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Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Computers, Materials & Continua 2022年第11期73卷 2351-2370页

作者： Majida Kazmi Hashim Raza Khan Lubaba Mohammad Hashir Bin Khalid saad ahmed qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset

关键词： Electricity forecasting short term higher educational institution artificial neural network random forest accuracy computational time

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Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

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Computers, Materials & Continua 2022年第1期70卷 375-395页

作者： Fareeha Sameen Abdul Momin Kazi Majida Kazmi Munir A Abbasi saad ahmed qazi Lampros K Stergioulas Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Department of Pediatrics and Child Health Aga Khan UniversityKarachi74800Pakistan Surrey Business School University of SurreyGuildfordGU27XHUnited Kingdom Neurocomputation Lab National Centre of Artificial IntelligenceNED University of Engineering&TechnologyKarachi75270Pakistan Faculty of IT and Design The Hague University of Applied Sciences2521 ENThe HagueThe Netherlands

Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the *** being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine *** this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing *** predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 *** design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem *** optimization of predictive model is obtained through selection of significant features and removing data *** machine learning algorithms were applied for prediction of defaulting children for the next immunization *** results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%*** main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting *** information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.

关键词： Machine learning predictive models routine immunization vaccine coverage pakistan optimization SMOTE

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Improving the Transmission Security of Vein Images Using a Bezier Curve and Long Short-Term Memory

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Computers, Materials & Continua 2024年第6期79卷 4825-4844页

作者： ahmed H.Alhadethi Ikram Smaoui ahmed Fakhfakh saad M.Darwish Computer Science and School of Electronics and Telecommunications University of SfaxSfax3029Tunisia LETI Laboratory University of SfaxSfax3029Tunisia Digital Research Center of Sfax Technopark of SfaxSfax3029Tunisia Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandriaEgypt

The act of transmitting photos via the Internet has become a routine and significant *** the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further *** study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous *** main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image *** paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during *** employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious ***,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been *** applications involving the transmission and sharing of images across a *** suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture *** effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of *** scheme ensures a large reduction in computational and energy requirements while minimizing image quality *** approach employs multi-scale characteristics to improve the resistance of signatures against image *** proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.

关键词： Image transmission image compression text hiding Bezier curve Histogram of Oriented Gradients(HOG) LSTM image enhancement Gaussian noise rotation

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Spinal dural arteriovenous fistula:a comprehensive review of the history,classification systems,management,and prognosis

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Chinese Neurosurgical Journal 2024年第1期10卷 48-56页

作者： Ali Alkhaibary Ahoud Alharbi Nada Alnefaie Hajar Alammar Alshaymaa M.Arishy Noor Alghanim Yazeed M.Aldhfyan Arwa Albaiahy Yahya H.Khormi Wael Alshaya saad AlQahatani ahmed Aloraidi ahmed Alkhani Sami Khairy College of Medicine King Saud Bin Abdulaziz University for Health SciencesRiyadhSaudi Arabia King Abdullah International Medical Research Center RiyadhSaudi Arabia Division of Neurosurgery Department of SurgeryKing Abdulaziz Medical CityMinistry of National Guard-Health AffairsRiyadhSaudi Arabia Department of Neurosurgery National Neurosciences InstituteKing Fahad Medical CityRiyadhSaudi Arabia College of Medicine Jazan UniversityJazanSaudi Arabia Department of Neurosurgery King Faisal Specialist Hospital and Research CenterRiyadhSaudi Arabia Department of Surgery Faculty of MedicineJazan UniversityJazanSaudi Arabia

