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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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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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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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Oilfield produced water treatment in internal-loop airlift reactor using electrocoagulation/flotation technique

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Chinese Journal of Chemical Engineering 2018年第4期26卷 879-885页

作者： saad H.Ammar ahmed S.Akbar Department of Chemical engineering AL-Nahrain University

Oilfield produced water is large quantities of salty water trapped in underground formations and subsisted under high temperatures and pressures that are brought to the surface along with oil during production. Produced water(PW) contains a lot of pollutants such as hydrocarbons and metals, this water must be treated before disposal. Therefore, different techniques are being used to treat produced water. Electrocoagulation is an efficient treatment technique involving the dissolution of anodes and formation of electro-coagulants, while the simultaneous generation of H_2 bubbles at the cathode leads to the pollutant removal by flotation. Electrocoagulation(EC)method is one of the most promising and widely used processes to treat oilfield produced water. In the present work, a conventional internal-loop(draught tube) airlift reactor was utilized as electrocoagulation/flotation cell for PW treatment by inserting two aluminum electrodes in the riser section of the airlift reactor. The EC airlift reactor was operated in a batch mode for the liquid phase. Different experimental parameters were studied on the oil and turbidity removal efficiencies such as current density, initial pH, electrocoagulation time, and air *** experimental results showed that mixing of the oil droplets in the PW was accomplished using only the liquid recirculation resulted by H_2 microbubbles generated by EC process which enhanced the oil removal. The experimental results further showed that the EC time required achieving ≥ 90% oil removal efficiency decreases from 46 to 15 min when operating current density increases from 6.8 to 45.5 mA·cm^(-2). This reactor type was found to be highly efficient and less energy consuming compared to conventional existing electrochemical cells which used mechanical agitation.

关键词： Produced water Wastewater treatment Electrocoagulation/flotation Internal loop Airlift reactor

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Nature-inspired Hybrid Optimization Algorithms for Load Flow Analysis of Islanded Microgrids

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1250-1258页

作者： saad Mohammad Abdullah Ashik ahmed Quazi Nafees Ul Islam the Department of Electrical and Electronic Engineering Islamic University of TechnologyBoard BazarGazipur 1704Bangladesh

Load flow analysis is a significant tool for proper planning,operation,and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental ***,due to the absence of a slack bus in an islanded microgrid,modified load flow algorithms should be adopted considering the system frequency as one of the solution *** paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded *** nature-inspired algorithms such as genetic algorithm(GA),differential evolution(DE),flower pollination algorithm(FPA),and grasshopper optimization algorithm(GOA)are separately merged with imperialistic competitive algorithm(ICA)to form four hybrid algorithms named as ICGA,ICDE,ICFPA,and *** of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system.A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics *** the statistical analysis,it is identified that on an average,ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

关键词： Nature-inspired hybrid algorithm global optimization islanded microgrid load flow analysis

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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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Dynamical analysis and anti-synchronization of a new 6D model with self-excited attractors

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Applied Mathematics(A Journal of Chinese Universities) 2023年第1期38卷 27-43页

作者： saad Fawzi Al-Azzawi ahmed S.Al-Obeidi Department of Mathematics College of Computer Science and MathematicsUniversity of MosulMosul 41002Iraq

A novel 6D dissipative model with an unstable equilibrium point is introduced *** of the dynamic characteristics of the proposed model were explored via analyses and numerical simulations including critical points,stability,Lyapunov exponents,time phase portraits,and circuit ***,anti-synchronization phenomena were implemented on the new ***,the error dynamics is ***,four different controllers are adopted to stabilize this error relying on the nonlinear control technique with two main ways:linearization and Lyapunov stability *** comparison with previous works,the present controllers realize anti-synchronization based on another method/linearization ***,a comparison between the two ways was *** simulation results show the effectiveness and accuracy of the first analytical strategy.

关键词： 6D hyperchaotic system self-excited attractor anti-synchronization Lyapunov stability theory

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DETECTION OF ELECTROMAGNETIC INCLUSIONS USING TOPOLOGICAL SENSITIVITY

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Journal of Computational Mathematics 2017年第5期35卷 642-671页

作者： Abdul Wahab Tasawar Abbas Naveed ahmed qazi Muhammad Zaigham Zia Bio Imaging and Signal Processing Lab. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 305-701 Daejeon Korea Department of Mathematics and Statistics Faculty of Basic and Applied Sciences International Islamic University 44000 Islamabad Pakistan Weierstrass Institute for Applied Analysis and Stochastics Leibniz Institute in Forschungsverbund Berlin e. V. (WIAS) Mohrenstr. 39 10117 Berlin Germany Department of Mathematics COMSATS Institute of Information Technology Islamabad Campus Park Road Chak Shahzad 44000 Islamabad Pakistan

In this article, a topological sensitivity framework for far-field detection of a diamet- rically small electromagnetic inclusion is established. The cases of single and multiple measurements of the electric far-field scattering amplitude at a fixed frequency are tak- en into account. The performance of the algorithm is analyzed theoretically in terms of its resolution and sensitivity for locating an inclusion. The stability of the framework with respect to measurement and medium noises is discussed. Moreover, the quantitative results for signal-to-noise ratio are presented. A few numerical results are presented to illustrate the detection capabilities of the proposed framework with single and multiple measurements.

关键词： Electromagnetic imaging Topological derivative Localization Resolution analysis Stability analysis Medium noise Measurement noise.

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3-D seismic interpretation of stratigraphic and structural features in the Upper Jurassic to Lower Cretaceous sequence of the Gullfaks Field,Norwegian North Sea:A case study of reservoir development

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Energy Geoscience 2021年第4期2卷 287-297页

作者： Muhammad Usman Numair ahmed Siddiqui Eduardo Garzanti Muhammad Jamil qazi Sohail Imran Luqman ahmed Laboratory for Provenance Studies Department of Earth and Environmental SciencesUniversity of Milano-BicoccaPiazza Della Scienza 420126MilanoItaly Geoscience Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Shale Gas Research Group Institute of Hydrocarbon RecoveryUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Institute of Geology University of the PunjabLahore56300Pakistan

The 3-D seismic dataset is a key tool to analyze and understand the mechanism of structural and stratigraphic hydrocarbon(HC)trapping in the *** used subsurface seismic characterization methods for fractures are based on the theory of effective anisotropy *** aim of this work is to improve the structural images with dense sampling of 3-D survey to evaluate structural and stratigraphic models for reservoir development to predict reservoir *** present study of the Gullfaks Field,located in the Norwegian North Sea Gullfaks sector,identifies the shallowest structural *** steepness of westward structural dip decreases eastward during the Upper Jurassic to Lower Cretaceous *** sands consist of the Middle Jurassic deltaic deposits and Lower Jurassic fluvial channel and delta plain *** supply steadily prevails on sea-level rise and the succession displays a regressive trend indicated by a good continuous stacking *** key factor for the development of reservoirs in the Gullfaks Field is fault transmissibility with spatially distributed *** majority of mapped faults with sand-to-sand contacts are non-sealing,which provide restriction for the HC flow between the fault *** traps for HC accumulation occur between the post-rift and syn-rift strata,*** set by extensional system,unconformity trap at the top of syndeposition,and structural trap due to normal *** reservoir quality in the studied area is generally excellent with average 35%porosity and permeability in the Darcy *** findings are useful to better understand the development of siliciclastic reservoirs in similar geological settings worldwide.

关键词： 3-D seismic model Mesozoic stratigraphic boundary Structural trap Reservoir quality Gullfaks Field

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