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Content Based Automated File Organization Using Machine Learning Approaches

Computers, Materials & Continua 2022年第10期73卷 1927-1942页

作者： Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangorMalaysia School of information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Riphah School of Computing&innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120GyeonggidoKorea Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

in the world of big data,it’s quite a task to organize different files based on their *** with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer *** of file management related tasks will be solved if the files on any operating system are somehow categorized according to their ***,the browsing process can be performed quickly and *** research aims to design a system to automatically organize files based on their similarities in terms of *** proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer *** results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user *** results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their *** proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.

关键词： File organization natural language processing machine learning

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and iT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and intelligent Energy Management System(iEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the internet of Energy(ioE)*** ioE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an iEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： intelligent energy management system smart cities machine learning

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Modeling Radon Diffusion across Some Areas of Southern Jordan

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Applied Mathematics 2015年第8期6卷 1271-1275页

作者： hussam Alrabaiah Department of Mathematics Tafila Technical University Tafila Jordan

A numerical has been developed for the transport of radon gas in the soil-rocks of Afrahotsprings, southern of Jordan using a one-dimensional model diffusion formula. Model Result shows a consistent relation between distance from radiation source and radon concentration. Data model output were in the range of data collected from previous studies.

关键词： Modeling Radioactive Radon Diffusion

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GTR-Mediated Radial import Directs Accumulation of Defensive Glucosinolates to Sulfur-Rich Cells in the Phloem Cap of Arabidopsis inflorescence Stem

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Molecular Plant 2019年第11期12卷 1474-1484页

作者： Deyang Xu Pascal Hunziker Olga Koroleva Andreas Blennow Christoph Crocoll Alexander Schulz hussam Hassan Nour-Eldin Barbara Ann Halkier DynaMo Center Department of Plant and Environmental SciencesFaculty of ScienceUniversity of Copenhagen1871 Frederiksberg CDenmark Lonza Biologies Pic 228 Bath RoadSlough SL14DXUK Section for Plant Glycobiology Department of Plant and Environmental SciencesFaculty of ScienceUniversity of Copenhagen1871 Frederiksberg CDenmark

in the phloem cap region o i Arabidopsis plants,sulfur-rich cells(S-cells)accumulate>100 mM glucosinolates(GLS),but are biosynthetically *** source and route of S-cell-bound GLS remain *** this study,using single-cell sampling and scanning electron microscopy with energy-dispersive X-ray analysis we show that two GLS importers,NPF2.10/GTR1 and NPF2.11/GTR2,are critical for GLS accumulation in S-cells,although they are not localized in the *** of GLS levels in S-cells in multiple combinations of homo-and heterografts o lg t r l gtr2,biosynthetic null mutant and wild-type plants indicate that S-cells accumulate GLS via symplasmic connections either directly from neighboring biosynthetic cells or indirectly to non-neighboring cells expressing GTR1/*** sources and transport routes exist for different types of GLS,and vary depending on the position of S-cells in the inflorescence *** on these findings,we propose a model illustrating the GLS transport routes either directly from biosynthetic cells or via GTR-mediated import from apoplastic space radially into a symplasmic domain,wherein the S-cells are the ultimate ***,we observed accumulation of the cyanogenic glucoside defensive compounds in high-turgor cells in the phloem cap of Lotus japonicus,suggesting that storage of defensive compounds in high-turgor cells may be a general mechanism for chemical protection of the phloem cap.

关键词： Arabidopsis glucosinolate S-cell phloem cap xylem symplasmic transport

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The future of competitive advantage in Oman: integrating green product innovation, Al, and intellectual capital in business strategies

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international Journal of innovation Studies 2024年第2期8卷 154-171页

作者： Fadi Abdelfattah Mohammed Salah Khalid Dahleez Riyad Darwazeh hussam Al Halbusi Modern College of Business and Science(MCBS) MuscatOman Dean College of Business Administration A'Sharqiyah UniversityIbraOman Head of Accounting Department Business SchoolAl-Ahliya Amman UniversityAmmanJordan Management Department Ahmed Bin Mohammed Military College(ABMMC)DohaQatar

This study delves into the dynamics of green product innovation,artificial intelligence(Al)adaption,and intellectual capital,investigating their impact on the competitiveness of firms in *** emphasizes the crucial role of government intervention and R&D investments in this *** on the responses of 214 top managers in Oman,the research employs structural equation modeling to analyze the intricate relationships between these *** findings underscore a significant positive correlation between green innovation,Al implementation,and intellectual capital,with government involvement and R&D investments as vital *** study provides a novel perspective on the synergy of technology,innovation,and intellectual capital in developing *** offers essential insights for business leaders,policymakers,and scholars,highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive *** research adds to the existing body of knowledge on innovation and *** offers practical implications for enhancing firm performance in Oman and similar emerging markets.

关键词： Green product innovation R&D investments Ai adoption intellectual capital Government involvement Knowledge-driven culture Oman

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COViD-19 infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad irfan Uddin Wiyada Kumam Poom Kumam hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&iT Glim Institute of Modern StudiesBahawalpur63100Pakistan institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COViD-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COViD-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROis)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COViD-19 collected in Pakistan.

