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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Wismantoro, Yohan Westri Kekalih Susilowati, M.G. Himawan, Heribertus Dian Nuswantoro University Faculty of Economics and Business Semarang Indonesia Soegijapranata Catholic University Faculty of Economics and Business Semarang Indonesia Dian Nuswantoro University Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350339215

Virtual Reality Technology for tourism (VR Tourism) can be a means to boost tourist visits. However, this opinion is still controversial. VR Tourism, as a VR technology, provides a pleasant experience for its users and is supposed to encourage people to visit a tourist site. As a result, evaluating VR tourism's effectiveness in mood management becomes critical. This study develops a research model on how Mood Management Theory (MMT) influences visit intentions. This study uses an experimental approach by providing an experience using VR Tourism Making Batik with natural dyes. The data were analyzed using a path analysis approach processed using SmartPLS 3.0. The findings indicate that Entertainment Experience, Education Experience, Esthetic Experience, and favourably impact the perception of positive mood enhancement. However, the intention to visit does not display any significant association with the perception of positive mood improvement. © 2023 IEEE.

关键词： Virtual reality

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The Intensive Model Design using FL for Smart HC System Design 4

The Intensive Model Design using FL for Smart HC System Desi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Limited India India Sri Balaji University Faculty of Management It & Business Analytics Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Using cutting-edge technology like wearables, internet access of Things, and smartphones, smart healthcare has become a crucial component of health policy services. This revolutionary method enables seamless connection and ongoing information access between patients, medical equipment, the facilities, allowing for intelligent responses to a variety of demands within the medical ecosystem. No matter when or where they are, patients may get urgent assistance, diagnostic services, and monitoring via the digital platform of the Smart Health care Management systems (SHMS). This research emphasizes innovation with direct healthcare services and suggests the use of the Smart the management of healthcare Examination using Fuzzy Decision-Taking (SHME-FDM) technique to measure the effectiveness of technology integration. Furthermore, the Fuzzy Asymmetric Hierarchy Process-Technique for Orders of Preference by The sameness to Ideal Solution (Fuzzy AHP-TOPSIS) is used to evaluate the privacy protection and healthcare data inside the SHMS. In order to make predictions about healthcare, the research uses a fuzzy-based neural network. Through experimental analysis, reliability, accuracy, and error rates are evaluated. Notably, a security risk analysis highlights the proposed fuzzy model's potential for protecting healthcare data by demonstrating its improved risk assessment performance when compared to current methods. © 2024 IEEE.

关键词： Risk assessment

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Enhancing School and College Attendance Using Advanced Technology 3

Enhancing School and College Attendance Using Advanced Techn...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Budiarto, Mukti Audiah, Sipah Astuti, Eka Dian Sanjaya, Yulia Putri Ayu Firli, M. Zaki University of Raharja Faculty of Informatics Engineering Tangerang Indonesia Cai Sejahtera Indonesia Faculty of Digital Business Tangerang Indonesia Asosiasi Dosen Indonesia Faculty of Digital Business Tangerang Indonesia Alfabet Inkubator Indonesia Faculty of Retail Management Tangerang Indonesia Rey Faculty of Computer Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

In today digital era traditional manual attendance systems in educational institutions are both inefficient and outdated. This study assesses the impact of implementing technology-driven attendance systems developed using the Waterfall Model, which promises to revolutionize educational settings by enhancing operational efficiency and reducing administrative burdens. The research involved deploying automated attendance software across various institutions and comparing its efficacy with traditional methods in terms of time efficiency, accuracy, and cost. The results indicate that technology-based systems significantly reduce the time and effort required for attendance recording, minimize manual errors, and offer real-time data that helps quickly identify attendance patterns and issues. These improvements lead to enhanced student engagement and accountability. Moreover the transition to automated systems results in considerable administrative cost savings by decreasing reliance on extensive paperwork, thereby allowing for a better allocation of resources towards improving the educational experience. Ultimately, the study confirms that adopting modern attendance monitoring technologies not only streamlines processes but also enhances the educational framework, making institutions more efficient and responsive. This shift benefits administrative and educational outcomes and sets a robust foundation for future educational practices, highlighting the critical role of technology in advancing educational environments. © 2024 IEEE.

关键词： Educational technology

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech Technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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Review and Proposed Digital Twin Model for Sustainable Smart University Asset Management 1

Review and Proposed Digital Twin Model for Sustainable Smart...

