Scopus Research — Ammar Kareem Obayes

Accurate methods for early detection are required since heart disease is still a major problem in world health. In order to accurately forecast the occurrence of heart illness using electrocardiogram data, this research introduces a hybrid model called MLP-FRCNN (Multi-Layer Perceptron-Faster Region-Based Convolutional Neural Network). The suggested method uses the DNLMS algorithm to remove baseline fluctuations and motion artifacts from ECG signals before processing them. By utilizing Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT), we are able to extract features, with a particular emphasis on important components such the QRS complex. To improve the Faster R-CNN, the Honey Badger Algorithm (HBA) takes into account factors including computing efficiency and overlapped detecting boxes. Results from further tests show that, in comparison to contemporary methods, we achieve better accuracy, sensitivity, specificity, and F1 score. Machine learning, which began with data modification and accumulation, has evolved into a powerful tool for driving transformative change and remains a central component of the ongoing pursuit of artificial intelligence. Accurate detection and treatment for coronary heart disease patients are greatly enhanced by the suggested model’s higher speed of convergence and enhanced predictive capabilities. To improve accuracy, an FFNN combiner takes the estimates from both the Faster R-CNN and MLP and applies them to patient’s demographics information and low-order characteristics. With a 98% accuracy rate, the hybrid model outperforms both MLP (94% accuracy rate) and HBA-FRCNN (96% accuracy rate). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

The adverse environmental impacts of fossil fuels is becoming a major concern considering the climate changes. In this respect, the H2 as a carbon-free energy carrier has absorbed interest of researchers and policy makers to pay more attention to this field. In this regard, the solar energy driven frameworks are of major importance due to the abundance of solar energy as well as being a clean energy resource. However, the solar based systems suffer from lower efficiency compared to conventional fossil fuel-based systems due to large exergy destructions. One way to solve this problem and to enhance solar-based system performance is employment of nanoparticles for improving heat transfer characteristics of solar thermal loop. Present research is an attempt in this regard in which effects of employment nanofluid instead of the pure solar salt is investigated in a solar power tower system. The solar power tower unit supplies required energy to run a steam cycle for power generation and a thermochemical H2 production unit for co-generation of power and H2. Feasibility evaluations are carried out based on thermodynamic laws and exergy-based analyses as well as economic investigations are conducted to assess the system performance and multi-objective optimization is conducted to specify the optimum operation of system. A parametric evaluation is carried out to consider how design variables affect the system performance, prior to implementation of multi-objective optimization. The results have revealed positive effect of nanofluid application instead of pure solar salt and indicated enhancement of 4−5% on technical performance. In addition considering the economic investigations, the nanofluid employment bring about a reduction by around 3−4% in unit cost of product of cogeneration plant, depending on the operating conditions. © 2023 Hydrogen Energy Publications LLC

Power generation of a fuel cell (FC) is mostly dependent upon operational variables such as cell temperature and membrane water content. There is an individual maximum power point (MPP) on the P-I curve of the FC. The location of the MPP varies with respect to the MPP position. Thus, an MPP tracking (MPPT) system should exist to guarantee that the FC works at the MPP in order to maximize the functionality. Due to their straightforward structure, prevalent MPPT methods had strong functionality. However, their primary limitations include fluctuations around the MPP and inefficiency under abrupt variations of operating conditions. The primary objective of this paper is to maintain the PEMFCs operation at an efficient power point. To this purpose, the efficiency of PEM-FC is tested and enhanced using a variety of MPPT-based smart controller techniques. To determine the appropriate MPPT controller parameters, the modified fluid search optimization (MFSO) approach and fuzzy logic controller (FLC) are employed. Furthermore, the MFSO method is deployed to adjust the membership functions (MFs) of the FLC. The MFSO is an excellent approach for coping with the stochastic behavior of the PEM-FC system when the temperature and water content of the membrane change. In terms of improved dynamic behavior, better convergence rate, reduced oscillations, and better tracking of the MPP, the results obtained by employing the suggested strategy demonstrate the superior functionality of the system compared to case using other methods. Moreover, the power generated by the PEMFC system is less than the nominal capacity for the temperature's rated capacity. Therefore, the deficit in power would be covered by transacting power with the grid. © 2022 Hydrogen Energy Publications LLC

