College Baghdad Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Informatics Engineering Dian Nuswantoro University 207 Imam Bonjol Street Semarang50131 Indonesia Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna66002 Iraq

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Accurate file recovery of the corrupted or fragmented file is a very important task when missing the file system, since file recovery procedure involves methods, techniques, and tools that analyze and classify the contents of each data cluster including JPG file or non-JPG file. For classification problems, convolutional neural networks and high-deep learning have been applied to be very efficient and effective in file recovery. This study aims at finding a systematic comparison between a support vector machine (SVM) classifier method and an extreme learning machine (ELM) classifier method for classification issues. The classification methods automatically classify the files in a continuous series of data clusters based on three deep features which are rate of change (RoC), byte frequency distribution (BFD), and entropy. The methods are automatically assigning a class label of JEG file or non-JPG file for fragmented data clusters. The RABEI-2017 dataset was used for evaluating the performance of this study. The results demonstrate that the SVM method outperforms the ELM method with a high accuracy rate to classify fragmented JPG files. The accuracy of classification of the SVM method is 90.15% and of the ELM method is 96.21%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Support vector machines

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Efficient Low Cost Automatic External Defibrillator Circuit 11

Efficient Low Cost Automatic External Defibrillator Circuit

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Sayed, Mohammed S. Elsagheer, Samir A. Hassan, Mohsen A. Egypt-Japan University of Science and Technology Department of Electronics and Communications Engineering New Borg El-Arab City Alexandria21934 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt Egypt-Japan University of Science and Technology Computer Science and Information Technology New Borg El-Arab City Alexandria21934 Egypt Aswan Universty Faculty of Engineering Aswan Egypt Egypt-Japan University of Science and Technology Department of Materials Science and Engineering New Borg El-Arab City Alexandria21934 Egypt

ISBN: (纸本)9798350369328

Cardiac arrest happens when the heart stops beating suddenly, hence, blood stops flowing to the brain and other vital organs. The lack of blood flow to the brain and other organs can cause a person to lose consciousness, become disabled or die if not treated immediately. Restoring heart rhythm can happen by delivering an electrical shock to the patient through the chest wall. With the increase of out-of-hospital cardiac arrest (OHCA) the need for Automatic External Defibrillator (AED) increases. AED is a portable device used to restore normal heart rhythm to patients by delivering an electric shock through the chest to the heart when it detects an abnormal rhythm. It is easy-to-use medical device that can be used by normal person. It analyzes the heart's rhythm and, if necessary, deliver an electrical shock, or defibrillation, to help the heart regain its normal rhythm. In this paper we present a low cost and light weight circuit for mobile Automatic External Defibrillator. © 2023 IEEE.

关键词： Electrocardiograms

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Melanoma Skin Cancer Identification Using Machine Learning Technique

Melanoma Skin Cancer Identification Using Machine Learning T...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： R. Lakshmi B. Arthi Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai TN India Department of Computing Technologies Faculty of Engineering and Technology College of Engineering and Technology SRM Institute of Science and Technology Chennai TN India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Melanoma, a type of skin cancer originating from the cells that produce melanin, has witnessed an alarming rise in its incidence globally, necessitating efficient and accurate detection strategies Being detected early plays a pivotal role in patient prognosis, making the development of precise diagnostic tools imperative. In this research endeavor, we explore the application of optimization algorithms to improve the accuracy and efficiency of melanoma detection using dermoscopy imaging. Leveraging advanced computational techniques, we seek to optimize preprocessing steps, feature extraction, and classification processes. Integration of these optimization methodologies aim to refine the quality of dermoscopy images, extract salient features characteristic of melanoma lesions, and facilitate robust classification using machine learning and deep learning models. This research study intends to contribute to an automated and efficient melanoma detection system, offering early diagnosis and potentially saving lives through timely intervention.

关键词： Accuracy Imaging Melanoma Medical services Green computing Feature extraction Reliability

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PACTDet - An Artificially Intelligent Approach to Detect Pulmonary Illnesses: Pneumonia, Asthma, COVID-19, and Tuberculosis 1

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2nd International Conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2022

作者： Tasmi, Sidratul Tanzila Raihan, Md. Mohsin Sarker Nasif, Atahar Imtiaz Shams, Abdullah Bin Department of Computer Science and Engineering Islamic University of Technology Dhaka1704 Bangladesh Chattogram Bangladesh Department of Computer Science Faculty of Science University of British Columbia 3333 University Way KelownaBCV1V 1V7 Canada The Edward S. Rogers Sr. Department of Electrical Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

