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10th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2022

作者： Perera, U.D.Harshi Lahiruka Harshanath, S.M.B. Sri Lanka Institute of Information Technology Faculty of Graduate Studies Research Sri Lanka Sliit Department of Computer Science and Software Engineering Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

While social media has reached the peak of its popularity, the addiction to social media become a major issue. With the present pandemic the authorities are forced to close the schools and to shift to virtual classrooms. The students must therefore use a connected devise to participate to the classes which has contributed to the increased addiction to social media among the youth. Inappropriate or excessive usage social media may affect the studies as well as mental and physical health of the user. Spread of fake news through social media has also been a big problem. Mainly, due to its convenience of usage, social media, has become a daily necessity, especially among the youth. So, the primary goal of this study project is to determine the impact of social media on school students' academic performance and to suggest a separate social media platform which will meet their needs. © 2022 IEEE.

关键词： Students

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Trans-Mamba: The Cross-network of Transformer and Mamba for Traffic Flow Prediction 10

Trans-Mamba: The Cross-network of Transformer and Mamba for ...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Qian Jia, Haonan Ye, Junchen Feng, Jinyan Lan, Fayang Du, Bowen Huang, Runhe Beihang University Ccse Lab Beijing China Beihang University School of Transportation Science and Engineering Beijing China Guizhou Qiantong Zhilian Technology Co. Ltd Guizhou China Beihang University and Zhongguancun Laboratory Transportation Science and Engineering Beijing China Hosei University Faculty of Computer and Information Sciences Department of Computer Science Metropolis Tokyo Japan

ISBN: (纸本)9798331520861

Traffic flow prediction is a crucial aspect of intelligent transportation systems. It holds significant importance in optimizing traffic management and enhancing road safety. However, the task of traffic flow forecasting remains a significant challenge due to its inherent complexity, dynamic nature, and the presence of long-range dependencies. Existing graph neural network methods exhibit two primary limitations. Firstly, when static graphs are employed to capture spatial dependencies, they fail to account for the dynamics in these dependencies. Secondly, these methods struggle to effectively capture long-range spatial dependencies. Furthermore, attention-based methods, while potentially addressing some of these issues, require substantial computational and memory resources, leading to inefficiencies. We design a cross-network model that combines Transformer and Mamba architectures for traffic flow prediction, which efficiently captures dynamic long-range spatio-temporal dependencies. The model architecture is composed of three main components: a learnable spatio-temporal embedding layer that adaptively captures the unique features of different nodes and time;stacked spatio-temporal blocks, which include two temporal blocks and one spatial block arranged between them to effectively capture spatio-temporal dependencies;and a fully connected output layer that generates the final predictions. Experiments on three public real-world datasets demonstrate the effectiveness of TransMamba. Specifically, the model improves the mean absolute error (MAE) and root mean square error (RMSE) by 2.59% and 4.14%, respectively, on the PeMS08 dataset. Additionally, ablation studies confirm the effectiveness and efficiency of each component of the model. © 2024 IEEE.

关键词： Graph neural networks

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route

Optimation Cavacity Vehicle Routing Problem with K-Nearest N...

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International Conference of computer science and information technology

作者： Muhammad Syahputra Novelan Syahril Efendi Poltak Sihombing Herman Mawengkang Doctoral Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of ‘nodes’ on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods.

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New Architecture Quantum Perceptron using Quantum Circuit

New Architecture Quantum Perceptron using Quantum Circuit

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2021 International Conference on Data science, Artificial Intelligence, and Business Analytics, DATABIA 2021

作者： Solikhun, Solikhun Efendi, Syahril Zarlis, Muhammad Sihombing, Poltak Universitas Sumatera Utara Doctoral Program Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9781665426800

the learning algorithm that is not optimal is due to the incomplete use of quantum in the perceptron quantum learning algorithm which is the background of this research. Previous research has shown that the proposed architectural form still needs to increase its probability value to be more optimal. The previous probability was 90.7%. The proposed model is a quantum circuit architecture in a quantum perceptron algorithm consisting of a quantum bit gate. The researchers conducted training and testing of the proposed quantum circuit architecture using quantum computer of the IBM Quantum Experience. This research produces a perceptron quantum architecture model using a quantum circuit that can solve problems regarding data classification. After measuring the proposed quantum circuit architecture, it shows a 100% probability. After training and testing, the probability is 100%, the same as that at the time of measurement. Then it produces the same output of |111100000000000000〉. The resulting probability shows a significant increase from the previous 90, 7%. © 2021 IEEE.