Spinal dural arteriovenous fistulas account for the majority of spinal vascular *** are typically located in the thoracolumbar region and are diagnosed in the middle-aged and elderly *** spinal dural arteriovenous fistulas have been postulated to be acquired,their exact development remains ***,the arteriovenous shunt is situated close to the spinal nerve root,inside the dura mater,where the blood from the radiculomeningeal artery and radicular vein *** history,there have been multiple classification systems of spinal arteriovenous shunts since *** were mainly based on the evolution of diagnostic studies as well as the treatment of these *** classification systems have undergone significant changes over the *** intracranial dural arteriovenous fistula,spinal dural arteriovenous fistula is progressive in *** neurological manifestations,due to venous congestion,tend to be insidious as well as *** include sensory deficits,such as paresthesia,bilateral and/or unilateral radicular pain affecting the lower limbs,and gait *** dural arteriovenous fistulas can be suspected on magnetic resonance imaging/magnetic resonance angiography and confirmed by digital subtraction angiography(DSA).The management includes surgery,endovascular therapy,and in selected cases,*** treatment goal of spinal dural arteriovenous fistula is to halt the progression of the *** prognosis depends on both the duration of symptoms as well as the clinical condition prior to *** present article comprehensively reviews the pathophysiology,changes in classification systems,natural history,clinical manifestations,radiological features,management,and prognosis.

关键词： Connection Endovascular Myelopathy Vascular

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Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

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Computer Systems Science & Engineering 2023年第10期47卷 255-272页

作者： Majida Kazmi Maria Tabasum Shoaib Arshad Aziz Hashim Raza Khan saad ahmed qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan Department of Electrical Engineering PNECNational University of Sciences and Technology(NUST)Karachi75350Pakistan

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor *** integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant *** study presents an IIoT-based sensor node for industrial *** sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud *** initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple *** proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main *** broker’s bridging allows for data backup in case of connection *** proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial *** data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying *** proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

关键词： IIoT sensor node condition monitoring fault classification predictive maintenance MQTT

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Effect of Diabetes Self-Management Education on Glycaemic Control in Sudanese Adults with Type 2 Diabetes

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Journal of Biosciences and Medicines 2024年第3期12卷 316-327页

作者： Sahar Moawia Balla Elnour Tayseer Abdelmotalib ahmed Taha Haiam Abdalla Wadatalla Ziryab Zainelabdin Mohamed Elmahdi Marwah Isam Abdulmajeed Mohammedahmed Rowa Abdelmonem Sidig Hamadto Nahla Yousif Osman Mohammed Saeed Omnia Mubarak saad Abdallah Sulafa Abdelbagi Mustafa ahmed Hanady Abdelhameed ahmed Mohamed Sarah Khalil Fathi Khalil Family Medicine Health Gates Primary Care Riyadh SAU Family Medicine Dhaman Health Assurance Hospitals Company Kuwait KWT Family Medicine First Health Cluster Riyadh SAU Emergency Department Sheikh Khalifa Medical City Abudahbi ARE Family Medicine University of Medical Sciences and Technology Khartoum SDN Family Medicine Sudan Medical Specialization Board-Sudan Khartoum SDN Family Medicine Ultra Care Home Care Company Riyadh SAU Medicine Department Health Gates Primary Care Riyadh SAU Family Medicine Mouwasat Hospital Madinah SAU

Research Background: The high prevalence of diabetes in Sudan, estimated at 16%, highlights the importance of effective health education in diabetes management. Diabetes self-management education has been identified as a crucial tool in enhancing the knowledge, attitudes, and abilities necessary for self-management among individuals with diabetes. Aim: To assess the impact of diabetes self-management education on medication adherence and glycemic control in Sudanese adults with type 2 diabetes before and 3 months after the DSME intervention. Method: The study was conducted in Sudan between September 2022 and March 2023, it was an interventional, one-group, pre- and post-test study that aimed to assess the impact of diabetes self-management education (DSME) on medication adherence and diabetes control in Sudanese adults with type 2 diabetes. The research was conducted in primary health care centers in six cities in Sudan and involved 244 participants. The data entry and statistical analysis were conducted using the Statistical Package for Social Sciences version 27.0. A paired t test was used for analysis. Results: The study included 244 participants, 67% of whom were males. The age mean ± SD was 48.6 ± 9.3 years, and 85.3% of participants were married. Age at onset of diabetes mean ± SD was 40.60 ± 7.81 years;44.6% had diabetes for less than 5 years;and 84.1% had a positive family history of diabetes mellitus. The levels of poor, low, and partial adherence to medication decreased by 8.2%, 4%, and 20.6%, respectively, after the intervention. The levels of good and high medication regime adherence increased by 13% and 19.8%, respectively;BMI decreased by 1.1 ± 0.73 kg/m2 (p = 0.005). The fasting blood sugar decreased by 69 ± 32.9 mg/dl (p = 0.049), and the glycated hemoglobin decreased by 1.21 ± 0.28% (p = 0.001). Conclusions: The findings of this study reinforce the importance of patient education in improving glycemic control and enhancing self-management

