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10th International Conference on Information and Communication Technology, ICoICT 2022

作者： Cai, Shuxian Yau, Wei Chuen Ong, Thian Song Teng, Jackson Horlick School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia Multimedia University Faculty of Information Science and Technology Melaka Malaysia

ISBN: (数字)9781665481656

ISBN: (纸本)9781665481656

Unlike single biometric systems, multi-instance biometric systems can reduce the risk of user privacy leakage. Meanwhile, maintaining biometric security also requires template protection. However, there is still limited study focusing on template protection in multi-instance biometrics. To address such issues, this paper proposes a method for multi-instance finger vein template protection. In this work, the vein features of different finger instances are extracted at feature fusion and image fusion levels using Gabor filter method, respectively. For feature transformation, the Gabor feature blocks are transformed with Bloom filters to extract the finger vein template for secure storage and verification. When analyzing the performance, the experiments are mainly conducted with the constant key scenario to be compared with performance of the baseline Gabor feature extraction method and the genuine key scenario. It is shown that this proposed method effectively improves system performance, with equal error rate (EER) as low as 5.00% in constant key scenario, while maintaining zero error rate in genuine key scenario. Besides, the proposed method is also examined for the properties of unlinkability and irreversibility as required for a template protection method. © 2022 IEEE.

关键词： Gabor filters

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Effect of copper nanoparticle volume fraction on flow in a 3D lid-driven cavity with phase change materials using molecular dynamics simulation

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Hassan, Waqed H. Thiab, Rifqa F. Al-zahy, Younis Mohamed Atiah Salahshour, Soheil Hekmatifar, M. Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia University of Warith Al-Anbiyaa Kerbala 56001 Iraq Department of Civil Engineering College of Engineering University of Kerbala Kerbala 56001 Iraq College of Health and Medical Techniques Al-Zahraa University for Women Karbala 56100 Iraq Department of Physics College of Education Misan University Maysan Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Background: Phase Change Materials are substances characterized by specific properties, including defined melting points and substantial latent heat of fusion. Effective heat transfer management is vital in modern industries, as it supports essential processes across various sectors. Methods: This study investigates the effect of copper nanoparticle volume fraction on flow behavior and thermal dynamics in a 3D lid-driven cavity. This enclosure was a controlled environment filled with phase change materials, designed to optimize thermal energy management. The system configuration comprised a wavy bottom wall and an adaptable upper wall, allowing for dynamic adjustments during the simulation. The results show that as the volume ratio of copper nanoparticles increased from 1 % to 3 %, the steady heat transfer process in the simulated nanoparticles also increased. Increasing the volume ratio from 1 % to 3 % resulted in a decrease in the maximum density of nanoparticles, which decreased from 0.0152 to 0.0146 atom/Å³. Additionally, this increase led to a rise in thermal conductivity from 1.26 to 1.45 W/m·K and in heat flux from 8.26 to 9.95 W/m². Significant Findings: The study demonstrates that optimizing the volume fraction of Cu-NPs in PCMs can significantly enhance thermal conductivity and heat flux, offering potential improvements in thermal energy storage systems. © 2025 The Authors

关键词： Density Heat flux Molecular dynamics simulation Phase change material Thermal conductivity

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Sequences analysis and phylogenetic of leukemia DNA 2

Sequences analysis and phylogenetic of leukemia DNA

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2nd International Seminar on science and Technology 2020, ISST-2 2020

作者： Lusiyanti, D. Nacong, N. Irawan, M.I. Mathematics Study Program Science Faculty Tadulako University Indonesia Dapertment of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

DNA is one part of a biological sequence other than RNA and protein. DNA is a nucleic acid that stores all the unique biological information of every living thing and several viruses. Someone who has an illness can be seen in the structure of the DNA which can then be compared with other DNA structures. This process is part of sequence analysis which is the core of bioinformatics. This study aims to analyze the results of sequences alignment of cancer. The sequence was obtained from DNA leukemia taken from GenBank (***). In the sequence analysis, the most basic thing is the sequence alignment. Alignment results can provide information about the level of similarity in the structure of the two sequences. This study will apply the Super Pairwise Alignment method that is implemented in JAVA-based programming languages. Alignment results are carried out by giving different values of gap penalty and gap extend. The results of the study obtained different alignment values, this proves that there is a significant effect of the selection of core parameters used in alignment. © Published under licence by IOP Publishing Ltd.

