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5th International Conference on information Technology for Education and Development, ITED 2022

作者： Ndunagu, Juliana Ngozi Jimoh, Rasheed Gbenga Chidiebere, Ugwuegbulam Opeoluwa, George Deborah. National Open University of Nigeria Department of Computer Science Abuja Nigeria University of Ilorin Faculty of Communication and Information Sciences Department of Computer Science Ilorin Nigeria ACETEL - National Open University of Nigeria Management Information System Abuja Nigeria

ISBN: (纸本)9781509064229

The data gleaned from the National Open University of Nigeria's (NOUN) E-ticketing system was studied in this paper. NOUN is one of the Open and Distance Learning (ODL) institutions, where students and their facilitators are in different physical locations. Multinomial Naive Bayes algorithm, preferred using 'intent' for its classification method for the chatbot system. Within 4 months of the launch of the NOUN E-ticketing system, 38,263 tickets (students' complaints and inquiries) were generated and 30,601 have been manually responded to and closed while the remaining 7,662 tickets are still in progress. The chatbot's goal is to respond to students inquiries quickly and efficiently while easing the burden on the management system. With the availability of chatbot, students' responses will be automated and accessible 24/7. The NOUN chatbot will increase student engagement, strengthen communication and create a seamless interaction for both the ODL institutions and its students all together culminating to a robust congenial student-ODL relationship ultimately leading to a higher attraction rate and more importantly, a lower attrition rate, not just in ODL institutions alone, but to other conventional higher institutions. © 2022 IEEE.

关键词： Students

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Manufacturing a Smart Locker Security system for Public Spaces using E-KTP as a Primary Access 9

Manufacturing a Smart Locker Security System for Public Spac...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Pramono, Andi Febriantono, M. Aldiki Agustina, Ira Audia Ananta Wijaya, Ida Bagus Widia Primadani, Tiara Ika Budiman, Satrio Arif Bina Nusantara University Interior Design Department School of Design Jakarta Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta Indonesia Information System Computer Science Faculty Brawijaya University Malang Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Everyone requires protection of belongings, especially security in public spaces such as locker rooms. Several locations in public areas keep hiring conventional security systems based on keys and padlocks, while others provide merely keyless lockers guarded by personnel. As a result, the user must ensure that the goods deposited with the locker storage officer are adequately secured. Numerous technologies have been used in conventional security systems, ranging from the difficult-to-imitate shape of keys and padlocks to the use of a padlock combination of numbers. The security system for storage places is also a purpose of information technology. This research aims to provide information on security systems in public spaces, especially shared access lockers. This project employs an experimental approach to create a prototype in the form of a smart locker that incorporates information technology. The lockers were constructed utilising plywood with an HPL finish on the exterior and PVC sheets on the interior. The dimensions of the locker were determined using the standard sizes of small and large luggage. Additionally, the approach to materials is addressed to minimise production waste. This research employs RFID technology that runs on the Arduino Nano to supplement the locker security system. Numerous RFID cards, including E-Toll cards and E-KTP, have been tested as the principal access method for opening and closing the smart locker's door. © 2022 IEEE.

关键词： Radio frequency identification (RFID)

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Model-Based Testing of Access Control Requirement in Multi-tenant Application: An Extensive Life Cycle 1

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3rd International Conference on computer science, Electrical and Electronic Engineering, ICCEE 2021

作者： Duraisamy, Gunavathi Ghani, Abdul Azim Abd Zulzalil, Hazura Abdullah, Azizol Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Selangor Serdang Malaysia Faculty of Computer Science and Information Technology Department of Communication Technology and Networking Universiti Putra Malaysia Selangor Serdang Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Sungai Long Campus Selangor Kajang Malaysia

ISBN: (数字)9789811697814

ISBN: (纸本)9789811697807

Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity of model-based testing is the modelling activity. Models are used to explain and represent the behaviour of the product. The requirement will be translated into models and those models will be used in generating the test cases automatically in model-based testing. The modelling activity clarifies test requirement and contribute to automatic generation and execution of large test suites with tool support. Model-based testing provides numerous advantages in testing;mainly on achieving appropriate test coverage and reduce manual effort and time spent since it can execute the test cases automatically. Despite all the benefit it can provide, there are some drawbacks too. However, the challenges faced can overcome by implementing proper test modelling activity and by adopting a well-versed test management activity. Thus, in this paper, we have studied the existing testing process, the taxonomy of model-based testing and model-based testing steps and proposed an extensive lifecycle for model-based testing with detail activities and steps to be carried out in order to test and validate access control requirement for multi-tenant application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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ROD-WGAN hybrid: A Generative Adversarial Network for Large-Scale Protein Tertiary Structures

ROD-WGAN hybrid: A Generative Adversarial Network for Large-...

