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

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

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Parameterized Max Min Feedback Vertex Set 48

Parameterized Max Min Feedback Vertex Set

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48th International Symposium on Mathematical Foundations of computer science, MFCS 2023

作者： Lampis, Michael Melissinos, Nikolaos Vasilakis, Manolis Université Paris-Dauphine PSL University CNRS LAMSADE Paris75016 France Department of Theoretical Computer Science Faculty of Information Technology Czech Technical University in Prague Czech Republic

ISBN: (纸本)9783959772921

Given a graph G and an integer k, Max Min FVS asks whether there exists a minimal set of vertices of size at least k whose deletion destroys all cycles. We present several results that improve upon the state of the art of the parameterized complexity of this problem with respect to both structural and natural parameters. Using standard DP techniques, we first present an algorithm of time twO(tw)nO(1), significantly generalizing a recent algorithm of Gaikwad et al. of time vcO(vc)nO(1), where tw, vc denote the input graph’s treewidth and vertex cover respectively. Subsequently, we show that both of these algorithms are essentially optimal, since a vco(vc)nO(1) algorithm would refute the ETH. With respect to the natural parameter k, the aforementioned recent work by Gaikwad et al. claimed an FPT branching algorithm with complexity 10knO(1). We point out that this algorithm is incorrect and present a branching algorithm of complexity 9.34knO(1) © Michael Lampis, Nikolaos Melissinos, and Manolis Vasilakis

关键词： Parameterization

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Comparative Analysis and Evaluation of Well-being Activity-Infused Fine-Tuned Language Models with Benchmark Models

Comparative Analysis and Evaluation of Well-being Activity-I...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Mohammed, Hamza Haruna Kiss, Gabriel Serrano, J. Artur Lindseth, Frank Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9798331508333

Large language models (LLMs) have significantly impacted natural language processing (NLP), healthcare, and beyond fields. LLMs have demonstrated unprecedented abilities to understand and generate human-like text, making them highly valuable for various tasks, including information retrieval, question-answering, and conversational AI. However, as these models are integrated into real-world applications, especially in sensitive domains such as healthcare, it becomes essential to evaluate their performance comprehensively. We present a process for fine-tuning LLMs to incorporate cardiac exercise and corpus of well-being activity, aiming to promote activity health support through NLP-driven applications. Using QLoRA, you can ensure that the model size remains small and reduce overhead while maintaining the high performance of the fine-tuned model. Subsequently, this paper presents a comparative analysis benchmark pipeline to evaluate fine-tuned models with base models on standard benchmark datasets from the healthcare domain. The aim is to evaluate the performance, robustness and accuracy of these models, showing that the fine-tuning process does not degrade the model performance, but improves the performance to some extent in some benchmarks. © 2025 IEEE.

关键词： Nutrition

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Applications of the Rua-Rai-Sai for Disaster Monitoring and Warning in Thailand 7

Applications of the Rua-Rai-Sai for Disaster Monitoring and ...

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7th International Conference on information technology, InCIT 2023

作者： Teeravech, Kumpee Yungkumyart, Anusorn Srimala, Wira Yarak, Kanitta Samma, Taweesak Thongjing, Patikom Rambhai Barni Rajabhat University Faculty of Computer Science and Information Technology Chanthaburi Thailand Royal Thai Navy Headquarters Bangkok Thailand Co. Ltd Pathum Thani Thailand

ISBN: (纸本)9798350358698

This paper provides an overview of the ongoing developments and applications of the Rua-Rai-Sai (RRS) system in Thailand. RRS is a system for monitoring and issue warnings about natural disasters to its local communities. The fundamental operation centers around the analysis of electromagnetic switch states, which are interconnected through electrical wiring. Various versions of RRS devices has been developed to address different disaster scenarios, including landslides, floods, and elephant intrusions. The default detection range of RRS is 1 km, but can be adjusted based on user requirements. One of the significant strengths of RRS is its user-friendly and ease of maintenance. However, the cost of RRS is heavily depends on the required detection range due to use of electrical wires. © 2023 IEEE.