One of the best solutions to overcome environmental, technical as well as economic problems in the power system is the use of Plug-in hybrid electric vehicles (PHEVs). The penetration of a large number of PHEVs in the power system and the possibility of their proper control and management provide benefits of a large energy storage system for the distribution system operator. However, the correct management of PHEVs along with renewable energy sources (RESs) is a very important challenge that requires more research in this field. The main goal of the article is the scheduling of a microgrid with several PHEVs and RESs in order to achieve economic, technical and environmental benefits. To obtain more accurate results in microgrid operation, the intermittent behavior of renewable resources, PHEVs and loads has been modeled using the Mont Carlo simulation (MCS). Uncertainty parameters considered in this article include the charging demand of PHEVs, loads, electricity price and output power of RESs. The objective function is to reach minimum total costs considering the technical constraints. In order to resolve the defined optimization problem, including the objective function and the constraints of the problem, the modified sparrow search (MSS) algorithm is applied. The recommended technique is simulated on the test network with the MATLAB software and the outcomes are compared with conventional algorithms. As the results of the simulations show the suggested scheme performs superior performance than other optimization algorithms. © 2023 Elsevier Ltd

With the expanding demand for electrical energy across the world and the growing concern about environmental matters resulting from the widespread use of fossil fuels in the power system, it is necessary to find suitable alternatives to resolve the problem. In this regard, Renewable Energy Sources (RES) that produce almost no pollution have become the preferred means of supplying the global energy demand. In this paper, a novel approach is proposed for formulating the problem and reducing reliability costs to achieve the minimum total cost of the grid. Concurrently, the transportation system has been replacing conventional vehicles with Electric Vehicles (EVs), where Plug-in Electric Vehicles (PEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) have got the public's attention. Thanks to the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies, it is technically possible and economically justified to plug in such vehicles to receive/inject power from/to the grid. Alternatively, an emerging concept in electric power systems, i.e. Microgrid (MG), was developed and employed to level up the RESs’ penetration rate and maximize the capabilities of EVs using the smart infrastructure. Indeed, the V2G option is utilized for mitigating operating costs in order to host PEVs in the network. Therefore, optimally scheduling the MG would be of utmost importance. Thus, an efficient day-ahead stochastic operation framework has been developed in this research work for an MG equipped with renewable energy-powered Distributed Generation (DG) units and EVs. The mentioned stochastic programming framework is based on the Unscented Transform (UT). It is worth mentioning that the problem has been formulated as a stochastic programming problem with the objective of optimizing the total operating cost. The studied problem is then tackled using a biomimicry well-known approach called the “Converged Barnacles Mating Optimizer (CBMO) algorithm” and the results derived from simulating the problem would be compared to the ones achieved by some prominent algorithms. © 2022 Elsevier Ltd

In the present research, the least squares support vector machine technique is employed to develop a relationship to predict the total useful exergy yield of a hybrid renewable energy system consisting of a building integrated photovoltaic thermal (PVT) system and an earth air heat exchanger (EAHX) system. This system is designed to bring the temperature of the ambient air to the desired temperature and also to supply the required electricity of the building. In the cold months of the year, the ambient air is preheated by passing through the PVT and EAHX systems. In the hot months of the year, the ambient air is pre-cooled by passing through the EAHX system, while the air leaving the building is also used to cool the photovoltaic panels. The electricity produced by photovoltaic panels is used throughout the year to meet the electricity needs of the building. The dimensions of different parts of the PVT and EAHE systems along with air flow rate are considered as the main variables and the annual exergy yield of the hybrid system is considered as the dependent variable. The LSSVM model in terms of (R = 0.9670, RMSE=1,152,165.21, and MAPE=29,264.80) resulted in promising outcomes to simulate the useful exergy. © 2023

Due to rising global energy demand and mounting environmental concerns associated with the widespread use of fossil fuels in conventional power plants, it is imperative that viable and cleaner energy sources are used. Here, virtually pollution-free renewable energy sources have replaced traditional fossil fuels as the go-to option for meeting the rising energy demand. This research article utilizes a new formulation for minimizing the total cost of a microgrid through a short-term operational strategy. Microgrids and demand-side management can improve the distribution network’s efficiency and reliability. To achieve this goal, this paper explores how to best schedule the uncertain operation of a microgrid including both renewable energy resources like wind turbines and photovoltaics, as well as dispatchable resources like fuel cells, microturbines, and electrical storage devices connected to charging stations for electric vehicles. Considering the unpredictability of wind power and solar power outputs, besides the behavior of plug-in electric vehicle owners in terms of plugging into the grid to inject or receive power, a stochastic programming-based framework is introduced for the operation of microgrids running in the grid-integrated mode. In this study, an innovative and effective optimization algorithm is employed, which is the modified manta ray foraging optimization algorithm, as a high-efficiency method for maximizing the microgrid efficiency. After applying the proposed method to a standard microgrid, the simulation results show how effective it is compared with other approaches. Copyright © 2023 by ASME.