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International Conference on computer and Applications (ICCA)

作者： Mena Nagy A. Khalaf Taysir Hassan A. Soliman Sara Salah Mohamed Information System Department Faculty of Computer and Information Assiut University Assiut Egypt Mathematics and Computer Science Department Faculty of Science New Valley University New Valley Egypt

The tertiary structures of proteins play a critical role in determining their functions, interactions, and bonding in molecular chemistry. Proteins are known to demonstrate natural dynamism under various physiological conditions, which enables them to adjust their tertiary structures and effectively interact with the surrounding molecules. The present study utilized the remarkable progress made in Generative Adversarial Networks (GANs) to generate tertiary structures that accurately mimic the inherent attributes of actual proteins, which includes the backbone conformation as well as the local and distal characteristics of proteins. The current study has introduced a robust model, ROD-WGAN hybrid, that is able to generate large-scale tertiary protein structures that greatly mimic those found in nature. We have made several noteworthy contributions in pursuit of this objective by integrating the ROD-WGAN model with a hybrid function that incorporates adversarial loss, perceptual loss, distance-by-distance loss, and structural similarity loss. Through this innovative approach, we achieved remarkable results, particularly in the generation of protein structures of considerable length. The ROD-WGAN hybrid model has transcended the limitations inherent in the ROD-WGAN model, demonstrating its capability in the successful synthesis of high-quality proteins, characterized by a length of up to 256 amino acids. This accomplishment serves as a demonstrating the effectiveness and potential of our proposed methodologies. Furthermore, these generated protein structures serve as a valuable resource for data augmentation in crucial applications such as molecular structure prediction, inpainting, dynamics, and drug design. Interested individuals can access the data, code, and trained models at https://***01/ROD-WGAN-and-ROD-WGAN-hybird-models.

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Development of Social Media Analytics system for Emergency Event Detection and Crisis Management

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computers, Materials & Continua 2021年第9期68卷 3079-3100页

作者： Shaheen Khatoon Majed AAlshamari Amna Asif Md Maruf Hasan Sherif Abdou Khaled Mostafa Elsayed Mohsen Rashwan Department of Information System College of Computer Science and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Faculty of Computers and Artificial Intelligence Cairo UniversityEgypt Faculty of Computing&Information Cairo UniversityEgypt Faculty of Engineering Cairo UniversityEgypt

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made *** response authorities,law enforcement agencies,and the public can use this information to gain situational awareness and improve disaster *** case of emergencies,rapid responses are needed to address victims’requests for *** research community has developed many social media platforms and used them effectively for emergency response and coordination in the ***,most of the present deployments of platforms in crisis management are not automated,and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response *** seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become *** research project aims to develop a generalized information Technology(IT)solution for emergency response and disaster management by integrating social media data as its core *** this paper,we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief *** specifically,we applied state-of-the-art Natural Language Processing(NLP),Machine Learning(ML),and Deep Learning(DL)techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a *** a proof of concept,a case study on the COVID-19 pandemic on the data collected from Twitter is presented,providing evidence that the scientific and operational goals have been achieved.

关键词： Crisis management social media analytics machine learning natural language processing deep learning

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection system(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab *** false information is harmful to society and *** and detecting the spread of fake news in Arabic becomes *** artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake ***,fake news in Arabic is identified by utilizing AI *** article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic *** HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful ***,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and ***,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN *** performance validation of the HPOHDL-FND technique is tested using two Arabic *** outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

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computer science, information Technology and Engineering (ICCoSITE), International Conference on

作者： Larissa Navia Rani Yuhandri Yuhandri Information System Computer Science Faculty Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Computer Science Faculty Universitas Putra Indonesia YPTK Padang Indonesia

In this study aimed to measure the level of similarity between two logos, both those that look different and those that look the same. This can be realized by forming a logo image database that is stored in a logo image database derived from various existing logo image data. This research uses 4 logo images as data testing and 210 image data for the database as data training. All of the logo images used come from the Ministry of Law and Human Rights of the Republic of Indonesia (Kemenkumham RI) West Sumatra Regional Office. The size images are color images with a pixel size of 320 x 320 pixels, the purpose of which is for the process of dimensional uniformity of the images to be studied. This research uses Content Base Image Retrieval (CBIR) method to search for images from a large image database than using Convolutional Neural Network (CNN) type Residual Network (ResNet-18) Architecture to get the similarity score accurately. The result of this implementation is the formation of an automatic distribution of training images and validation images with 147 training image data values (70%) and 63 validation images (30%) of the 210 existing images. The result of this research is producing the algorithm to implement the method and the tool software application to measure the similarity of logo images. The accuracy of this tool is 93.65% with a total of 84 iterations.

关键词： Training Image retrieval Software algorithms computer architecture Feature extraction Software Convolutional neural networks

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Development of Knowledge Management system with Soft system Metodhology in Aquatic Organization 9

Development of Knowledge Management System with Soft System ...

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9th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2022

作者： Widi, Anugerah Sediyono, Eko Hartomo, Kristoko D. Sembiring, Irwan Iriani, Ade University of Multi Data Palembang Information System Faculty of Computer Science and Engineer Palembang Indonesia University of Kristen Satya Wacana Faculty of Computer Science Salatiga Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Changes in coaches and administrators in an aquatic organization often hinder the coaching of athletes that has been carried out. The occurrence of obstacles in the transfer of knowledge from coaches to administrators, administrators to administrators and coaches to other trainers becomes an obstacle when differences are found in the explicit knowledge of each subject such as training periodization, activity reports, meeting results reports, coaching evaluation reports and other important documents that have not been well documented into a knowledge management. The method used in this research is the Soft system Methodology (SSM) approach. In its approach process, SSM uses CATWOE analysis. The purpose of this research is to develop a knowledge management system that can simplify the process of acquisition, storage, sharing, and reuse. © 2022 Institute of Advanced Engineering and science (IAES).

关键词： aquatic organitazion Knowledge Management softsystem methodology

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 1-11页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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