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arXiv 2024年

作者： Sikder, Niloy Khan, Al-Masrur Bairagi, Anupam Kumar Masud, Mehedi Tiang, Jun Jiat Nahid, Abdullah-Al Radboud University Medical Center Donders Institute for Brain Cognition and Behaviour Nijmegen Netherlands Faculty of Technology and Bionics Rhine-Waal University of Applied Sciences Kleve Germany Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Faculty of Engineering Multimedia University Cyberjaya63100 Malaysia Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh

Viruses are submicroscopic agents that can infect all kinds of lifeforms and use their hosts’ living cells to replicate themselves. Despite having some of the simplest genetic structures among all living beings, viruses are highly adaptable, resilient, and given the right conditions, are capable of causing unforeseen complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses are the biggest biological threat faced by animal and plant species. It is often challenging to promptly detect the presence of a virus in a possible host’s body and accurately determine its type using manual examination techniques;however, it can be done using computer-based automatic diagnosis methods. Most notably, the analysis of Transmission Electron Microscopy (TEM) images has been proven to be quite successful in instant virus identification. Using TEM images collected from a recently published dataset, this article proposes a deep learning-based classification model to identify the type of virus within those images correctly. The methodology of this study includes two coherent image processing techniques to reduce the noise present in the raw microscopy images. Experimental results show that it can differentiate among the 14 types of viruses present in the dataset with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable way of virus identification subsidiary to the thorough diagnostic procedures. © 2024, CC BY.

关键词： computer aided diagnosis

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Arduino-Based Wearable Health Monitoring System for Elderly Care Using Internet of Things 1st

Arduino-Based Wearable Health Monitoring System for Elderly ...

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Ather, Danish Singh, Anupam Khan, Rubina Liyakat Vijay, Richa Singh, Ramveer Kumar, Raj Amity University in Tashkent Tashkent Uzbekistan Department of Computer Science and Engineering Graphic Era Hill University Dehradun India The Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Amity University Noida Uttar Pradesh Noida India Department of CSE Galgotias College of Engineering and Technology Greater Noida India Faridabad India

ISBN: (纸本)9783031843938

Concerns regarding the health and well-being of the old have been raised globally due to the elderly population’s rapid rise. In order to overcome these difficulties, this study introduces an Internet of Things (IoT)-based wearable health monitoring system for senior care. The suggested system combines a variety of sensors with wireless communication capabilities to continuously monitor the health and physical activities of elderly people. The central element for data collection, processing, and transmission is the Arduino platform, which is renowned for its simplicity and adaptability. In addition to an accelerometer for tracking movement and fall detection, the wearable gadget has sensors for assessing vital signs like heart rate, blood pressure, body temperature, and oxygen saturation. The Arduino board processes the data locally, and it is then wirelessly transferred to a centralized monitoring system using Wi-Fi or Bluetooth Low Energy (BLE) communication. Caretakers or medical experts can access the central monitoring system, which offers real-time visualization and analysis of the gathered data. It makes it possible to discover aberrant health situations early, to act quickly, and to perform remote monitoring. Additionally, the system may produce warnings and notifications in the event of crises, ensuring quick access to medical care. The suggested wearable health monitoring system built on Arduino has a number of benefits, including affordability, mobility, and simplicity of use. It gives elderly people the ability to preserve their independence while offering a safety net through ongoing health monitoring. Additionally, the system enables healthcare professionals to give individualized and timely interventions based on real-time health data and simplifies remote caregiving. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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Methodological insights from unmanned system technologies in a rock quarry environment and geomining heritage site: coupling LiDAR-based mapping and GIS geovisualisation techniques

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Arabian Journal of Geosciences 2023年第11期16卷 1-18页

