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7th International Conference on information technology, InCIT 2023

作者： Pratama, Bernardus Osan Regi Nugroho, Agus Cahyo Chandrawati, Tecla Brenda Soegijapranata Catholic University Faculty Computer Science Department Information System Semarang Indonesia

ISBN: (纸本)9798350358698

In late 2019, a new infection was identified in Wuhan, China. This disease has been designated by WHO as Corona Virus Disease (Covid-19). The Covid-19 has been spreading rapidly to Indonesia. Several types of variants, such as alpha, delta and Omicron, have been identified by WHO. An expert system that can recognise Covid-19 at an early stage should be developed to limit its spread as a result of the number of cases of patients and variants. In order to cope with future changes in knowledge, the author developed the 'SiskaCov' expert system for early detection of Covid-19 based on the symptoms of each variant using the forward chaining method and the ripple down rule as a method of knowledge acquisition. The expert system is built with the Expert system Development Life Cycle (ESDLC). The evaluation process is based on a series of steps but only the stages of knowledge acquisition, design and testing are carried out. Siska-Cov was built based on WEB using PHP Native and Javascript. This system was tested on 62 respondents, the test results shows that the performance value, the ease of use, and the usability value of the system are correlate with user behaviour regarding further use of this expert system. © 2023 IEEE.

关键词： Expert systems

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Perception-Centric Explainable AI: Bridging Cognitive Theories and HCI Design for Enhanced User Experience 16

Perception-Centric Explainable AI: Bridging Cognitive Theori...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alhasan, Sara Alnanih, Reem Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Artificial Intelligence (AI) systems increasingly influence high-stakes decision-making, necessitating explainability tailored to non-technical users. This research investigates integrating cognitive theories, Human-computer Interaction (HCI) principles, and Explainable AI (XAI) techniques to enhance user perception, a core cognitive function. A perception-focused framework was developed and tested using a medical symptom checker tool. The study involved 20 participants divided into control and experimental groups. The experimental group used an enhanced tool with features like visual hierarchy and consistency, while the control group used a basic version with minimal explainability. Participants completed pre-test, testing, and post-test phases, including surveys and objective tasks measuring recognition and recall. Results showed that 90% of the experimental group achieved "Good Performance" in recognition tasks compared to 20% in the control group. Subjective feedback indicated significantly higher un-derstandability, with an average rating of 4.8 out of 5 for the enhanced tool versus 3.1 for the control. These findings underscore the effectiveness of combining HCI principles and cognitive theories, such as Gestalt and Feature Integration, in reducing cognitive load and improving clarity. This multidisciplinary approach advances the design of intuitive, user-centered AI systems, fostering trust and accessibility. © 2025 Elsevier B.V.. All rights reserved.

关键词： User centered design

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New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China Communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective 7

Toward Digital Transformation Adoption: A Conceptual Framewo...

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7th information systems International Conference, ISICO 2023

作者： Marcel Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey. © 2023 The Authors. Published by Elsevier B.V.

关键词： Failure rate

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Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： Big data Hadoop Music information retrieval Optimization Spark

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Proposing A Modified Proof of Stake system to Counter 51% Attack in Blockchain 2

Proposing A Modified Proof of Stake System to Counter 51% At...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Khatoon, Naghma Rishu, Rishav Verma, Sushant Pranav, Prashant Usha Martin University Faculty of Computing and Information Technology Ranchi India Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350372847

Blockchain technology has revolutionized many sectors in the past few years. From its application in health care, digital currencies to building smart cities, many sectors have incorporated blockchain based mechanisms to produce better and efficient solutions to many of their long-lasting problems. The technology itself is fast and the reliance of some sectors on it has improved the overall performance of the applications. One of the most important and sought after applications of blockchain is in digital currencies. Cryptocurrencies are used in many different areas now a days and have become the go to investment destination for many individuals. Although blockchain is secured but it has many underlying security issues which need to be addressed to make this emerging technique more reliable. One of the issues with blockchain is double spending or 51% attack in which the miners authenticate suspicious transactions which result in heavy loss. In this paper, a new consensus mechanism called Modified Proof of Stake to defy 51% attack in blockchain is proposed. © 2024 IEEE.