关键词： Probability

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Human-Centric Machine Learning: The Role of Users in the Development of IoT Device Identification and Vulnerability Assessment 5th

Human-Centric Machine Learning: The Role of Users in the Dev...

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5th International Conference on HCI for Cybersecurity, Privacy and Trust, HCI-CPT 2023, held as part of the 25th International Conference on Human-computer Interaction, HCII 2023

作者： Danso, Priscilla Kyei Molyneaux, Heather Zohourian, Alireza Neto, Euclides Carlos Pinto Whalen, Derrick Dadkhah, Sajjad Ghorbani, Ali A. Faculty of Computer Science University of New Brunswick FrederictonNB Canada FrederictonNB Canada Information Technology Services Port of Halifax HalifaxNS Canada

ISBN: (纸本)9783031358210

Big data, Artificial Intelligence (AI), and Machine Learning (ML) have recently been posited as both a challenge and an opportunity for Human-computer Interaction (HCI) research. Researchers and practitioners have also expressed concern about these systems’ potential for favouritism, lack of transparency, and impartiality. We focus on the real-world utilization of various IoT devices and systems, communications technologies, and privacy and security considerations specific to the industry. We found that while the survey responses did validate some of our initial assumptions about privacy and security needs at Canadian ports, responses to the survey questions on IoT device and system usage and privacy and security needs were diverse, indicating an initial requirement for flexibility in UX design. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Effect of Force Adjustment Control in Cooperative Work between Remote Robot Systems with Force Feedback

Effect of Force Adjustment Control in Cooperative Work betwe...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Ohnishi, Hitoshi Komori, Tatsuki Ishibashi, Yutaka Huang, Pingguo The Open University of Japan Faculty of Liberal Arts Chiba Japan Nagoya Institute of Technology Departmetn of Computer Science Nagoya Japan Nagoya Institute of Technology Graduate School of Engineering Nagoya Japan Gifu Shotoku Gakuen University Faculty of Economics and Information Gifu Japan

ISBN: (纸本)9798350324174

In this paper, we examined the effect of force adjustment control on cooperative tasks performed by two remote robot systems with force feedback. Two force adjustment controls were introduced: manual tuning and control acquired through reinforcement learning. The experiment involved an operator who carried an object between the robot arms from both sides. The results showed that both of the two force adjustment controls improved control performance. © 2023 IEEE.

关键词： Feedback

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Research on ECG Classification Method Based on Convolutional Neural Network 6th

Research on ECG Classification Method Based on Convolutional...

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6th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2021

作者： Tao, Jin Shi, Jianting Wu, Rongqiang Graduate College Heilongjiang University of Science and Technology Harbin150022 China School of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin150022 China

ISBN: (纸本)9783031044083

The electrocardiogram reflects the temporal changes in the body’s cardiac potential;This is also an important technology for diagnosing cardiovascular disease, so the classification of electrocardiogram has gradually become the focus of many scholars. This paper designs an ECG classification algorithm based on convolutional neural network, which aims to automatically classify ECG using artificial intelligence algorithm. The algorithm has the characteristics of less parameters and more layers, and the classification speed is faster and has very strong real-time performance. The experimental results show that the accuracy of the algorithm reaches 98.1%, which has strong advantages in medical diagnosis and application. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Electrocardiography

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Identification of Beef Freshness Using Linear Discriminant Analysis (LDA) Algorithm and Extraction of HSV Values 2

Identification of Beef Freshness Using Linear Discriminant A...

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2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022

作者： Napianto, Riduwan Tri Prastowo, Agung Deni Wahyudi, Agung Wajiran Yudoutomo, Yohanes Egi Pratama Suparto, Arico Ayani Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Information Technology Education Study Program Stkip Pgri Situbondo Situbondo Indonesia

ISBN: (纸本)9781665465410

To determine the quality of beef, it is usually seen using a flashlight and matching it with a predetermined color standard. However, this method is time consuming and the results are inconsistent due to human visual limitations. This study aims to identify the freshness of beef based on its color characteristics using feature extraction method with HSV and LDA method for identification. The identification process begins with the transformation process from RGB images to L∗a∗b, then image segmentation serves to separate one object from another and image identification uses the LDA method with feature extraction using HSV. The use of color feature extraction with the HSV algorithm to assist in obtaining information from the colors contained in the image for easy identification. Furthermore, the LDA algorithm has a function to obtain the maximum projection to obtain a smaller dimensional space so that the desired patterns can be separated through the boundary line of a linear equation so as to form a certain class or group. Based on the test results using the confusion matrix, it produces a precision value of 80%, recall of 84%, and accuracy of 82%. © 2022 IEEE.

关键词： Beef

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