作者： Pires, Ana Dias, André Silva, Pedro Ferreira, António Rodrigues, Paulo Santos, Tiago Oliveira, Alexandre Freitas, Liliana Martins, Alfredo Almeida, José Silva, Eduardo Chaminé, Helder I. INESCTEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Laboratory of Autonomous Systems Department of Electrotechnical Engineering School of Engineering (ISEP) Polytechnic of Porto Porto Portugal Laboratory of Cartography and Applied Geology (Labcarga) Department of Geotechnical Engineering School of Engineering (ISEP) Polytechnic of Porto Porto Portugal Centre GeoBioTec|UA Aveiro Portugal

The main goal of the research is to present a methodological approach based on geotechnologies to acquire, process and analyse high-resolution 3D data to support georesources monitoring and geoheritage site preservation. The work is focused on the unmanned aerial vehicle (UAV) mapping technology with applications in a challenging framework, i.e. rock quarry and geomining heritage sites. The investigation interconnects several ground fields: engineering geosciences, rock engineering, sensing and aerial technology, and data processing systems for acquiring high-resolution imagery. The study couples two technologies: (1) robotic systems and (2) LiDAR-based mapping and GIS geovisualisation techniques. The approach presented herein shows the complementarity between techniques to improve the rock quarry’s geotechnical assessment and geomining heritage site. It was also possible to operate 3D recording technologies to preserve and record ancient mines’ cultural heritage and surroundings. The methodology shows a common ground for an integrated vision for rocky georesources monitoring by applying UAV mapping technologies. The final outputs allowed the image processing, geospatial analysis and 3D ground modelling of the ‘Malaposta’ gneiss quarry (Lourosa) and the Roman mine ‘Fojo das Pombas’ (Valongo), both sites in NW Portugal.

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Advances in solar energy harvesting integrated by van der Waals graphene heterojunctions

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RSC Advances 2023年第44期13卷 31273-31291页

作者： Le, Top Khac Mai, The-Hung Iqbal, Muhammad Aamir Vernardou, Dimitra Dao, Van-Duong Ponnusamy, Vinoth Kumar Rout, Chandra Sekhar Pham, Phuong V. Faculty of Materials Science and Technology University of Science Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan School of Materials Science and Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering School of Engineering Hellenic Mediterranean University Heraklion71410 Greece Faculty of Biotechnology Chemistry and Environmental Engineering Phenikaa University Hanoi100000 Viet Nam Department of Medicinal and Applied Chemistry and Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan Department of Chemistry National Sun Yat-sen University Kaohsiung80424 Taiwan Centre for Nano and Material Sciences Jain University Bangalore562112 India

Graphene has garnered increasing attention for solar energy harvesting owing to its unique features. However, limitations hinder its widespread adoption in solar energy harvesting, comprising the band gapless in the molecular orbital of graphene lattice, its vulnerability to oxidation in oxidative environments, and specific toxic properties that require careful consideration during development. Beyond current challenges, researchers have explored doping graphene with ionic liquids to raise the lifespan of solar cells (SCs). Additionally, they have paid attention to optimizing graphene/Si Schottky junction or Schottky barrier SCs by enhancing the conductivity and work function of graphene, improving silicon's reflectivity, and addressing passivation issues at the surface/interface of graphene/Si, resulting in significant advancements in their power conversion efficiency. Increasing the functional area of graphene-based SCs and designing efficient grid electrodes are also crucial for enhancing carrier collection efficiency. Flaws and contaminants present at the interface between graphene and silicon pose significant challenges. Despite the progress of graphene/Si-based photovoltaic cells still needs to catch up to the efficiency achieved by commercially available Si p-n junction SCs. The low Schottky barrier height, design-related challenges associated with transfer techniques, and high lateral resistivity of graphene contribute to this performance gap. To maximize the effectiveness and robustness of graphene/Si-based photovoltaic cells, appropriate interlayers have been utilized to tune the interface and modulate graphene's functionality. This mini-review will address ongoing research and development endeavors using van der Waals graphene heterojunctions, aiming to overcome the existing limitations and unlock graphene's full potential in solar energy harvesting and smart storage systems. © 2023 The Royal Society of Chemistry.