关键词： Sudan Adherence Intervention Education Self-Management Diabetes

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Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model

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World Journal of Clinical Oncology 2024年第2期15卷 208-242页

作者： Hao-Ling Zhang Sandai Doblin Zhong-Wen Zhang Zhi-Jing Song Babu Dinesh Yasser Tabana DahhamSabbar saad Mowaffaq Adam ahmed Adam Yong Wang Wei Wang Hao-Long Zhang Sen Wu Rui Zhao Barakat Khaled Department of Biomedical Sciences Advanced Medical and Dental InstituteUniversiti Sains MalaysiaPenang 13200Malaysia School of Public Health Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Clinical College of Chinese Medicine Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Faculty of Pharmacy and Pharmaceutical Sciences University of AlbertaEdmonton AB T6G 2E1Canada Department of Science University of Technology and Applied Sciences RustaqRustaq 10 P.C.329Oman Department of Chemistry and Biochemistry San Diego State UniversitySan DiegoCA 92182United States Department of Pathology Center Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina College of Acupuncture-moxibustion and Tuina Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Universiti Sains Malaysia Advanced Medical and Dental InstitutePenang 13200Malaysia Department of Biomedical Science Universiti Sains MalaysiaPenang 13200Malaysia

BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health *** demise mechanisms play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic *** To investigate the impact of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its *** The foundational genes orchestrating AICD mechanisms were extracted from the literature,underpinning the establishment of a prognostic ***,a microRNA(miRNA)prognostic model was constructed that mirrored the gene-based prognostic *** between high-and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized,with the aim of delineating common influence mechanisms,substantiated through enrichment analysis and immune infiltration *** The mRNA prognostic model in this study encompassed four specific mRNAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin *** miRNA prognostic model integrated four pivotal miRNAs:hsa-miR-615-3p,hsa-miR-519b-3p,hsa-miR-342-3p,and hsa-miR-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and macrophages exhibited inverse correlations with risk scores across all breast cancer ***,Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways,while miRNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly *** Of paramount significance,distinct mRNA and miRNA signature models were devised tailored to AICD,both potentially autonomous prognostic *** study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative ***,this paper revea

关键词： ATP-induced cell death mRNA miRNA Prognostic model Breast cancer

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Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

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Computers, Materials & Continua 2022年第7期72卷 561-576页

作者： Aymen saad Israa SKamil ahmed Alsayat ahmed Elaraby Department of Information Technology Technical College of ManagementKufaAl Furat Alawsat UniversityIraq Department of Medical Laboratory Techniques Al-Mustaqbal University CollegeHillaIraq Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world *** spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be *** Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification *** using CNNs techniques requires a large number of images to learn and obtain satisfactory *** this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and *** this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia *** this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the *** Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the *** achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.

关键词： COVID-19 X-ray image enhancement classification CNN SqueezNet model

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风力发电机组运维数字孪生系统设计

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工程机械 2024年第5期55卷 1-4+255页

作者：杨嘉龙王国庆苏东鹏张旭东覃波石振宇 Ebrahim ahmed Hussein saad 长安大学工程机械学院

针对传统的风力发电机组运维管理存在被动运维、自动化程度低和劳动力需求高等问题，基于MBSE理论，提出建立风力发电机组运维数字孪生系统模型。介绍数字端模型构建过程，基于RPA流程自动化机器人技术的应用，以及基于风力发电机组运... 详细信息

针对传统的风力发电机组运维管理存在被动运维、自动化程度低和劳动力需求高等问题，基于MBSE理论，提出建立风力发电机组运维数字孪生系统模型。介绍数字端模型构建过程，基于RPA流程自动化机器人技术的应用，以及基于风力发电机组运维管理需求的运维系统引擎的设计方法，并开发了风力发电机组运维数字孪生系统原型软件。通过对风力发电机组运维数字孪生系统模型原型的开发，验证了建立风力发电机组运维数字孪生系统的可行性。

关键词： MBSE RPA 数字孪生仿真模型

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