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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Zwaid, Jassim Mohammed Ahmed Murad, Masrah Azrifah Azmi Khalid, Fatimah Binti Manshor, Noridayu Al-Jumaily, Abdulmajeed Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Affiliated to University of Anbar Ramadi Iraq Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Multimedia Department Faculty of Computer Science and Information Technology University Putra Malaysia UPM Serdang 43400 Malaysia Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganés Madrid28911 Spain

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Smart UML - Assignment Management Tool for UML Diagrams 4

Smart UML - Assignment Management Tool for UML Diagrams

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Weerasinghe, D.N.H. Thiwanka, K.A.T. Jayasith, H.B.C. Onella Natalie, P.A.D. Samantha Rajapaksha, U.U. Karunasena, Anuradha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system's stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most computer science-related students get assessed in UML modelling, diagram plagiarism isn't considered in most exams since it's a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage. © 2022 IEEE.

关键词： Unified Modeling Language

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A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM

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Intelligence-Based Medicine 2025年 11卷 100206-100206页

作者： Naveed, Sajid Husnain, Mujtaba Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects. © 2025 The Authors

关键词： Drug response recommendation system Drug SMILES Gene expression Glioblastoma K-mer fragmentation LightGBM

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The Role of AI in Predictive UX Shaping the Future of Metaverse Interactions

The Role of AI in Predictive UX Shaping the Future of Metave...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nedal Nwasra Khalil Omar Zahid Hussain Qaisar Software Engineering Department University of Petra Amman Jordan Faculty of Computing & Emerging Technologies Multan Pakistan Computer Science Department University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The Metaverse is defined as a collectively shared, persistent, and contextualized virtual space that will drive next-generation digital interaction facilitated by enhanced extended reality (XR) and edge computing. One of the major challenges within this development area is the proper anticipation and fulfillment of user context needs, preferences, and intentions in real-time. This paper proposes the concept of Predictive User Experience through AI for a turn to solve this challenge. With enabled predictive analytics, machine learning, and real-time data processing, the proposed AI-based predictive UX model dynamically adjusts metaverse interface features, content delivery, and modes of interaction to enhance immersion, all while maximizing user satisfaction. In this paper, we discuss AI's potential for explanatory mechanics in predictive UX and how it shapes the future of metaverse interactions. We propose a conceptual framework that describes how data collection, data processing, and adaptive interface design can be integrated within a feedback-driven architecture that seamlessly predicts and adjusts to user requirements. In this paper, we present a research methodology that integrates qualitative and technical approaches-from data modeling and architecture design up to scenario-based assessments-to validate our UX framework's predictiveness. Hence, we proceed with a use case that exemplifies the applicability of AI-based predictive UX systems for ensuring user presence and interaction consistency within a metaverse educational scenario. The discussion synthesizes and finally concludes the most pertinent findings relevant to user engagement and personalization regarding privacy and ethical considerations without compromising how these technologies can be safely and effectively scaled. We conclude our paper with the opinion that XR interactions driven by AI predictive UX mark a milestone in how applications are created, not just for entertainment and gaming but also

关键词： Adaptation models Metaverse Extended reality Machine learning computer architecture Predictive models Data collection Data models User experience Real-time systems

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Automating Bad Smell Detection in Goal Refinement of Goal Models

Automating Bad Smell Detection in Goal Refinement of Goal Mo...

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作者： Hayashi, Shinpei Asano, Keisuke Saeki, Motoshi Department of Computer Science Tokyo Institute of Technology Tokyo152–8550 Japan Department of Software Engineering Faculty of Science and Technology Nanzan University Nagoya-shi466–8673 Japan

Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment. Copyright © 2022 The Institute of Electronics, Information and Communication Engineers

关键词： Odors

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Facial Recognition Utilizing the CLM Model and Supervised Machine Learning: A Comparative Analysis 25

