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AHHO: Arithmetic Harris Hawks Optimization algorithm for demand side management in smart grids

Discover Internet of Things 2023年第1期3卷 3页

作者： Manzoor, Awais Judge, Malik Ali Islam, Saif ul Neggaz, Nabil Abulaigh, Laith Ahmad, Ishfaq Department of Computer Science COMSATS University Islamabad Islamabad44550 Pakistan Department of Computer Sciences Institute of Space Technology Islamabad Pakistan Faculty of Mathematics and Computers University of Sciences and Technologies-USTO-MB Oran Algeria Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Science and Engineering The University of Texas at Arlington Arlington United States

Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively. © The Author(s) 2023.

关键词： Optimization

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Lévy flights and Lévy walks under stochastic resetting

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Physical Review E 2024年第4期109卷 044147-044147页

作者： Bartosz Żbik Bartłomiej Dybiec Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland

Stochastic resetting is a protocol of starting anew, which can be used to facilitate the escape kinetics. We demonstrate that restarting can accelerate the escape kinetics from a finite interval restricted by two absorbing boundaries also in the presence of heavy-tailed, Lévy-type, α-stable noise. However, the width of the domain where resetting is beneficial depends on the value of the stability index α determining the power-law decay of the jump length distribution. For heavier (smaller α) distributions, the domain becomes narrower in comparison to lighter tails. Additionally, we explore connections between Lévy flights (LFs) and Lévy walks (LWs) in the presence of stochastic resetting. First of all, we show that for Lévy walks, the stochastic resetting can also be beneficial in the domain where the coefficient of variation is smaller than 1. Moreover, we demonstrate that in the domain where LWs are characterized by a finite mean jump duration (length), with the increasing width of the interval, the LWs start to share similarities with LFs under stochastic resetting.

关键词： Brownian motion Escape problems Levy flights Noise

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BXtendDSL: A layered framework for bidirectional model transformations combining a declarative and an imperative language (Summary)

BXtendDSL: A layered framework for bidirectional model trans...

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2023 Fachtagung des GI-Fachbereichs Softwaretechnik, Software Engineering 2023 - 2023 Conference of the GI Software Engineering Division, Software Engineering 2023

作者： Buchmann, Thomas Bank, Matthias Westfechtel, Bernhard Faculty of Computer Science Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Applied Computer Science I University of Bayreuth Universitätsstraße 30 Bayreuth95440 Germany

ISBN: (纸本)9783885797265

This summary is based on an article which appeared in 2022 in The Journal of Systems & Software [BBW22]. Bidirectional transformations have been studied in a wide range of application domains. In model-driven software engineering, they are required for roundtrip engineering processes. We present a pragmatic approach to engineering bidirectional model transformations that assists transformation developers by domain-specific languages, frameworks, and code generators. A thorough evaluation demonstrates conciseness, expressiveness, and scalability of our approach. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Problem oriented languages

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Phytochemical screening, antioxidant properties, and photocytotoxicity of Clinacanthus nutans leaf extracts

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Vegetos 2024年第4期37卷 1652-1661页

作者： Chen, Chee-Shien Loke, Chui-Fung Poh, Tze-Ven Tan, Siow-Ping Department of Physical Science Faculty of Applied Sciences Tunku Abdul Rahman University of Management and Technology Kuala Lumpur 53300 Malaysia Research & Development and Commercialization Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia Department of Computer Science and Embedded Systems Faculty of Computing and Information Technology Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia

Clinacanthus nutans (Burm. f.) Lindau (C. nutans) is renowned in many tropical countries for its wide range of traditional uses and medicinal properties. This study qualitatively screened ethanol, hexane, and ethyl acetate (EA) leaf extracts of C. nutans for phytochemicals and evaluated their antioxidant activities using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and ferric reducing antioxidant power (FRAP) assays. Furthermore, the photocytotoxicity of the extracts against the A431 human skin squamous carcinoma cells was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Qualitative phytochemicals screening revealed the presence of terpenes, alkaloids, phenols, quinones, and chlorophyll, whereas flavonoids and anthraquinones were absent in all crude extracts. In the DPPH assay, the EA crude extract exhibited the lowest EC₅₀ value of 31 ± 2 μg/mL, whereas the same extract demonstrated the highest FRAP value of 7.42 ± 1.87 mmol Fe2+/g DW. After photodynamic therapy (PDT) activation, EA crude extract demonstrated the strongest photocytotoxic activity against A431 cells with an IC₅₀ value of 8 ± 1 μg/mL, followed by the hexane crude extract (IC₅₀ = 16 ± 2 μg/mL) and the ethanol crude extract (IC₅₀ = 20 ± 1 μg/mL). However, all three crude extracts showed negligible activity against the A431 cells in the absence of PDT treatment, even at concentrations as high as 200 μg/mL after 24 h of dark incubation. These findings suggest that C. nutans could serve as a potential source of natural antioxidants and photosensitizers for photodynamic therapy in cancer treatment. © 2023, The Author(s) under exclusive licence to Society for Plant Research.

