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6th International Conference on Advancements in computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

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The Indian healthcare system turns to digital health: eSanjeevaniOPD as a national telemedicine service

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Journal of information technology Teaching Cases 2023年第1期13卷 67-76页

作者： Naithani, Charu Sood, Sanjay P Agrahari, Amit Information Technology & Systems Indian Institute of Management Lucknow India Health Informatics Division Centre for Development of Advanced Computing Mohali India

Healthcare infrastructure in the emerging economies is largely concentrated in their urban areas, and the burgeoning rural population is usually deprived of quality medical care. Telemedicine systems are expected to bridge this gap. This case study documents telemedicine’s significance and allows students to examine India’s national patient-to-doctor telemedicine service, eSanjeevaniOPD. The portal provides free and contactless consultation by a government doctor using video calls. COVID-19 hastened the adoption of eSanjeevaniOPD in the country, but its post-covid future would largely depend on feature innovation, architecture development, and digital strategies. With a total of three million teleconsultations in one year, eSanjeevaniOPD is one of the world’s largest digital healthcare delivery systems. This case provides insight into Indian health infrastructure, summarizes the journey of eSanjeevaniOPD, and raises questions on the digital transformation of the Indian healthcare delivery system. © Association for information technology Trust 2021.

关键词： COVID-19 pandemic Developing Economy Digital Health Digital Tranformation Healthcare Delivery System Indian Public Sector Teaching Case Telemedicine

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Adaptation of Transformer Model for Numeric Case 23

Adaptation of Transformer Model for Numeric Case

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Daniljuk, Mihhail Rikka, Sander Nõmm, Sven School of Information Technology Tallinn University of Technology Department of Software Science Tallinn Estonia School of Natural Sciences Tallinn University of Technology Department of Marine Systems Tallinn Estonia

ISBN: (纸本)9798350374889

The present paper discusses the adaptation of the transformer model for the case of numerical data. Although transformers are usually associated with large language models, there are many areas where the dependencies between the numeric input and the numeric output are too complex for statistical machine learning techniques. To apply a transformer for the numeric case, alternations of tokenizer and encoder-decoder mechanisms have been made. The proposed technique has been tested to estimate the wave specter of coastal seas on the basis of satellite-based synthetic aperture radar imagery and has demonstrated high accuracy. The mean correlation coefficient of 0.71 was achieved. © 2024 IEEE.

关键词： Radar imaging

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Bidirectional attentional mechanism for Arabic chatbot

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International Journal of information technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

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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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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of information technology (Singapore) 2024年 1-18页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Machine Learning-Driven Worker Activity Recognition with Wearable IoT and Smartphone Sensors 2nd

Machine Learning-Driven Worker Activity Recognition with Wea...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hafizh, Hadyan Yunus, Muhammad Razi Bin Majeed, Anwar P. P. Abdul Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Sarawak Metro Sarawak Kuching93100 Malaysia

ISBN: (纸本)9789819639489

Worker activity recognition is a crucial area of research with significant applications in manufacturing, healthcare, and security. Traditional methods relying on computer vision-based algorithms often demand extensive infrastructure, such as video cameras. This study presents a more efficient approach by utilizing data from embedded sensors in smartphones and microcontrollers. These wearable devices capture worker activities, including walking, sitting, standing, laying, and climbing stairs, through accelerometer sensors. The data is transmitted and monitored via an IoT platform, where machine learning techniques are employed for activity recognition and classification. The k-NN and Naïve Bayes classifiers achieved the highest accuracy rates, with 77.8% for smartphones and 76.1% for microcontrollers. This system provides a practical solution for recognizing and monitoring worker activities, with potential applications in factories for enhancing health, safety, and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wearable sensors

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Seeding based on complex factors-local fitness method 6

Seeding based on complex factors-local fitness method

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6th International Conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Hu, Jieling Yang, Yufei Dai, Xianhua School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Melbourne Australia

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

This paper studies the static network community detection algorithm, improves the overlapping community detection algorithm LFM based on local expansion, and improves the randomness and redundancy of this algorithm in the process of seed selection and community expansion. Vertex influence and their similarity are used to select seeds, and the redundant steps in community expansion are optimized, and the SCF-LFM algorithm is proposed. The experimental results show that the algorithm has improved accuracy and modularity, and the result of the division has a high degree of community. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Complex networks

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A Framework for Exploring Computational Models of Novelty in Unstructured Text 22

A Framework for Exploring Computational Models of Novelty in...

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6th International Conference on information System and Data Mining, ICISDM 2022

作者： Mohseni, Maryam Maher, Mary Lou Department of Software and Information Systems College of Computing University of North Carolina at Charlotte United States

ISBN: (纸本)9781450396257

Novelty modeling in unstructured text data is a research topic within the Natural Language Processing (NLP) Community. Effective novelty models can play a key role in providing relevant and interesting content to the users which is the central goal in many applications including education and recommender systems. This paper presents a framework for comparing different approaches and applications of computational models of novelty in unstructured text data. We focus on computational models that apply methods such as natural language processing and information theory. The framework provides an ontology for computational novelty with respect to the source of text data, methods for representing the data, and models for measuring novelty. We explore the value of the framework by applying it to research on computational novelty in news articles, research publications, books, and recipes. This framework is independent of the type of data in the items and can be used as a tool for researchers to study, compare, and extend existing computational novelty models and applications. © 2022 ACM.

关键词： Recommender systems

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