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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Javed, Salman Akhtar, Rizwan Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Dept. It and Computer Science Haripur Pakistan

ISBN: (纸本)9798350394764

Demand forecasting is viewed as a complex challenge for industries as it relies heavily on various factors. The Supply Chain Management (SCM) integrates processes vital for corporate environments across industries. This paper presents a comprehensive data-driven framework for sales demand forecasting in SCM aiming to enhance accuracy of forecasts. Using Rossmann Stores data case study the sales are predicted up to 6 weeks ahead with a comparative analysis conducted for long-term predictions. Multi-dimensional data complexity is addressed by identifying relevant features and selecting appropriate algorithms. The proposed framework utilizes four data-driven machine learning algorithms: Extreme Learning Machine (ELM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) aiming to construct a high-precision sales demand forecasting model. The experimental validation using Rossmann Stores data demonstrates the effectiveness of the proposed framework, highlighting its significance in improving the accuracy of forecasts in SCM and enhancing decision-making processes. © 2024 IEEE.

关键词： Forecasting

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Examiti: Revolutionizing Exam Paper Creation and Quality Evaluation Using Artificial Intelligence 4

Examiti: Revolutionizing Exam Paper Creation and Quality Eva...

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Jawwad, Muhammad Zaryab, Syed Muhammad Chohan, Abdul Rafay Ahmed, Muhammad Rohan Khan, Muhammad Mubashir Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science & I.T. Karachi Pakistan

ISBN: (纸本)9798331504342

Teachers often spend a significant amount of time and effort creating and managing exams to meet Outcome-Based Education (OBE) standards and ensure the quality of examination papers, which can take away from their primary focus on educating students. Examiti is an innovative platform that automates the examination management process, utilizing AI-powered evaluation through the Lama 2.0 model to assess the quality, difficulty, and originality of exam papers. The platform allows educators to categorize and store questions in a dynamic question bank facilitating quick paper assembly. Its reactive editor simplifies document formatting so that teachers can concentrate on producing high-quality content. This paper outlines the development and testing of Examiti, highlighting its multi-layered testing strategy and user-centered design, which provide a reliable, scalable, and easy-to-use exam management system. Examiti possesses the capability to significantly improve the exam experience for students in educational institutions. © 2024 IEEE.

关键词： Students

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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The Influence of Kanban Agile Methodology on Software Project Management: A Survey Method 10

The Influence of Kanban Agile Methodology on Software Projec...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Ahmed, Irfan Munir, Fatima Nafees, Fouzia Mahmood, Jabar Chaudhry, Shehzad Ashraf Javaid, Maryam Batool, Khadija University of Sialkot Dept. of Computer Science Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain Hangzhou China College of Engineering Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates International Institute of Science Arts and Technology Dept. of Computer Sciences Gujranwala Pakistan

ISBN: (纸本)9798331532895

Software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods. © 2024 IEEE.

关键词： Agile DSDM Kanban Quality Enhancement Scrum SPM XP

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Multi-label Bengali Abusive Comments Classification using Problem Transformation Method 20

Multi-label Bengali Abusive Comments Classification using Pr...

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20th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2023

作者： Khan, Tahsin Tasnia Hassan, Abid Ahamed, Md Faysal Islam, Samiul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh North South University Dept. of Electrical & Computer Engineering Dhaka Bangladesh International Standard University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306767

Sentiment analysis on the Bengali language is limited to binary and multi-class classifications. This paper focuses on the multi-label classification of the Bengali abusive social media comments dataset having 10220 rows of different social media negative comments using the problem transformation method. The texts are categorized into five labels: toxic, threat, obscene, insult, and racism. Three different multi-label approaches: Binary Relevance, Label Powerset, and Classifier Chain combined with three popular machine learning algorithms: Multinomial Naive Bayes, Random Forest, and Logistic Regression are implemented in this research. This study evaluates the performance of these models by applying them to various classification scenarios, considering five-label, four-label, three-label, and two-label classifications independently. Our research outcomes are evaluated with some performance indicators: Accuracy, Precision, Recall, F1-Scores, Confusion matrix, Macro-average, and Hamming score. The combination of Label Powerset and Logistic Regression outperformed the other two classifier-model combinations because of their quick adaptation to the dataset with an accuracy of 88.07% for five label classifications, 90.12% for four label classifications, 90.56% considering three label classifications and 92.67% for two label classification individually. With 88.07% accuracy, the Label Powerset with Logistic Regression approach can be applied to classify five types of sentiment in a single text. © 2023 IEEE.

关键词： Classification (of information)

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Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Comparison of Faster RCNN and YOLO V3 for Video Anomaly Loca...

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2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Anoopa, S. Salim, A. Nadera Beevi, S. College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala India Government Engineering College Thrissur Dept. of Computer Science and Engineering Kerala India Tkm College of Engineering and Technology Dept. of Computer Applications Kerala India

ISBN: (纸本)9798350334463

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Convolutional neural networks

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A Security Aware Lighting Control System for Public Open Spaces: An IoT Powered Approach

A Security Aware Lighting Control System for Public Open Spa...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Firdhous, Mohamed Fazil Mohamed Elbreiki, Walid Abdullahi, Ibrahim Briki, Wesam Arlimatti, Shivaleela University of Moratuwa Dept. of Information Technology Moratuwa Sri Lanka College of Computer Technology Dept. of Computer Science Benghazi Libya Ibrahim Badamasi Babangida University Dept. of Computer Science Niger State Lapai Nigeria Higher Institute of Engineering Technologies Dept. of Computer Science Benghazi Libya Dr. D y Patil Prathisthan's College of Engineering Dept. of Computer Science and Engineering Maharashtra Kolhapur India

ISBN: (纸本)9798350313796

Cities world over are becoming more congested as people moving into them for better living conditions. Such fast migration towards cities puts heavy demands on city resources. One of the resources that suffers an impact almost immediately is the open spaces in cities. Open spaces are the public spaces that are accessed by people freely for different purposes. Open spaces help citizens maintain a healthy life by relieving stresses. The open spaces visited by people at night must provide adequate lighting for them to engage in their activities. It is seen that many open spaces waste energy as the lights have been on throughout the nights irrespective of usage of the space. In this paper, the authors propose a security aware lighting control system that helps minimize the energy waste without compromising on security. A prototype implementation was tested and the results were positive promising real field implementations. © 2023 IEEE.

关键词： Control systems

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Metaverse Interview Room Creation With Virtual Interviewer Generation Using Diffusion Model

Metaverse Interview Room Creation With Virtual Interviewer G...

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2023 IEEE Asia-Pacific Conference on computer Science and Data Engineering, CSDE 2023

作者： Si, Jongwook Yang, Seongeun Kim, Daemin Kim, Sungyoung Kumoh National Institute of Technology Dept. Computer Ai Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Dept. Computer Engineering Gumi Korea Republic of

ISBN: (纸本)9798350341072

Virtual interviews are an effective way to respond quickly to the changing trends of our time and adapt flexibly to the hiring processes of various organizations. Through this method, applicants have the opportunity to practice their interview skills and receive feedback, greatly aiding their job preparation. Additionally, experiencing a virtual interview environment that is similar to an actual one enables them to adapt more easily to a variety of new interview situations. This paper delves deeply into the virtual interview environment implemented by combining cutting-edge metaverse technology and generative AI. Specifically, it focuses on creating an environment utilizing realistic Diffusion models to generate interviewers, enabling the provision of scenarios that are similar to actual interviews. © 2023 IEEE.

关键词： Virtual reality

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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