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Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Computers, Materials & Continua 2021年第11期69卷 2013-2029页

作者： C.Rajinikanth P.Selvaraj Mohamed Yacin Sikkandar T.Jayasankar Seifedine Kadry Yunyoung Nam Department of Electronics and Communications Engineering Erode Sengunthar Engineering College(Autonomous)Erode638057India Department of Information Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Electrical Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Faculty of Applied Computing and Technology(FACT) Noroff University CollegeKristiansand4608Norway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded *** e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and *** research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of *** this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT *** presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic *** EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data *** addition,the IoT devices forward the data to the cloud where the actual classification procedure is *** classification process,variational autoencoder(VAE)is used to determine the existence of disease or *** order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes *** obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

关键词： Energy efficiency intelligent models decision support system IoT e-healthcare machine learning

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A Review of Android and iOS Operating System Security

A Review of Android and iOS Operating System Security

引用

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

作者： Khan, Shahnawaz Yusuf, Ammar Haider, Mohammad Thirunavukkarasu, K. Nand, Parma Imam Rahmani, Mohammad Khalid Faculty of Engineering Design and Information and Communications Technology Bahrain Polytechnic Isa Town Bahrain Applied Science University College of Arts and Science Bahrain Saudi Electronic University College of Computing and Informatics Dammam Saudi Arabia Galgotias University Department of Computer Science and Engineering Greater Noida India Sharda University School of Engineering and Technology Greater Noida India Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Mobile devices are an inseparable part of our lives. They have made it possible to access all the information and services anywhere at any time. Almost all of the organizations try to provide a mobile device-based solution to its users. However, this convenience has arisen the risk of losing personal information and has increased the threat to security. It has been observed recently that some of the mobile device manufacturers and mobile apps developers have lost the private information of their users to hackers. It has risen a great concern among mobile device users about their personal information. Android and iOS are the major operating systems for mobile devices and share over 99% of the mobile device market. This research aims to conduct a comparative analysis of the security of the components in the Android and iOS operating systems. It analyses the security from several perspectives such as memory randomization, application sandboxing, isolation, encryption, built-in antivirus, and data storage. From the analysis, it is evident that iOS is more secure than Android operating system. However, this security comes with a cost of losing the freedom. © 2022 IEEE.

关键词： Android (operating system)

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Fire Hawk Optimization based Multi-objective Dynamic Load Balancing in Cloud computing

Fire Hawk Optimization based Multi-objective Dynamic Load Ba...

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Distributed computing and Optimization Techniques (ICDCOT), International Conference on

作者： K Aruna Kumari Thota Soujanya Zaid Alsalami I Rohini Bala Dhandayuthapani V Department of CSE SRKR Engineering College Chinaamiram Bhimavaram India School of Agricultural Sciences SR University Warangal Telangana India The Islamic university Najaf Iraq Dept of EEE Malla Reddy Engineering college for Women Telangana India Department of IT Shinas branch College of Computing and Information Sciences University of Technology and Applied Sciences Oman Arab

Load Balancing (LB) of the tasks on Cloud computing (CC) is the significant characteristics of distribution of the resources from data center. LB has to be taken out in such a way that every Virtual Machines (VMs) must have balanced the load to attain an optimal utilization of its capabilities. As an outcome, the LB of tasks on VM is examined as a significant thing of task scheduling in the cloud. The LB in cloud faced various challenges like fault tolerance as well as data security. In this research, Fire Hawk Optimization (FHO) algorithm is proposed for the dynamic LB in cloud environment. The aim of this research is to enhance the system performance and it considers the multi-objective functions named Makespan, Resource Utilization (RU), Response Time (RT), Degree of Imbalance as well as throughput. The experimental results proven that the proposed FHO-based LB had obtained the 2246, 6.7, 134.23, 141 and 5.5 in the number of tasks of 1500 using nakespan, RT, RU, DOI and throughput. These results show better enhancement when compared to the previous approaches like Particle Swarm Optimization (PSO), Q-learning Modified PSO (QMPSO) and Binary JAYA algorithm.

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A unified fourth-order Bhatnagar-Gross-Krook lattice Boltzmann model for high-dimensional linear hyperbolic equations

arXiv

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arXiv 2024年

作者： Chen, Ying Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

In this work, we first develop a unified Bhatnagar-Gross-Krook lattice Boltzmann (BGK-LB) model for the d(d ≥ 1)-dimensional linear hyperbolic equation (L-HE), where the natural moments and the DdQ(2d2 + 1) [(2d2 + 1) discrete velocities in d-dimensional space] lattice structure are considered. Subsequently, at the acoustic scaling, we conduct an accuracy analysis on the developed BGK-LB model by the direct Taylor expansion (DTE) method, and present the second- and third-order moments of the equilibrium distribution functions (EDFs) to ensure that the BGK-LB model can be fourth-order consistent with the L-HE. And on this basis, when considering the Dirichlet boundary condition, the fourth-order full-way and half-way boundary schemes are proposed to approximate the unknown distribution functions to ensure that the BGK-LB model can be overall fourth-order accurate. Thereafter, based on the kinetic entropy theory, we derive the conditions that the fourth-order moments of the EDFs should satisfy to ensure the microscopic entropy stability of the BGK-LB model. In addition, with the aid of the von Neumann stability analysis, we also discuss the L2 stability of the BGK-LB model and numerically plot the stability regions. In particular, from a numerical perspective, we find that the region of microscopic entropy stability is identical to that of L2 stability. Finally, we carry out some numerical experiments to test the accuracy and stability of the BGK-LB model, and the numerical results are in agreement with our theoretical analysis. In addition, we compare the developed full-way and half-way boundary schemes for the Dirichlet boundary condition, which shows that the full-way boundary scheme is more stable. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images

An Integrated Image Classification Approach to Detect the Fl...