关键词： COViD-19 machine learning fuzzy c-mean deep learning J4

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Nano-Enhanced Dairy Scum Biodiesel Blends:Unleashing the Power of Aluminium Oxide for Sustainable and Efficient Engine Performance

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Journal of Thermal Science 2025年第1期 283-302页

作者： Veeranna MODi Prasad B.RAMPURE Sunil Kumar SHETTY Madeva NAGARAL Channa Keshava NAiK N Johar MGM Muhammad irsyad ABDULLAH Abdullah i.AL-MANSOUR Shamshad ALAM KLE College of Engineering and Technology ChikodiVisvesvaraya Technological University Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT)Department of Mechanical Engineering Aircraft Research and Design Centre HAL Department of Mechanical Engineering BGS College of Engineering and TechnologyBengaluruAffiliated to Visvesvaraya Technological University Management and Science University Department of Civil Engineering College of EngineeringKing Saud University

This study investigates the effect of aluminium oxide nanoparticlesaddition to biodiesel blends made from dairy milk scum on performance and combustion characteristics of the diesel *** dispersion of Al2O3nanoparticles in B20 blends at different concentrations was done with the help of *** number of blends were prepared for the *** Machine learning algorithms(Random Forest（RF）and CatBoost) was used for the *** results show that in comparison to biodiesel blends without nanoparticles,the kinematic viscosity and density is higher for fuel blends with *** these Fuel blends have higher calorific *** blends exhibited reduced Brake Specific Fuel Consumption（BSFC） of2.85% than the blends without nano particles(Dairy Scum Methyl Ester Biodiesel 20%+Neat Diesel 80%（in volume）,DSMEB20),57.14% less CO,40.8% less hydrocarbon,and increased NOxemissions compared to conventional diesel,contributing to the development of environmentally friendly and renewable biofuel blends with *** B20NP30 is the optimal blend for performance and emission *** study concludes with findings on enhanced Brake Thermal Efficiency（BTE） of 26.29% in 3×10-5（in volume） Al2O3nanoparticle-blended DSME B20 and other DSME B20 fuel blends,emphasizing the importance of optimal nanoparticle *** correlation matrix shows how engine load,efficiency measures（BTE,BSFC）,and emissions（CO,CO2,NOx,Smoke） are connected in complex *** results help us understand the complicated dynamics of engine performance and emission characteristics ***'s diagram for BTE and BSFC shows that CatBoost-based BTE models perform superior to RF-based models during the training as well as testing *** results were obtained for CO and CO2emission results.

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Transport of Suspended Solids in Dammam (Saudi Arabia) Coastal Areas: Fish Market Works

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Journal of Water Resource and Protection 2010年第6期2卷 540-544页

作者： hussam Al-Rabai’ah 不详

Some new projects are considered in the eastern coasts of Dammam city, Saudi Arabia Dredging operations would significantly alter coast hydrological and sediment transport processes. it is important that the project areas must keep flushing the fresh sea water in and out with good water quality parameters, which are currently facing increased pressure from urbanization and navigation requirements in conjunction with industrial developments. A suspended solids or sediments are expected to affect the flora and fauna in that area. A numerical modeling study in needed to study the effect of dredging and in particular the suspended sediments concentrations (mg/L) changed in the region. The results were obtained using finite element method and Newton-Raphson iterations.

关键词： Numerical Modeling TSS Arabian Gulf Sediment Transport

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Combinations of chronic conditions,functional limitations and geriatric syndromes associated with periodontal disease

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Family Medicine and Community Health 2022年第3期10卷 20-26页

作者： hussam M Alqahtani Siran M Koroukian Kurt Stange Nabil F Bissada Nicholas K Schiltz Department of Population and Quantitative Health Sciences Case Western Reserve University School of MedicineClevelandOhioUSA Department of Preventive Dental Science King Saud bin Abdulaziz University for Health SciencesRiyadhSaudi Arabia Center for Community Health integration Case Comprehensive Cancer CenterClevelandOhioUSA Department of Periodontics Case Western Reserve University School of Dental MedicineClevelandOhioUSA

Objective To identify complex multimorbid conditions,including chronic conditions,functional limitations and geriatric syndromes,associated with the presence and severity of periodontal disease(PD),after accounting for a series of demographic and behavioural *** This cross-sectional study used secondary data from a nationally representative sample,classification and regression tree analysis and random forest identified combinations of specific conditions constituting complex multimorbidity associated with the presence and severity of *** US National Health and Nutritional Examination Survey(2013-2014).Participants individuals 60 years of age or older who completed a periodontal *** Among 937 participants aged 60 and over,the prevalence of PD was 72.6%.PD was associated with sociodemographic factors and limitations in instrumental activities of daily *** sex and non-white race were the two most critical predictors of stage iii/iV *** important factors included age,education level and the federal poverty *** Rather than chronic conditions or geriatric syndromes,PD was associated with sociodemographic factors and functional *** for the co-occurrence of sociodemographic and functional limitations will help recognise older adults who are at an increased vulnerability to the severity of PD.

关键词： periodontal limitations functional

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An Automated Approach for Software Fault Detection and Recovery

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Communications and Network 2016年第3期8卷 158-169页

作者： Amjad A. Hudaib hussam N. Fakhouri Department of Computer information Systems The University of Jordan Amman Jordan Department of Computer Science The University of Jordan Amman Jordan

Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.

关键词： Software Engineering Autonomic Software Systems Automatic Recovery Automatic Diagnosis Auto Restore

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