关键词： Graphene

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Optimizing Electricity Theft Detection using Neural Turing Machines and Machine Learning

Optimizing Electricity Theft Detection using Neural Turing M...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： Erdal Büyükbiçakci Selcuk Bulat C Senthilkumar A Rajesh Malatthi Sivasundaram Rama Raju Svsv Palla Department of Computer Technologies University of Applied Sciences Information Technologies Vocational Highschool Sakarya Turkey Department of Nanoscience and Nanoengineering Institute of Natural Sciences Sakarya University Turkey 2 Turkish Standards Institution Istanbul Turkey Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of CSE Erode Sengunthar Engineering College Erode India Department of Computer Science and Design KSR College of Engineering Tiruchengode India Department of CSE Vignan’s Institute of Information Technology Duvvada India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Electricity theft detection (ETD) is crucial for smart grids to maintain their cost-effectiveness. Current approaches for identifying theft struggle with large datasets of electricity use owing to problems with missing values, data variance, and nonlinear patterns in the data; furthermore, an integrated infrastructure to coordinate the study of electricity load data is not currently in *** suggested method trains models after feature extraction and stage preprocessing. Data normalization, missing value imputation, and data cleansing are all components of data preprocessing. Utility data sets often suffer from inconsistent and missing data as a result of manual and unsynchronized meter reading and registering processes; however, this can be mitigated through the feature extraction process. All the way through training, the proposed approach stuck with the NTM paradigm. The average accuracy of this state-of-the-art approach is $\mathbf{94.22\%}$, which is higher than RNN and ELM.

关键词： Training Accuracy Turing machines Electricity Biological system modeling Feature extraction Imputation Smart grids Meter reading Synchronization

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Security enhancement and attack detection using optimized hybrid deep learning and improved encryption algorithm over Internet of Things

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Measurement: Sensors 2023年 30卷

作者： M, Syed Shahul Hameed Akshaya, V. Mandala, Vishwanadham Anilkumar, Chunduru VishnuRaja, P. Aarthi, R. Department of Information Technology University of Technology and Applied Sciences - SUR Campus PO BOX: 484 SURPC411 Oman Department of AIML Rajalakshmi Engineering College Chennai India Student in Indiana University US.MS in Data science IU Bloomington BloomingtonIN United States Dept of Information Technology GMR Institute of Technology Rajam Andhra Pradesh Vizianagaram India Department of Computer Science and Engineering Kongu Engineering College India Department of Information Technology Kongu Engineering College Perundurai 638 060 India

Exponential increases in smart devices and reduced costs of sensors have increased applications using IoT (Internet of Things). There has been an extensive analysis on the Internet traffic detection and classification in the past decade, however this is still a trending subject with respect to IoT. The objective of this work is to enhance attack detection rates in a timely fashion. The security and accuracy of attack detection rates an IDS (Intrusion Detection System) that uses HCNN (Hybrid Convolutional Neural Networks) for identifying IoT attacks in a city. After completion of pre-processing stages, FS (Feature Selection) using EHOA (Entropy-Hummingbird Optimization Algorithm) is used. Subsequently, classifications that use optimizations are carried out for IoT attack detections and classification results evaluated. KH-AES (Krill Herd-Advanced Encryption Standard) algorithm in data exchanges for security. The NSL-KDD dataset is utilised in this research to implement IDS. The data was classified based on six types of attacks: U2R, DoS, R2L, Probing, normal, and unknown. The weights in HCNN layers have significant impacts on classification outcomes. The proposed scheme is compared with popular approaches in terms of FS, classifications and security of data shares where it was found that proposed approach yields commendable outcomes. © 2023 The Author(s)

关键词： Entropy

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Users Acceptance Online Attendance Application Using Barcode Scanner: The Case Study in al Buraimi University College (BUC) Students