关键词： DNA

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Comparison of document similarity algorithms in extracting document keywords from an academic paper 7

Comparison of document similarity algorithms in extracting d...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Miah, M. Saef Ullah Sulaiman, Junaida Azad, Saiful Zamli, Kamal Z. Jose, Rajan Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Malaysia Pahang Center for Data Science and Artificial Intelligence Data Science Center Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Green University of Bangladesh Department of Cse Dhaka Bangladesh Universiti Malaysia Pahang Faculty of Industrial Sciences and Technology Gambang Malaysia

ISBN: (纸本)9781665414074

The idea of this study is to validate a list of keywords derived from a scientific article by a domain expert from years of knowledge with prominent document similarity algorithms. For this study, a list of handcrafted keywords generated by Electric Double Layer Capacitor (EDLC) experts are chosen, and relevant documents to EDLC are considered for the comparison. Then, different similarity calculation algorithms were employed in different settings on the documents such as using the whole texts of the documents, selecting the positive sentences of the documents, and generating similarity score with automatically extracted keywords from the documents. The experiment's outcome provides us with findings that the machine-generated keywords are mostly similar to the curated list by the domain experts. This study also suggests the preferable algorithms for similarity calculation and automated key-phrase extraction for the EDLC domain. © 2021 IEEE.

关键词： Supercapacitor

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Facilities Utilization Framework for Universal Design in Tourism 13

Facilities Utilization Framework for Universal Design in Tou...

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13th IIAI International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2022

作者： Tongtep, Nattapong Charoenporn, Thatsanee College of Computing Prince of Songkla University Phuket Thailand Musashino University AAII Faculty of Data Science Tokyo Japan

ISBN: (纸本)9798350309928

A suitable environment for people with disabilities has widely campaigned around the world. There are many attempts to mobilize the community to help people with disabilities can enjoy their lives equally. This paper, therefore, presents our initiative on adapting the universal design concept to develop a facilities utilization framework for tourist spots. The system provides information about places and various necessary details of the facilities and services in accordance with the unique needs of people with disabilities in the form of web applications. The paper presents the concept of development, related theories, and the result of applying the information system in practice. The preliminary usability evaluation results are at an outstanding level. Collecting more data on facilities and usage patterns are ongoing task with a focus on monitoring and suggesting the coverage and access rate of all groups of people in the public area. © 2022 IEEE.

关键词： Tourism

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Modeling of toddler stunting in the province of east nusa tenggara using multivariate adaptive regression splines (mars) method 5

Modeling of toddler stunting in the province of east nusa te...

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5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Azizah, D.M. Permatasari, E.O. Statistics Department Faculty of Mathematics Computing and Data Science InstitutTeknologiSepuluhNopember GebangKidul Road number 66 Surabaya60111 Indonesia

Stunting is a condition of failure to thrive in children under five (babies under five years old) as a result of chronic malnutrition so that the child is too short for his age. Malnutrition occurs since the baby is in the womb and in the early period after the baby is born. However, the condition of stunting only appears after the baby is 2 years old. Based on Basic Health Research in 2017 the province with the highest prevalence of short and very short toddlers in Indonesia is East Nusa Tenggara which is equal to 40.3 percent. This shows that the prevalence of stunting toddlers in NTT Province is still far from the overall prevalence of stunting toddlers in Indonesia. Cases of toddler stunting can be analyzed using the Multivariate Adaptive Regression Splines (MARS) method because the data used are high-dimensional data and do not show clear patterns of relationships between response variables and predictor variables. The data used is the percentage of stunting toddlers in NTT in 2017 with the district / city research unit. Based on the analysis, it was found that the district in NTT with the highest percentage of stunting children was South East Timor and the district with the lowest percentage was East Manggarai. The best MARS model is formed from 3 basis functions with 2 predictor variables included in the model, namely the percentage of pregnant women at risk of chronic lack of energy (X1) and the percentage of infants with low birth weight (X6). And after the classification results obtained the accuracy of the classification of 77.27% with an error of 22.72%. © Published under licence by IOP Publishing Ltd.

关键词： Clustering algorithms

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Thermal and mechanical attributes and swelling percentage of hydrogels by changing in magnetic field frequency using computer simulation

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International Communications in Heat and Mass Transfer 2025年 165卷

作者： Wang, Haoyu Basem, Ali Alhamdi, Sabah F.H. Singh, Narinderjit Singh Sawaran Al-Bahrani, Mohammed Abdullaeva, Barno Salahshour, Soheil Esmaeili, Sh. Medical College Xijing University Shaanxi Xi'an710123 China Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Mechanical Engineering University of Misan Misan Amarah62001 Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai71800 Malaysia Chemical Engineering and Petroleum Industries Department Al-Mustaqbal University Babylon51001 Iraq Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Turkey Fast Computing Center Shabihsazan Ati Pars Tehran Iran