Many researchers have optimized the thermo-hydraulic performance of heat exchangers (HE) used in industries. Using passive methods, such as nanofluid, turbulator, and fins can be beneficial in the field. In this paper, two-phase entropy generation and thermo-hydraulic behavior of hybrid nanofluid CuO-diamond/Therminol hybrid nanofluid in a HE equipped with high fins, micro fins, and turbulator are numerically examined. The volume fraction of nanoparticles changes from 0 to 4% and the flow Reynolds numbers (Re) are 14,000, 18,000, 22,000, and 26,000. Two-phase Eulerian-mixture method, k-ω turbulence model, finite volume method, and SIMPLEC algorithm are employed for the simulations. It is concluded that in terms of pressure drop, the use of tubes with micro fins is more favorable. The maximum values of the average Nusselt number (Nu) correspond to the heat exchanger equipped with high fins. The largest change in the average Nusselt number at all Reynolds numbers and volume fractions investigated is 32.67%, which occurs at a Reynolds number of 22,000 and a volume fraction of 4%. From the point of view of the total entropy generation in the presence of a hybrid nanofluid, it is more desirable to use a heat exchanger with micro fins. © 2022

Due to its technical characteristics and economic benefits, the use of the photovoltaic (PV) system has become very common in the world. Nevertheless, the intermittent behavior of solar systems has created challenges for power grids due to their strong dependence on radiation and temperature. Moreover, PV systems must be operated at their maximum power so that they can return the investment costs in less time and also have higher efficiency. As a result, it is crucial to propose new maximum power point tracking (MPPT) methods to track the maximum power in conditions of uncertainty. To cope with this challenge, the fuzzy logic controller (FLC) is very effective approach. This study suggests the FCL for the MPPT algorithm for an isolated PV system using a push-pull converter. The suggested technique is optimized using the improved shuffled frog leaping algorithm (ISFLA) to improve the current MPPT controllers. To deal with the intermittent behavior of solar systems during peak load conditions, Grid is used to respond to the essential load. Fast convergence and low power fluctuations are the advantages of the recommended algorithm in tracing the MPP. The MATLAB/SIMULINK software is used to confirm the effectiveness of the recommended scheme. According to the simulation results, the PV system efficiency is increased to 99.7%. © 2022

Many studies have been conducted to explore the influence of feature selection (FS) techniques on software defect prediction (SDP) models, with conflicting empirical results and research outcomes. These reported contradictions may be due to relative research limitations, such as types of FS techniques or the size of defect datasets. In the instance of FS methods, it was discovered that selecting a suitable threshold value for picking top-ranked features in FS methods might be a cause of discrepancies in reported findings on SDP. Investigating and assessing the impacts of threshold values for the rank-based filter (RBF) FS techniques, as done in this work, becomes critical. 4 RBF (Chi-square, Correlation, Information Gain, and Relief) methods with 5 thresholds (No FS, log2N, Top20%, Top 30%, and Top 50%) values were investigated with 2 prediction models (Naïve Bayes (NB) and Decision Tree (DT)) on 25 software defects datasets. The experimented RBF techniques were selected based on distinct computational features to assure heterogeneity, as well as their performance in the current SDP research. Developed SDP models were evaluated using accuracy and area under the curve (AUC) values while the Scott-KnottESD rank statistical test technique was employed to rank experimented RBF methods with applied threshold values. According to the experimental results, selecting the Top20% of top-ranked features in RBF methods had a greater (positive) impact on the prediction performances of SDP models than other applied threshold values. Furthermore, the outcomes of this study corroborate previous research on the capacity of FS techniques to improve the prediction efficacies of SDP models. Consequently, we urge that FS methods be utilized in SDP tasks. In the case of RBF methods, the Top20% threshold value should be used since it outperforms de-factor log2N and other threshold values. Moreover, findings from this study can be a guide to subsequent SDP studies and further strengthen the tenacity of experimental findings and conclusions in SDP studies. © School of Engineering, Taylor's University.

Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions. © 2022 by the authors.

With recent developments in power electronics devices, today there is increasing interest in renewable energies at the level of distribution networks and close to consumers. However, it is obvious that with the high integration of such resources in the form of distributed generation (DGs), the operation and management of distribution networks will face many challenges. Therefore, it is required to design optimal management systems to properly operate active distribution networks. Proper operation of distribution networks with high integration of DGs is established in the concept of microgrids. This study recommends a day-ahead operation for a microgrid including renewable-based sources such as photovoltaic (PV) and storage systems. Microgrid scheduling has been evaluated in different weather conditions and various output power for the PV system. In order to perform different evaluations, four dissimilar days from the four seasons of the year have been selected to evaluate the amount of radiation of the solar system based on this information. The objective function considered in this work is a single-objective optimization to minimize the total costs of the microgrid. To solve the problem formulation, the modified manta ray foraging optimization (MMRFO) algorithm is applied. To confirm the superiority of the suggested technique, the results of optimization are compared with conventional approaches. © 2022 Elsevier Ltd