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1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023

作者： Fashal, Nada A. Elkhayat, Ghada A. Elmorsy, Shaimaa A. Alexandria University Faculty of Business Information Systems and Computers Department Alexandria Egypt Alexandria University Faculty of Sciences Mathematics and Computer Sciences Department Alexandria Egypt

ISBN: (纸本)9798350394689

Egyptian educational institutions suffer from poor asset management and inefficient energy management for buildings which results in poor performance, low return on investment, inability of remote management, high expenses and poor decision making. Current problems result in wasting limited resources and incurring high utility costs. Since digitization plays an important role in achieving sustainability and stretching resources and infrastructure, the Egyptian government is working on related adjustments in the higher education sector. In this paper, we focus on assets represented by educational buildings. The main objective of the paper is to make good use of the Digital Twin Technology for better information visibility in educational institutions, taking a building in the new Alexandria University Abis Campus as an example. Using Digital Twin based Information systems for monitoring the most precise level of asset utilization and building conditions will allow better decision making and better investments. After a detailed presentation of the digital twin related concepts and a literature review, the paper proposes a model for sustainable smart university asset management together with its technology requirements and its positive impact on campus. © 2023 IEEE.

关键词： Sustainable development

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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Big Data Analytics in loT Challenges and Opportunities 9

Big Data Analytics in loT Challenges and Opportunities

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Kirubakaran, S. Vidhya, R. Prlya, V. Kalai ShyamPrakash, D. Dhotre, Sunita Balaji, N. Alangudi Cmr College of Engineering And Technology Department of Computer Science And Engineering Telangana Hyderabad501401 India Sri Krishna College of Technology Department of Computer Science And Engineering Tamil Nadu Coimbatore641042 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Puduvoyal601206 India Sri Eshwar College of Engineering Deportmem of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India College of Engineering Department of Computer Engineering Maharashtra Pune411043 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350365092

The intricate interplay between Big Data analytics and the Internet of effects is explored in this paper. The focus of this exploration is the integration of Big Data analytics into Internet of effects surroundings, probing into the complex geography and examining both the advantages and disadvantages. In the current period, where Internet of effects(loT) bias induce unknown quantities of data, the investigates the challenges associated with the collection and analysis of massive datasets. issues are under consideration, including data security, sequestration, scalability, and real-time processing. Again, openings lie in gaining pivotal perceptivity, enhancing decision- making processes, and fostering invention. This exploration provides a comprehensive understanding of the commerce between Big Data analytics and the Internet of effects(IoT), offering a precious resource for companies, academic institutions, and government officers seeking to navigate and acclimatize to this fleetly expanding confluence of technologies. © 2024 IEEE.

关键词： Scalability

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Improved Diagnosis Accuracy Using Machine Learning for Plant Diseases 10

Improved Diagnosis Accuracy Using Machine Learning for Plant...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Rakhra, Manik Singh, Dalwinder Singh, Arun Sharma, Vikrant Dhakshinesh, S. Islam Zim, Md Khadimul Thapar, Puneet Kaur, Baljinder Singh, Harpreet School of Computer Science and Engineering Lovely Professional University Phagwara India Lovely Professional University Jalandhar Department of Computer Science &engineering Phagwara India Graphic Era Hill University Department of Computer Science &engineering Uttarakhand Dehradun India Ghent University Master of Science in Business Engineering Faculty of Economic and Business Administration Ghent Belgium

ISBN: (纸本)9798350382471

In agricultural fields, leaf diseases are a prevalent problem, and they often result in significant crop losses. The early discovery of leaf diseases is critical for reducing the amount of crop damage caused by the disease and preventing its further spread. Deep learning methods, such as convolutional neural networks (CNN), have shown a lot of potential in recent years for accurately diagnosing leaf diseases. In this article, we present a literature review on the use of CNNs for the detection of leaf diseases. We discuss the various architectures and techniques used to increase the accuracy of disease detection. In addition, we go over the datasets that are used for training and testing CNN models, as well as the challenges and potential future possibilities for the application of CNNs to the detection of leaf diseases. © 2023 IEEE.

关键词： agriculture convolutional neural networks deep learning leaf disease

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An Architecture Supporting Intelligent Mobile Healthcare Using Human-computer Interaction HCI Principles

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computer systems science & Engineering 2022年第2期40卷 557-569页

作者： Mesfer Alrizq Shauban Ali Solangi Abdullah Alghamdi Muhammad Ali Nizamani Muhammad Ali Memon Mohammed Hamdi College of Computer Science and Information Systems Najran UniversityNajranSaudi Arabia Faculty of Engineering&Technology University of SindhJamshoro78060Pakistan

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several ***,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health *** raises potential challenges for intelligent monitoring of patient's *** this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design *** HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare ***,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving *** intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related *** care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent *** HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.

关键词： Human computer interaction mhealthcare user-centric design sensor network nternet-of-things

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