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2023 International Conference on Informatics, Multimedia, Cyber and information systems, ICIMCIS 2023

作者： Hartawan, Muhammad Syarif Maharani, Maya Dewi Dyah Krisnanik, Erly Rahman, Aedah Abd Krinadwipayana University Faculty of Engineering Jakarta Indonesia Sahid University Faculty of Environmental Engineering Jakarta Indonesia Universitas Pembangunan Nasional Veteran Jakarta Computer Science Faculty Departement Information System Jakarta Indonesia Asia e University Departement Information and Communication Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

The use of information technology plays an important and necessary role in the development of ecotourism institutional. Likewise, if the development and progress of information technology can be applied to the small and medium businesses related to the ecotourism industry then, the selling value of the ecotourism industry will develop and increase. This research was conducted to determine the level of readiness of the ecotourism industry in carrying out the sustainable implementation of information technology in managing the ecotourism industry. We developed a research model by combining and adapting the technology readiness model and the successful information technology development model for the development of the ecotourism industry in six ecotourism units in Indonesia. This quantitative study involved 300 SME workers and managers. Data was processed and analyzed using the Multi Dimensional Scaling method, with descriptive data entered into a spreadsheet application. According to the analysis performed for this study, the sustainability index value was 53.11%, which is in the range of (50-74.99) percent or is fairly sustainable. The leverage attribute refers to the use of social media to promote wild products in the ecotourism sector. Wild products are goods or services that are produced without causing environmental harm. © 2023 IEEE.

关键词： Human resource management

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

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4th International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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Utilization of Augmented Reality technology in the Campus Environment 4th

Utilization of Augmented Reality Technology in the Campus En...

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4th International Conference on Inventive Computation and information Technologies, ICICIT 2022

作者： Gaol, Ford Lumban Kamil, Mufti Ikhsan Iswardhana, Aria Muhammad Firnandes, Steven Gusda Fahrezi, Fazri Matsuo, Tokuro Hutagalung, Fonny Binus Graduate Program—Doctor of Computer Science Jakarta Indonesia Alam Sutera School of Information System Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789811974014

Technological advancements are accelerating in the modern day;one such advancement is augmented reality (AR) technology. Naturally, these advancements have an effect on various sectors of life, one of which is education. The education industry will be the most impacted if campuses implement and deploy augmented reality technology. One of these is through the use of augmented reality technology as an application-based learning medium for students in order to motivate them to learn and make it easier for them to improve their academic performance on campus. The issue at hand is the absence of utilization of augmentation reality technology as a learning medium on campus, which, of course, does not capitalize on technical advancements to improve student interest in learning. This study employs a research and development model. The method is divided into four phases: (1) planning;(2) data collecting;(3) analysis, discussion, and development;and (4) documentation. The findings of this study reveal that students enthusiastically support the usage of augmented reality technology as a learning medium in order to boost accomplishment and introduce new experiences through its use. However, it is required to have a system capable of serving as a technological medium, so that the construction of a system capable of serving as a medium for augmented reality may be easily accessed by students, who hope that this system would enable them to better their academic performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Locality-Aware Spatial-Temporal Attention Neural Network with Contrast Learning for Traffic Flow Prediction 3

Locality-Aware Spatial-Temporal Attention Neural Network wit...

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3rd International Conference on Internet of Things and Smart City, IoTSC 2023

作者： Shi, Zheng Wang, Jingping Zhang, Yingjun Yin, Hui Huang, Hua Zhong, Yanglong School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Information Technology Center Beijing Jiaotong University Beijing100044 China School of Civil Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510666375

Traffic flow prediction is a crucial component of establishing the intelligent transportation system. Accurate and real-time traffic flow prediction is of great significance for urban traffic management. With the recent development of artificial intelligence, deep learning-based methods have been effective tools for traffic flow prediction. However, locality-unawareness and data scarcity are still two open issues to be considered in traffic flow prediction tasks. To address these problems, we propose a novel locality-aware spatial-temporal attention neural network, named LASTANN, in this paper. Specifically, we propose two elaborate attention modules to perceive local information in spatial and temporal dimensions. We also propose an auxiliary module based on contrast learning to strengthen the representation ability of model. We verify the effectiveness of the proposed model on two real-world traffic flow datasets. The experimental results demonstrate that the proposed LASTANN outperforms other baselines consistently and each component enhances the prediction performances significantly. © 2023 SPIE.

关键词： Deep learning

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Enhancing Signature Authenticity Recognition Using CNN, PCA, and DAG: A Study on Accuracy Improvement and Parameter Refinement

Enhancing Signature Authenticity Recognition Using CNN, PCA,...

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International Conference on Informatics and Computing (ICIC)

作者： Irma Yunita Nasution Muhammad Zarlis Maya Silvi Lydia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Departement of Information System Management BINUS Postgraduate Universitas Bina Nusantara Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study focuses on enhancing signature authenticity recognition by leveraging Convolutional Neural Networks (CNN) combined with Principal Component Analysis (PCA) and Directed Acyclic Graphs (DAG). While CNNs have demonstrated promising results, discrepancies between training and test accuracies remain a challenge. By integrating PCA for dimensionality reduction and DAG for optimizing training, this research aims to improve efficiency and accuracy. Despite achieving $\mathbf{1 0 0 \%}$ training accuracy, the test accuracy reached only $\mathbf{9 7 \%}$, indicating the necessity for refining parameters further. This study emphasizes the value of systematic methodologies and comprehensive parameter exploration to achieve substantial advancements in signature recognition accuracy.

