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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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Enhancing Software Cost Estimation Using Feature Selection and Machine Learning Techniques

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computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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Factors Influencing Participation in Data Crowdsourcing: A Systematic Literature Review 8

Factors Influencing Participation in Data Crowdsourcing: A S...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Panjaitan, Re. Miracle Setiawan, Budi Darma Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350342604

Crowdsourcing is becoming a trending concept used by many organizations, without being limited to a particular field. As of 2018, crowdsourcing was used by 85% of the largest worldwide companies. This trend of crowdsourcing would be increasing as it offers numerous advantages to companies in achieving their goals, such as the capability of obtaining data and information from the crowd. The crowd-provided data and information contain the essence of human ingenuity, which enables companies to do problem-solving with a human-centered approach. The more contributors participate, the more likely the crowdsourcing program will succeed. Several studies give different lists of factors that influence the participation rate of the crowd. However, there's a lack of discussion about the factors and it leads to a situation where organizations need to examine many factors when developing a crowdsourcing platform to get great crowd engagement. Therefore, this study conducted a Systematic Literature Review (SLR) to perform screening and analysis of preceding research systematically. The factors in each of these literatures are generated by considering the case of the study or the region of the people related to the research. This diversity helps identify the factors influencing contributor participation in data crowdsourcing from research all over the world. A study reflecting on these research findings would help answer research questions thoroughly. This study found that there are 19 factors that positively influence contributor participation in data crowdsourcing out of 26 factors found in the 16 selected documents. © 2023 IEEE.

关键词： Crowdsourcing

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GLCM Feature Extraction and PCA for Tuberculosis Detection with Neural Network

GLCM Feature Extraction and PCA for Tuberculosis Detection w...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Alfa Farah, M. Nafis Hayyin Suristiyanti, Wiwiek Ibad, Sholihul Pramunendar, Ricardus Anggi Fajar Shidik, Guruh Dian Nuswantoro University Faculty of Computer Science Postgraduate Program in Informatics Engineering Indonesia Dian Nuswantoro University Faculty of Computer Science College Teacher in Informatics Engineering Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Automatic recognition system for medical images is quite a challenging job in the medical image processing field. X-rays, CT, and MRI all provide medical pictures and other modalities which are utilized for diagnostic purposes. As in medical sector, detecting tuberculosis (TB) is a very important stage before further treatment is carried out. Human interpretation of a vast array of X-ray pictures can result in detection mistakes, so an automatic recognition system is needed that can detect TB disease. In this study, we use a dataset with two classes and extract GLCM-based texture features from each class, and apply them to a two-layer feed-forward neural network, which gives a classification rate of 99%. © 2022 IEEE.

关键词： Diagnosis

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Modified MobileNetV2 with Convolutional Block Attention Module for Facial Expression Recognition

Modified MobileNetV2 with Convolutional Block Attention Modu...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Nafi'an, Muh. Hafidh Ilmi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma Brawijaya University Computer Science Faculty Informatics Engineering Department Malang Indonesia

ISBN: (纸本)9798350382914

Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person's emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person's emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF) public benchmark dataset. Preprocessing is done with image augmentation to enrich the training data by resizing, center cropping, random horizontal flipping, random affine, and random rotation. The proposed method is evaluated by 5-fold cross-validation and compared with MobileNetV2 without modification and state-of-the-art methods. Experimental results show that the proposed model outperform all comparative methods by achieving a 5 -fold cross-validation accuracy of 94.387%. © 2023 IEEE.

关键词： Emotion Recognition

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Challenges on Cloud Computing Migration Strategy for Music I...

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2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Wirasti, Hijir Della Seta, Henki Witarsyah, Deden Prabu, Hamonagan K Azzahro, Ayu Hananto, Bayu Telkom University Faculty of Engineering Industry Bandung Indonesia Universitas Pembangunan Nasional Veteran Informatics Study Program Computer Science Faculty Jakarta Indonesia

ISBN: (纸本)9798350307306

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies. © 2023 IEEE.

关键词： Cloud computing architecture

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