关键词： Blockchain

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Effects of Quality Factor and Satisfaction on The Acceptance of Online Learning (Adoption Learning technology) 7

Effects of Quality Factor and Satisfaction on The Acceptance...

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7th International Conference on information technology, InCIT 2023

作者： Prasetya, Fx Hendra Widiantoro, Albertus Dwiyoga Harnadi, Bernardinus Soegijapranata Catholic University Department of Information System Faculty Computer Science Semarang Indonesia

ISBN: (纸本)9798350358698

In recent years, fundamental changes have occurred in the approach to education caused by advances in information and communication technology. Adoption of online learning technology is gaining increasing global attention for a number of reasons. This study aims to explore the complex dynamics between the quality attributes of online learning (system Quality and Service Quality), user Satisfaction (Satisfaction), and Continuance Intention to use (CI) and also pay attention to the factors of Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), Facilitating Condition (FC) and Self Efficacy (SE). The research methodology used in this study began with conducting a literature study on the correlation of the relationship between quality factors and user satisfaction in e-learning in acceptance of technology in e-learning, then a survey was carried out on e-learning users, especially students, then the data was processed statistically using PLS to find a correlation between quality factors and satisfaction in using e-learning. In this study conducted on 318 respondents. The results obtained in this study are that there is a correlation between SQ→Sat, SrQ→ Sat, SQ→EE, EE→PE, Sat →CI, PE→ CI, FC→ EE, SE→EE, FC→PE, SI→PE according to the hypothesis that was compiled based on a study of the literature. © 2023 IEEE.

关键词： E-learning

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Prediction of Student Performance by Using Machine Learning Techniques 4

Prediction of Student Performance by Using Machine Learning ...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ali, Donia Ashraf Aborizka, Mohamed Dahroug, Ahmed College of Computing and Information Technology Cairo Egypt Arab Academy for Science Technology and Maritime Transport Department Information System Egypt Arab Academy for Science Technology and Maritime Transport Department Computer Science Egypt

ISBN: (纸本)9798350348248

Each person's learning process is distinct and diversified. Therefore, using the right teaching techniques is crucial to improving student performance and academic achievement. Numerous educational institutions, including schools and universities, make an effort to anticipate their students' academic performance in order to improve the effectiveness of the educational systems. In educational technology and the recent learning systems, discovering useful hidden patterns in learner data is significantly valuable. In this paper, several machine learning classifiers were applied such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB), to predict the student's performance. Furthermore, a hybrid model was used to reduce data dimensionality and improve the overall performance of the classifiers used. A real dataset obtained from the UCI machine learning repository is used to evaluate the proposed hybrid model. The reported results show the performance of CNN-RNN outperforms other methods with an accuracy equal to 89.1%. © 2023 IEEE.

关键词： Recurrent neural networks

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Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease

Identification of Key Signaling Pathways and Novel Computati...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Morshed, Monir Uddin, Muhammad Shahin Daffodil International University Department of Computing and Information System Dhaka1216 Bangladesh Department of Computer Science and Engineering Dhaka1230 Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798331529765

Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions. © 2024 IEEE.

关键词： Blood vessels

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Machine Learning Empowered Electricity Consumption Prediction

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computers, Materials & Continua 2022年第7期72卷 1427-1444页

作者： Maissa A.Al Metrik Dhiaa A.Musleh Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

Electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy *** to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment *** electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and *** paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi ***,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi *** dataset used in this research is gathered exclusively from the Saudi Electric *** pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine *** number of neurons and learning rate of the standalone model were optimized using hyperparameter ***,the standalone model was tested against the bagging ensemble using the optimized *** bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN *** results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

关键词： Electricity consumption prediction artificial neural network machine learning

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