引用

International Conference on computing for Sustainable Global Development (INDIACom)

作者： Mohammed Siddique Tasneem Ahmed Mohd Shahid Husain Department of Computer Application Advanced Computing Research Lab Integral University Lucknow India Faculty of Information Technology Majan University College Muscat Oman Department of Information Technology CAS-Ibri University of Technology and Applied Sciences Muscat Oman

The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR) images which offer images in close to real-time. The Sentinel-1A satellite images from the European Space Agency provide C-band SAR images that have helped researchers achieve enhanced processes of flood detection and flood mapping in flood-prone areas. In regular cases, the Random Forest (RF) and K-Nearest Neighbor (KNN) supervised classification techniques provide higher accuracies than the unsupervised K-Means classification techniques. The integrated approach with the resultant accuracy provides long-reliable elucidation towards flood damages and assessment control. Thenceforth, the RF and KNN supervised classifications based outcomes were integrated with the outcomes of K-Means iterative clustering unsupervised classification producing an enhanced overall accuracy. The overall accuracy from the integrated approach for the city of Basti and Ayodhya is 97% and 93.8% which is higher than the accuracies derived from the supervised and unsupervised classifications. Further, these processed results when refined by time-series analysis over a number of years along with implementing change detection techniques would help in monitoring the flood situation and undertaking precautionary measures.

关键词： Radio frequency Satellites Time series analysis Urban areas Vegetation mapping Radar polarimetry Rivers

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

引用

computing and Data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

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GeoDisasterAINet: An Explainable Deep Ensemble Framework for Real-Time Urban and Rural Disaster Classification and Resilience

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IEEE Access 2025年 13卷 97944-97974页

作者： Narasimha Raju, Akella S Sreekanth, Seelam Gatla, Ranjit Kumar Rajababu, M. Kumar, Devineni Gireesh Flah, Aymen El-Bayeh, Claude Ziad El-Nagdy, Khaled A Alzaed, Ali Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankalattur Tamilnadu Chennai603203 India Institute of Aeronautical Engineering Talangana Hyderabad500043 India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India ENET Centre CEET VSB-Technical University of Ostrava Ostrava708 00 Czech Republic Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Civil Engineering College of Engineering Taif University Taif21944 Saudi Arabia Aditya University Andhra Pradesh Surampalem533437 India

These Sustainable Development Goal 11 (SDG 11), whose target is to make urban and rural settlements resilient and disaster-prepared, integrates with it the significant objective of having disaster-resilient communities and cities. GeoDisasterAINet is an AI deep learning and machine learning model for distinguishing between rural and urban-area occurrences of floods, cyclones, earthquakes, and wildfires effectively. In this article, we introduce GeoDisasterAINet. There are four successive phases in model performance improvement, with increasingly enhancing classification accuracy, namely an integrated CNN (baseline model), an integrated CNN with XGBoost (feature improvement model), an integrated CNN with XGBoost and Multiclass SVM (refinement model for decision boundary), and LIME segmentation for interpretability and model performance ***7, ResNet-50, InceptionV3, and DenseNet-201 are architectures in use in GeoDisasterAINet, and these architectures make three settings for an ensemble: ERI-2025 (EfficientNetB7, ResNet-50, InceptionV3), DRI-2025 (DenseNet-201, ResNet-50, InceptionV3), and DE-2025 (DenseNet-201, EfficientNetB7). In balancing out the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE). With 99.25% accuracy in training, 96.37% accuracy in testing, 98% in precision, 97% in recall, and 97% in F1-score, ERI-2025 + XGBoost + Multiclass SVM, our best model, effectively classified items. LIME-based interpretability uncovered disaster-specific features such as storm behaviour in cyclones, infrastructure loss in earthquakes, wildfire propagation in wildfires, and water build-up in floods, and in confirming that the model was reliable, it helped in an effective location of disasters. GeoDisasterAINet is a strong AI-powered system that addresses directly SDG 11 through its contribution towards safer and disaster-resilient communities and cities. It does this through AI-powered deep learning and machine learning technology

关键词： Deep learning

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A phase-field-based lattice Boltzmann method for two-phase flows with the interfacial mass/heat transfer

arXiv

引用

arXiv 2024年

作者： Chen, Baihui Zhan, Chengjie Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

In this work, we develop a phase-field-based lattice Boltzmann (LB) method for a two-scalar model of the two-phase flows with interfacial mass/heat transfer. Through the Chapman–Enskog analysis, we show that the present LB method can correctly recover the governing equations for phase field, flow field and concentration/temperature field. In particular, to derive the two-scalar equations for the mass/heat transfer, we propose a new LB model with an auxiliary source distribution function to describe the extra flux terms, and the discretizations of some derivative terms can be avoided. The accuracy and efficiency of the present method is also tested through several benchmark problems, and the influence of mass/heat transfer on the fluid viscosity is further considered by introducing an exponential relation. The numerical results show that the present LB method is suitable for the two-phase flows with interfacial mass/heat transfer. © 2024, CC BY.

关键词： Distribution functions

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