关键词： Training Dimensionality reduction Directed acyclic graph Accuracy systematics Fluctuations Refining Convolutional neural networks Informatics Principal component analysis

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A Deep Learning Approach to Causal Inference in Human Genomics using Counterfactual Reasoning

TechRxiv

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TechRxiv 2023年

作者： Gobonamang, Tshepo Kitso Mpoeleng, Dimane Computer Science and Information System Botswana Accountancy College Botswana International University of Science and Technology Botswana

In this paper, we delve into the intricate realm of human genomics, presenting a novel design that leverages deep learning and counterfactual reasoning for causal inference. We postulate that mutations occurring within DNA sequences have the potential to instigate diseases by interrupting essential biological processes, a hypothesis that fundamentally drives this research. To test this, we have undertaken a meticulous extraction of key attributes from a range of databases hosted by the National Center for Biotechnology information (NCBI). These attributes are subsequently processed using one-hot encoding, a technique that effectively transforms categorical variables into a form that could be provided to machine learning algorithms. A sophisticated deep learning model is then utilized to ascertain the accuracy of the hypothesis. The output, depicted as a causal graph, elucidates the relationships and interactions between the variables in question, providing a graphical representation of the proposed hypothesis. Our research suggests that strategic modifications to the DNA sequence or alterations to the set of mutations in the DNA could induce significant changes in the biological processes. This, in turn, can lead to alterations in the structure and function of proteins, a cornerstone of cellular operations. We also underline the importance of counterfactual statements in formulating hypotheses and driving intelligent behavior. Despite their untestable nature and inherent subjectivity, these counterfactuals serve as powerful tools for comprehending and predicting outcomes. The implications of this design extend beyond academic interest. It provides a pathway for a deeper understanding of human genomics and holds promise for the development of targeted therapies for genetic diseases. It fosters the possibility of personalized medicine and therapeutic strategies that can alter the course of the disease at a genetic level, potentially revolutionizing healthcare. © 2023,

关键词： DNA sequences

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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Integrating IoT, Blockchain, and Vision Transformers for Enhanced Security and Efficiency in Smart Home and Healthcare systems

Integrating IoT, Blockchain, and Vision Transformers for Enh...

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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Salman Jan Toqeer Ali Syed Saad Said Alqahtany Sohail Iqbal Ijaz Khan Shahrulniza Musa Faculty of Computer Studies Arab Open University A'Ali Kingdom of Bahrain Faculty of Computer and Information System Islamic University Madinah Madinah Saudi Arabia Department of Information Technology Alburaimi University College Alburaimi Oman Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Internet of Things (IoT) and cloud computing have revolutionized various realms including healthcare, education, industries and businesses. Wearable health monitors, smart medical equipment continuously collect patients data that are sent to cloud computing that possess powerful analytical tools facilitating individuals and healthcare units in terms of personalized treatment, proactive real-time health management, remote patient monitoring. For an efficient processing, edge computing satiated in terms of analysis however this decentralized nature, edge computing is significantly targeted and exposed to security threats and cyber attacks due to inhibition of vulnerabilities and breaches in the IoT devices. This research study focuses on enhancing strengths of edge computing to insure the reliability and integrity of data by utilizing the Blockchain infrastructure. This research also utilizes the transformer models to identify the real and fake data that fosters trust in edge computing technologies.

关键词： computer vision Computational modeling Data security Medical services Smart homes Transformers Threat assessment Blockchains Internet of Things Edge computing

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A Development of 5G Wireless Sensor Network using Man Design 4

A Development of 5G Wireless Sensor Network using Man Design

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

作者： Vennila, C. Almusawi, Muntather Kumar, K. Sriram Priya, B. Rawat, Vidhi Amirthayogam, G. Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h TN India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Karpagam Institute of Technology Department of Information Technology TN India Nehru Institute of Engineering and Technology Department of Computer Science and Business System Tamil Nadu T.M.Palayam India Ies College of Technology Department of Electrical & Electronics Engg Madhya Pradesh Bhopal India Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

This article investigates the key drivers influencing the design of the forthcoming Metropolitan Area Network (MAN), focusing on the integration of two pivotal elements: Cloud Radio Access Network (C-RAN) for 5G implementation and the deployment of edge computing. As we delve into these aspects, the article systematically uncovers the crucial architectural components essential for effectively addressing the challenges inherent in implementing C-RAN and edge computing. Through this strategic approach, the objective is to ensure a cost-effective solution that aligns with the specific requirements of these applications. The article aims to provide insights into the symbiotic relationship between CRAN and 5G, emphasizing their collaborative role in enhancing network efficiency. Simultaneously, it underscores the significance of edge computing, highlighting its potential to optimize data processing and reduce latency within the evolving landscape of metropolitan networks. This comprehensive exploration contributes to a nuanced understanding of the future MAN design, grounded in innovative solutions and considerations for seamless integration. © 2024 IEEE.

关键词： Radio access networks

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A Multi Target Drug Design Method Based on Target Protein Sequence and Feature Similarity

A Multi Target Drug Design Method Based on Target Protein Se...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haoran Liu Xiaoli Lin Jing Hu Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This paper describes a multi target drug design method based on features of the target proteins. The multi target drug which inhibit multiple proteins have prospective applications, but design is difficult. In this study, the target protein sequences are embedded to obtain target features. The features of each target are independently encoded as target latent vectors, and the features of multiple targets are jointly encoded as target similarity latent vectors. Based on the features of targets and the similarity features among the targets, multi target drugs are efficiently designed. In the experiments, the designed multi target drugs can be docked with target proteins with a proprietary molecular structure according to the requirements of different target pocket structures. The excellent fitness among the molecular structure of multi target drugs and the protein structure of multiple targets confirms the performance of the proposed method. In the molecular docking part of the experi¬ments, the binding affinity of designed multi target drugs is far better than that in the previous studies, which administrates the better performance of this work.

关键词： Drugs Design methodology Vectors Protein sequence Encoding Bioinformatics

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