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IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Ehab Ebrahim Mazen Sayed Mahmoud Youssef Hisham Essam Salma Abd El-Fattah Dina Ashraf Omar Magdy Reem ElAdawi Computer and Systems Engineering Department Ain Shams University Cairo Egypt Siemens Digital Industries Software Cairo Egypt

This paper presents Alfred, a web-based artificial intelligence (AI) chatbot powered by the Rasa framework and designed to facilitate Agile software release planning by using two machine learning (ML) models to estimate the time for each task selected by the project managers and recommend the optimal resource to be assigned to each task. Extreme gradient boosting (XGBoost) is used for time estimation, and the model’s performance evaluated using accuracy, adjusted coefficient of determination (R2), and fitting a line between the estimation and actual values. The results obtained were 82% accuracy and 98% adjusted R2 score. K-nearest neighbors (K-NN) is used for resource recommendation in a one-shot learning manner. The application integrates a dashboard with useful visualizations and an interactive smart assistant AI-based chatbot, providing project managers with a tool to make more informed decisions. Additionally, an approach is used to indicate the model’s confidence in tasks estimation of given tasks based on the amount of available data showing the history in the system to give project managers more insights. The approach uses a keywords frequency indication algorithm based on the summary tokens to group the task estimates into one of three categories of confidence: high, medium, or low.

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Blood Cell Classification and Counting in Hematology through Deep Learning

Blood Cell Classification and Counting in Hematology through...

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Sustainable Expert systems (ICSES), International Conference on

作者： P. Ananthi K Nirmala Devi T. E. Ramya P. Shanmugapriya T. Aradhya Department of Computer Technology Kongu Engineering College Perundurai Erode Tamil Nadu Department of Computer Applications Madanapalle Institute of Technology & Science M.Sc Software Systems Kongu Engineering College Perundurai Erode Tamil Nadu

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Analytical models Accuracy Computational modeling Cells (biology) Real-time systems Reliability Object recognition Medical diagnosis Blood Medical diagnostic imaging

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj Hafiz Tayyab Rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

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A Binary PSO Based Ensemble Under-Sampling Model for Rebalancing Imbalanced Training Data

arXiv

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arXiv 2025年

作者： Li, Jinyan Wu, Yaoyang Fong, Simon Tallón-Ballesteros, Antonio J. Yang, Xin-She Mohammed, Sabah Department of Computer and Information Science University of Macau Taipa China Department of Languages and Computer Systems University of Seville Seville Spain Design Engineering and Math School of Science & Technology Middlesex University London United Kingdom Department of Computer Science Lakehead University Thunder Bay Canada

Ensemble technique and Under-sampling technique are both effective tools for resolvig imbalanced dataset classification problems, which commonly denotes the quantitative imbalance of a binary class dataset where minority class is the target class. In this paper, a novel ensemble method absorbing the advantages of both ensemble learning for biasing classifiers and a new evolutional under-sampling method is proposed. The under-sampling method is named Binary PSO instance selection, it gathers with ensemble classifiers to find the most suitable length and combination of majority class samples to build a new dataset with minority class samples. The proposed method adopts multi-objective strategy, objectives of this method is to improve the performances of imbalanced classification and to guarantee the maximum integrity of the original dataset. We examined our proposed method, the Binary PSO instance selection by comparing its performance of processing imbalanced dataset with several other conventional basic ensemble methods. Experiments are also conducted with Binary PSO instance selection wrapping with ensemble classifiers for further improvement of imbalanced classification. Based on comaprison experiments, our proposed methods outperform single ensemble methods, state-of-the-art under-sampling methods as well as their combinations with traditional PSO instance selection algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Data assimilation

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Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

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International Journal of Information Technology (Singapore) 2023年 1-22页

作者： Salah, Abdelrahman Adel, Ghada Mohamed, Hussein Baghdady, Youssef Moussa, Sherin M. Department of Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo 11351 Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Laboratoire Interdisciplinaire de l’Université Française d’Égypte (UFEID Lab) Université Française d’Égypte Cairo 11837 Egypt

Considering recent smart home solutions that automate home appliances through voice commands, elderly people and/or with special needs prefer using their mother-tongue. However, most of current systems adopt the English language, making it harder for non-native English speakers, while personalization is not heavily considered. In this paper, we propose the Arabic Personalized Control of Things (APCoT) system that achieves inhabitant-tailored home automation through Arabic voice commands based on the user’s preferences to control a home device. A new dataset is created for a wider range of Arabic voice commands to interact with home appliances rather than just turning them on/off. Internet of Things, voice and speech recognition are utilized to attain personalization according to the inhabitants' voiceprints, while infrared is adopted to control non-smart devices supporting infrared signals. As a Proof-of-Concept, three home appliances are adopted: TV, Lights and Bluetooth speaker. Machine learning algorithms are customized for Arabic voice recognition, integrating face and hand gestures recognition to provide an additional facility for inhabitant’s identification and sending control commands respectively. The experimental results indicate that the best average accuracies obtained for Arabic voice recognition were 99.63% for a single speaker and 95.4% for multi-speakers, with average response times 0.141s and 14.29s respectively. For speech recognition, Google online API and a customized Vosk offline pretrained model achieved average accuracies of 95% and 90%, average response times 0.7553s and 0.3933s respectively. The face and hand gestures recognition achieved average accuracies of 90% and 98%, average response times 0.5934s and 15.62049s respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Arabic commands Elderly people Home automation People with disabilities Smart home Speech and voice recognition

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COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals

arXiv

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arXiv 2023年

作者： Shati, Asmaa Hassan, Ghulam Mubashar Datta, Amitava Department of Computer Science and Software Engineering University of Western Australia Australia Department of Information Systems King Khalid University Saudi Arabia

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

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computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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AI-Driven Virtual Mock Interview Development

AI-Driven Virtual Mock Interview Development

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Prabhat Kumar Mishra Arun Kumar Arulappan In-Ho Ra Thanga Mariappan L Gina Rose G Young-Seok Lee School of Computer Science Engineering and Information Systems VIT University Vellore India School of Computer & Software Kunsan National University Gunsan Republic of Korea Department of Computer Technology Anna University Chennai India

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from ₹3000 to just ₹300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners

关键词： Costs Large language models Learning (artificial intelligence) Educational technology Transforms Machine learning Natural language processing Mirrors Interviews Standards

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

引用

Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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