关键词： Landslides

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Performance LoRa technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

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24th International Seminar on Intelligent technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

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Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

Exploring the Impact of Random Distribution Choices on Parti...

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27th International Conference on Knowledge Based and Intelligent information and Engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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A Study of Enhancing Computational Thinking Skills through STEAM Robotics Activities 2

A Study of Enhancing Computational Thinking Skills through S...

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2nd International Annual Meeting on STEM Education, IAMSTEM 2019

作者： Phadung, Muneeroh Yokkhun, Atcharaporn Persoh, Sulaiman Computer Education Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand Information Technology Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand Computer Science Program Faculty of Science Technology and Agriculture Yala Rajabhat University Yala95000 Thailand

STEAM robotics activities are designed to be an enhancement on students' computational thinking skills, which are claimed to be essential for the 21st century. The aims of this study were to examine the learning outcomes of secondary students' computational thinking skills through the use of STEAM robotics activities, and to investigate the students' satisfaction after the implementation of STEAM robotics activities. A quasi-experimental research design was deployed in this study. The research participants were 37 eleventh-grade students of Private Islamic Schools in Yala province, Thailand. A quiz with pre- and post-test and satisfaction questionnaires were employed as data. Descriptive statistics - including mean, standard deviation and a pair sample t-test - was used to analyse the collected data. The findings were that students' learning outcomes indicated a significant improvement in their computational skills after the use of STEAM robotics activities as compared with before, with the statistical level of significance of 0.05. In response to the second research question, the overall satisfaction of the students after the use of such STEAM robotics activities was high (mean=4.01;SD=0.54). © Published under licence by IOP Publishing Ltd.

关键词： Robotics

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Mutual authentication enabled trust model for vehicular energy networks using Blockchain in Smart Healthcare Systems

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Simulation Modelling Practice and Theory 2024年 136卷

作者： Umar Javed, Muhammad Javaid, Nadeem Alrajeh, Nabil Shafiq, Muhammad Choi, Jin-Ghoo Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science Faculty of Computer Science and Information Technology University of South Asia Lahore54000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia School of Computer Science and Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Healthcare systems face critical issues worldwide such as data breaches, lack of interoperability and inefficiencies in patient data management. These challenges hinder the quality of care and patient outcomes. The increasing adoption of Electric Vehicles (EVs) in Smart Healthcare Systems (SHSs) has brought about new security and privacy challenges. EVs, including electric ambulances, rely on communication networks to exchange critical information and perform energy trading. However, the open nature of these networks makes them vulnerable to various attacks, such as false information dissemination and collusion attacks. In the recent years, Blockchain (BC) technology has emerged as a transformative solution for various industries, including healthcare. The integration of BC in healthcare systems offers enhanced security, transparency and efficiency in managing patient data and other critical information. The paper introduces a data-oriented trust paradigm that is facilitated by revocation transparency. In order to enable the present EVs operating in a SHS to realize their full potential, the model aims to successfully manage security, privacy, storage and other issues. The electric ambulance, an integral part of an SHS, is a special type of EV, which is considered in the study. The proposed approach employs the Password Authenticated Key Exchange by Juggling (J-PAKE) mechanism to provide mutual authentication across distinct entities inside a SHS. Moreover, the Real-time Message Content Validation (RMCV) approach precludes collusion attacks by performing a message credibility check. Moving ahead, anonymization of reputation data is performed via K-anonymity algorithm. Restrictions on the identification of the consistent patterns seen in the reputation data serve to avoid privacy leaks. Additionally, a Proof of Revocation (PoR) technique helps to provide revocation transparency. The Inter Planetary File System (IPFS), a decentralized storage system, houses the vehicl

关键词： Transparency

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