Users Acceptance Online Attendance Application Using Barcode...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Alsideiri, Abir Alsharida, Rawan A. Hammood, Maytham Shakir, Mohanaad Binti Cob, Zaihisma Rashid Al Shamsi, Ibrahim Muttar, Ahmed Kh. Abood, Rehab Sheker, Mustefa Buraimi Information Technology Department Oman Tikrit University Computer Science Department Tikrit Iraq Cob University of Buraimi College of Business Buraimi Oman Universiti Tenaga National College of Computing and Informatics Malaysia Applied Science University College of Administrative Sciences Manama Bahrain Al-Fallujah Secondary School for Girls General Directorate of Anbar Education Fallujah Iraq Department of the Sewage of Fallujah Ministry of Municipalities and Public Works Fallujah Iraq

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Marking students' attendance within a class session is the prime task of the subject requirements since marking the attendance will regulate the students to attend the session. Moreover, it verifies how many students are attending the class. Previously, attendance was recorded manually by pen and paper. This method takes a long time, especially if the number of students is significant and adds workload to the teacher, and sometimes the data may be prone to error. This paper presents a mobile application for student attendance to avoid these issues. This application will ease the process of attending to the students in the registration instead of the traditional ones where the teacher spends time calling students to know who is present or absent. The students will use their mobile to log in to the application and select the current class to open the camera to scan the code provided by the teacher, and automatically their attendance will be registered. This application is mainly designed for Buraimi University College lecturers and students. © 2022 IEEE.

关键词： E-learning

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Artifact Augmentation for Learning-based Quality Control of Whole Slide Images

Artifact Augmentation for Learning-based Quality Control of ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Artur Jurgas Marek Wodzinski Weronika Celniak Manfredo Atzori Henning Müller Information Systems Institute University of Applied Sciences Western Switzerland (HES-SO Valais) Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

The acquisition of whole slide images is prone to artifacts that can require human control and re-scanning, both in clinical workflows and in research-oriented settings. Quality control algorithms are a first step to overcome this challenge, as they limit the use of low quality images. Developing quality control systems in histopathology is not straightforward, also due to the limited availability of data related to this topic. We address the problem by proposing a tool to augment data with artifacts. The proposed method seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The datasets augmented by the blended artifacts are then used to train an artifact detection network in a supervised way. We use the YOLOv5 model for the artifact detection with a slightly modified training pipeline. The proposed tool can be extended into a complete framework for the quality assessment of whole slide *** relevance— The proposed method may be useful for the initial quality screening of whole slide images. Each year, millions of whole slide images are acquired and digitized worldwide. Numerous of them contain artifacts affecting the following AI-oriented analysis. Therefore, a tool operating at the acquisition phase and improving the initial quality assessment is crucial to increase the performance of digital pathology algorithms, e.g., early cancer diagnosis.

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Electron and neutrino scattering off the deuteron in a relativistic framework

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Physical Review C 2023年第2期107卷 024617-024617页

作者： A. Grassi J. Golak W. N. Polyzou R. Skibiński H. Witała H. Kamada M. Smoluchowski Institute of Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University PL-30348 Kraków Poland Department of Physics and Astronomy The University of Iowa Iowa City Iowa 52242 USA Department of Physics Faculty of Engineering Kyushu Institute of Technology Kitakyushu 804-8550 Japan

We build a relativistic model to perform calculations of exclusive, semiexclusive, and inclusive unpolarized cross sections and various polarization observables in electron and neutrino scattering experiments with deuteron targets. The strong interaction dynamics is defined by an explicit dynamical unitary representation of the Poincaré group, where representations of space translations and rotations in the interacting and noninteracting representations are the same. The Argonne V18 potential is used to construct a relativistic nucleon-nucleon interaction reproducing the experimental deuteron binding energy and nucleon-nucleon scattering observables. Our formalism does not include the pion production channel and neglects two-body contributions in the electromagnetic as well as in the weak nuclear current operator. We show that it is applicable to processes at kinematics, where the internal two-nucleon energy remains below the pion production threshold but the magnitude of the three-momentum transfer extends at least to several GeV.

关键词： Electroweak interactions in nuclear physics Nuclear reactions Polarization phenomena Special relativity Few-body systems

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