Facial Recognition Utilizing the CLM Model and Supervised Ma...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Iqtait, Musab Ababneh, Jafar Abu-Jassar, Amer Al-Refai, Mohammad Dmour, Mohammad Arabiat, Mohammad Binsaddig, Ruaa Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa Jordan Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Amman Arab University University College of of Computer Sciences and Information Amman Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan University of Business and Technology College of Business Administration Department of Finance Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Facial recognition is a common challenge in artificial intelligence. We extensively utilized this program in our daily life. Face recognition software was implemented on many phones to protect user identities and promptly recognize Facebook users in photographs. Despite the numerous methodologies proposed to date, the actual implementation of facial recognition continues to pose significant challenges. Distinguishing individuals by a fundamental method relies on various factors, including lighting, postural differences, and partial facial obstruction. This study aims to develop a facial recognition technique utilizing a constrained local model (CLM) and a machine learning algorithm. It is evaluated with KNN, SVR, and CCA. Furthermore, it has employed CLM and CCA to achieve a maximum recognition accuracy of 3.98 MAE. © 2024 IEEE.

关键词： Adversarial machine learning

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QianXun: A Novel Enterprise File Search Framework 25

QianXun: A Novel Enterprise File Search Framework

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Si, Chun Qi, Peng Xue, Hongjia Sun, Yan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing100876 China University of Nottingham Computer Science Faculty of Science and Engineering United Kingdom

ISBN: (纸本)9781665405270

With the deepening of the digital transformation, enterprises have accumulated plenty of electronic files. For users, how to quickly find their desired files from the massive resources is a big challenge, which is also a critical concern for enterprises. Although a series of enterprise search engines have emerged in the market, there is a lack of search systems for enterprise files. In this paper, we design a novel enterprise file search framework, named QianXun, which supports searching in both Chinese and English. By introducing technologies such as automatic text summarization, online file previewing, and Knowledge Graph-enhanced search, our proposed framework improves the efficiency of file search as well as the user experience, which is of great significance to enterprises. © 2022 IEEE.

关键词： Knowledge graph

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Intrusion Detection in Smart IoT Devices for People with Disabilities 8

Intrusion Detection in Smart IoT Devices for People with Dis...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Naveed, Muhammad Usman, Syed Muhammad Satti, Muhammad Islam Aleshaiker, Sama Anwar, Aamir Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Department of Computer Science Islamabad Pakistan Ai Air University Faculty of Computing Department of Creative Technologies Islamabad Pakistan Riphah University Faculty of Computer Science and Software Engineering Islamabad Pakistan School of Computing & Engineering University of West London London United Kingdom

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

An intrusion Detection System (IDS) is a system that resides inside the network and monitors all incoming and outgoing traffic. It prevents unethical activities from happening over the network. With the use of IoT devices, network traffic is also increased. Intruders and hackers are attracted to this network because of its low processing power and openness. IoT has transformed diagnostic and monitoring systems for patients in the healthcare industry. However, a secure network is needed for these health care devices. This research proposes a hybrid model to secure the IoT network from external intrusions. The proposed method consists of preprocessing data with the help of normalization and feature selection by removing high correlated features with the help of the Pearson correlation coefficient and Support Vector Machine (SVM) for classification. The proposed approach has achieved an accuracy of 99.3%, precision of 99.1% and an F-1 score of 99.25% on the standard dataset. Results have been compared with state-of-the-art, and the proposed method outperforms all performance measures. © 2022 IEEE.

关键词： Support vector machines

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Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

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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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Detection of Android Malware using Machine Learning

Detection of Android Malware using Machine Learning

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Fiky, Ahmed Hashem El Elshenawy, Ayman Madkour, Mohamed Ashraf Al-Azhar University Systems and Computer Engineering Faculty of Engineering Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

ISBN: (纸本)9781665412438

Nowadays, smartphones became an integral part of human life due to the great necessity for their daily activities. Most smartphone users are downloading and installing mobile apps without worrying about security. Therefore, smartphones are a winning goal for malware, mainly with Android devices. So, it was necessary to provide an intelligent model for detecting malware applications before installing them on android smartphones. In this paper, an intelligent model using machine learning algorithms is proposed for detecting the malware applications in smartphones based on the static malware analysis technique. The contents of an android application features are analyzed, such as permissions, intents, system commands, and API-calls that were extracted from the application manifest file and source code. The proposed model is applied on two separate datasets DREBIN and MALGENOME. The experimental results are compared with some other similar researches, and their results were analyzed. The results indicate that the performance of the proposed model outperforms other similar models for some selected classifiers and can reliably detect both malware and benign Android applications with high accuracy. © 2021 IEEE.

关键词： Android malware

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