ISBN: (数字)9783031313530

ISBN: (纸本)9783031313523

Pulmonary Illness has become very common due to different emerging viruses, bacteria, pollution, and lifestyle. If these diseases are not diagnosed in a patient, they may have a severe impact and fatal conditions in a patient’s life. In this paper, we propose a methodology for the diagnosis of common pulmonary diseases: Pneumonia, Bronchial Asthma, COVID-19, and Tuberculosis based on symptoms. Our research paper functions as a technological doctor, which can detect the probability of a person having a certain category of pulmonary disease. The patient can then take necessary precautions and treatment for an early cure and minimize the severity of the disease. In this research, common classification-based machine learning algorithms: Random Forest, Decision Tree, Support Vector Machine, and Gradient Boosting algorithms were used after preprocessing the dataset from 2 sources. We have developed an Artificial Neural Network (ANN) model that can successfully detect the type of pulmonary disease with an accuracy of 99%. This application can help medical workers in their decision-making process regarding a patient, and the widespread, inexpensive application of this can help save people’s lives globally. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Automated COVID-19 screening framework via Deep Convolutional Neural Network with Chest X-ray Medical Images 6

Automated COVID-19 screening framework via Deep Convolutiona...

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6th International Conference on Information Technology, InCIT 2022

作者： Damkham, Waris Thaipisutikul, Tipajin Supratak, Akara Kraisangka, Jidapa Mongkolwat, Pattanasak Wang, Jja-Ching Mahidol University Faculty of Information and Communication Technology Thailand National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665489126

COVID-19 screening using chest X-rays plays a significant role in the early diagnosis of COVID-19 illness during the ongoing pandemic. Manually identifying this infection from chest X-ray films is a challenging and time-consuming technique due to time restrictions and the competence of radiologists. Also, the manual Covid-19 identification technique is made much more difficult and opaquer by the feature similarity between positive and negative chest X-ray images. Therefore, we propose an automated COVID-19 screening framework that utilizes artificial intelligence techniques with a transfer learning approach for COVID-19 diagnosis using chest X-ray images. Specifically, we employ the transfer learning concept for feature extraction before further processing with modified deep neural networks. Also, Grad-CAM visualization is used for our case study to support the predicted diagnosis. The results of the experiments on the publicly accessible dataset show that the convolutional neural network model, which is simple yet effective, performs significantly better than other deep learning techniques across all metrics, including accuracy, precision, recall, and F-measure. © 2022 IEEE.

关键词： COVID-19

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ChatGPT and Sentiment Analysis: Unveiling Emotional Insights in Textual Data

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Procedia computer science 2025年 259卷 52-60页

作者： Suresh Kumar N Zafar Ali Khan B. Swaminathan S. Prakash S. Jayanthi Ragini Kumari Department of DS & AI Faculty of Science and Technology The ICFAI Foundation for Higher Education Hyderabad Telangana Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad Haryana

Sentiment analysis plays a critical role in understanding public opinion on social media platforms. This research article presents an in-depth analysis of sentiment in ChatGPT-generated tweets using Natural Language Processing (NLP) techniques which has implications for various domains, including market research, brand reputation management, and public opinion analysis. The objective is to improve the accuracy and effectiveness of sentiment analysis on ChatGPT-generated content. The study begins by pre-processing the tweet data, including the removal of punctuation, special characters, and user mentions. Tokenization is applied to convert the tweets into a structured format while eliminating stopwords to focus on meaningful words. Stemming and lemmatization techniques are employed to further enhance word normalization. Visualizations, such as word clouds, provide insights into the most frequently used words in the ChatGPT tweets, uncovering prevalent topics and themes within the dataset. Sentiment analysis is conducted using Text-Blob and nltk libraries, comparing nltk’s Vader Sentiment Intensity Analyzer outperforms TextBlob, achieving an average accuracy of 85% compared to 76%. A machine learning model is constructed using the LinearSVC algorithm, incorporating the Tfidf Vectorizer for feature extraction. The model achieves an accuracy of 89% in sentiment prediction when trained and evaluated on the tweet dataset. To validate the model’s performance, an equalized dataset is created, balancing the number of positive, neutral, and negative tweets. It demonstrates consistent accuracy across different sentiment classes, with average accuracies of 87% for positive sentiment, 92% for neutral sentiment, and 86% for negative sentiment.

关键词： Natural Language Processing (NLP) TextBlob nltk LinearSVC Algorithm TfidfVectorizer

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