The thermodynamic, mechanical, and expansion properties of synthetic hydrogels derived from polyacrylamide (PAM) are investigated in this study to the impact of magnetic field frequency (MFF) as an external stimulus. The impact of various MFFs on essential parameters, such as swelling percentage (SP), ultimate strength (US), Young's modulus (YM), heat flux (HF), and thermal conductivity (TC), is assessed using Molecular Dynamics (MD) simulation with LAMMPS software, ranging from 0.01 to 0.05 1/fs. It is important to note that our results indicate that the structural volume decreased from 356,985 to 349,982 Å at 0.05 1/fs as the MFF increased. The alignment of polymer chains in the hydrogel was improved by increasing the MFF, resulting in a more compact structure. Through this compaction, the total structural volume diminished as the chains were drawn closer together, thereby reducing the spaces among them. US experienced a decrease from 0.0325 to 0.0331 MPa, while YM converged to 0.0008 MPa. The alignment and packaging of polymer chains improved, resulting in an increase in the US of hydrogels as the MFF increased. This enhanced alignment resulted in a material that can withstand a larger amount of stress before failing, as a result of the stronger intermolecular interactions. Additionally, the temperature coefficient (TC) increased to 0.56 W/m·K as the MFFs increased. An increase in molecular alignment and a decrease in free volume within the hydrogel can be attributed to the higher MFF. This enhanced alignment enabled the molecules to transfer heat more efficiently, resulting in improved TC and increased HF. These findings illustrate the substantial influence of MFF on hydrogel properties, offering valuable insights for the development of drug delivery systems and responsive materials. © 2025 Elsevier Ltd

关键词： Positive temperature coefficient

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Next-Generation Human Activity Recognition Using Locality Constrained Linear Coding Combined With Machine Learning (NG-HAR-LCML)

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Healthcare Technology Letters 2025年第1期12卷

作者： Maryam Shabbir Fahad Ahmad Saad Awadh Alanazi Muhammad Hassan Khan Jianqiang Li Tariq Mahmood Shahid Naseem Muhammad Anwar Department of Computer Sciences Bahria University Lahore Punjab Pakistan Department of Computer Science Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan School of Computing Faculty of Technology University of Portsmouth Portsmouth UK Portsmouth Artificial Intelligence and Data Science Centre (PAIDS) University of Portsmouth Portsmouth UK Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Aljouf Saudi Arabia The School of Software Engineering Beijing University of Technology Beijing China Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Department of Information Sciences University of Education Vehari Pakistan Faculty of Information Sciences Division Science &Technology University of Education Lahore Pakistan

Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using Locality-Constrained Linear Coding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based act

关键词： feature encoding human activity recognition locality constrained linear coding support vector machine wearable sensors

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Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations 14

Explaining Decision-Tree Predictions by Addressing Potential...

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14th International Conference on Natural Language Generation, INLG 2021

作者： Maruf, Sameen Zukerman, Ingrid Reiter, Ehud Haffari, Gholamreza Dept. of Data Science and AI Faculty of IT Monash University VIC Australia Dept. of Computing Science University of Aberdeen United Kingdom

ISBN: (纸本)9781954085510

We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. Our human evaluation focused on the effect of explanations on users' understanding of a DT's reasoning and their willingness to act on its predictions. The results show that (1) explanations that address potential conflicts are considered at least as good as baseline explanations that just follow a DT path;and (2) the conflict-based explanations are deemed especially valuable when users' expectations disagree with the DT's predictions. © 2021 Association for Computational Linguistics

关键词： Forecasting

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Computer vision and Generative AI for yield prediction in Digital Agriculture 2

Computer vision and Generative AI for yield prediction in Di...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Majumder, Sayan Khandelwal, Yash Sornalakshmi, K. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer numerous economic, environmental and social benefits. Controlled Environment Agriculture (CEA) is providing more benefits since the weather and other environmental conditions is controlled and predictable. In CEA, the benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Generative AI to visually forecast the potential yield of a crop using stable diffusion and conditional prompts additionally implementing several Generative Adversarial Networks (GAN) architectures for data generation, augmentation and super-resolution. Our proposed system also adopts generative AI for real time monitoring of plants. studying their respective conditions and their autonomous cultivation and harvesting patterns. The dataset used contained over 8479 examples for different fruits alongside annotations. This data was used to train the detection models as well categorically feed the GANs for data generation. data generated by DCGAN has a structural similarity of 0.5-0.7 whereas data generated by pix2pix network returns us a structural similarity of 0.7-0.9. © 2024 IEEE.

关键词： Cultivation

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