关键词： Antioxidant activity Clinacanthus nutans PDT treatment Photocytotoxicity Phytochemical screening

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Inter-correlation between Sunspot Oscillations and Their Internal Structures

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Research in Astronomy and Astrophysics 2022年第11期22卷 97-106页

作者： Libo Fu Zizhan Zhu Ding Yuan Jiaoyang Wang Song Feng Sergey Anfinogentov Institute of Space Science and Applied Technology Harbin Institute of TechnologyShenzhen 518055China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Institute of Solar-Terrestrial Physics SB RAS Lermontov st.126a664033IrkutskRussia

Three-and five-minute oscillations are commonly observed in any *** they are modulated by the internal thermal and magnetic structures of a sunspot,they could be used as an effective tool for researching sunspot *** this paper,we investigate the properties of oscillations in sunspot groups with varying sizes and magnetic fields,and aim to establish the relationships between sunspot oscillations and its internal structure *** selected three groups of the unipolar sunspot with approximately axial-symmetric magnetic field and calculated their Fourier spectra based on the ultraviolet/extreme ultraviolet emission intensity variations recorded by the Solar Dynamics Observatory/Atmospheric Imaging *** found that the distribution of three-minute oscillation is defined by the joint effect of diverging magnetic field and the stratification of the sunspot *** distribution could be modified by any invading magnetic structures in the *** contrast,the five-minute oscillations are more prominent in small spots,implying that five-minute oscillation is very closely connected with umbral dynamics.

关键词： (Sun:)sunspots Sun:oscillations methods:data analysis

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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A Comprehensive Classification and Analysis of Geomagnetic Storms Over Solar Cycle 24

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Research in Astronomy and Astrophysics 2024年第8期24卷 75-84页

作者： Raja Adibah Raja Halim Shah Nurul Shazana Abdul Hamid Mardina Abdullah Adlyka Annuar Idahwati Sarudin Zahira Mohd Radzi Akimasa Yoshikawa Department of Applied Physics Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia Space Science Centre(ANGKASA) Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 BangiMalaysia Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Physics Universiti Sains Malaysia11800 USMPulau PinangMalaysia Malaysian Space Agency(MYSA) Kuala Lumpur50480Malaysia International Research Center for Space and Planetary Environmental Science(i-SPES) Kyushu University819-0395 FukuokaJapan

A geomagnetic storm is a global disturbance of Earth?s magnetosphere,occurring as a result of the interaction with magnetic plasma ejected from the *** considerable research,a comprehensive classification of storms for a complete solar cycle has not yet been fully developed,as most previous studies have been limited to specific storm *** study,therefore,attempted to present complete statistics for solar cycle 24,detailing the occurrence of geomagnetic storm events and classifying them by type of intensity(moderate,intense,and severe),type of complete interval(normal or complex),duration of the recovery phase(rapid or long),and the number of steps in the storm?s *** analysis was applied to data from ground-based magnetometers,which measured the Dst index as provided by the World Data Center for Geomagnetism,Kyoto,*** study identified 211 storm events,comprising moderate(177 events),intense(33 events),and severe(1 event)*** 36%of ICMEs and 23%of CIRs are found to be geoeffective,which caused geomagnetic *** to four-step development of geomagnetic storms was exhibited during the main phase for this solar *** showed the geomagnetic storms developed one or more steps in the main phase,which were probably related to the driver that triggered the geomagnetic storms.A case study was additionally conducted to observe the variations of the ionospheric disturbance dynamo(Ddyn)phenomenon that resulted from the geomagnetic storm event of 2015July *** attenuation of the Ddyn in the equatorial region was analyzed using the H component of geomagnetic field data from stations in the Asian sector(Malaysia and India).The variations in the Ddyn signatures were observed at both stations,with the TIR station(India)showing higher intensity than the LKW station(Malaysia).

关键词： (Sun)sunspots-Sun activity-(Sun)solar-terrestrial relations-Sun coronal mass ejections(CMEs)

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Developing Healthcare Chatbot: Migrant Workers' Study With Design Implication 8

Developing Healthcare Chatbot: Migrant Workers' Study With D...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Jarupreechachan, Weerachaya Surasak, Thattapon Kasetsart University Faculty of Engineering at Kamphaeng Saen Department of Computer Engineering Kamphaeng Saen Campus Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350399646

Migrant workers are often those with low income who are subject to a high risk of virus transmission due to environmental and occupational exposures. The COVID-19 pandemic is reported to exacerbate the existing vulnerabilities of these marginalized groups. This study aims to shed light on how the potential of a conversational agent (Chatbot) could help improve migrant workers' health information seeking and health literacy. The exploration extended to the existing barriers to seeking health information strategies to obtain the needed information during COVID-19. This study conducted an exploratory qualitative study using semi-structured interviews with a scenario-based approach to study the experiences of the migrant Thai low-income workers in Taiwan. A total of 12 Thai stakeholders and migrant workers participated in the study. Migrant Thai workers developed distinct health information behaviors and improved various types of health competencies amid the COVID-19 pandemic. However, the critical barriers to health literacy remain. As a result, this work found that the Chatbot posted several potential features that could improve health literacy on Access. Therefore, Apprise and Apply depend on the understanding, which can be enhanced if the understanding rate increases. This study addresses the need for the development of the potential of a conversational agent. The findings of this study would guide the authority and technological interventions to respond to migrant workers' health information needs with appropriate tools. © 2023 IEEE.

关键词： COVID-19

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