Scopus Research — Hassan Muwafaq Gheni hemadi

The effective integration of photovoltaic (PV) systems with battery storage is essential for advancing sustainable energy adoption yet translating forecasts into adaptive and interpretable control remains a key challenge. This study introduces a SHapley Additive exPlanations–Guided Energy Management System (SHAP-EMS) that directly embeds model interpretability into real-time control for residential solar-battery systems. A hybrid Linear Regression–eXtreme Gradient Boost (LR-XGBoost) model provides one-hour-ahead PV forecasts, while a SHAP-weighted rule-based controller dynamically adjusts decision priorities based on feature importance, system state, and temporal interactions. Results demonstrate that SHAP-EMS achieved an 18.3% reduction in peak grid imports (63.2% to 44.9%), a 4.5% decrease in total imports compared with Mixed-Integer Linear Programming optimization, and consistently high self-consumption ratios under polycrystalline PV conditions. By efficiently adapting to temperature fluctuations and generation variability, the framework illustrates how SHAP values can be leveraged to transform black-box forecasts into transparent, computationally efficient, and adaptive control strategies, establishing a novel paradigm for explainable energy management. © 2025 The Author(s)

Big Data Analytics are said to help in transforming huge amounts of raw data towards valuable information that can be used, but there are formidable challenges in feature selection and classification due to the complexity and high dimensionality of the data. Traditional methods are usually too weak to handle the built-in uncertainty, imprecision, and inconsistency within big data and they often fail to perform well. This paper aims to induce the new methodology on these problems using the sets of neutrosophic in dealing with more flexible and nuanced data analysis. The key contributions to the current approach proposed are threefold. First, generalization of the classical set through extension of the notions of truth, indeterminacy, and falsity by allowing representations of uncertainty in data. The second combines a powerful process for selecting features based upon neutrosophic set theory that is optimal by genetic algorithms and advances a step further by applying these features in training and validating the classification models across a set of different domains. Therefore, the major aim from this study is to increase accuracy and reliability in feature selection and classification in big data analytics. This methodology has been implemented and tested over datasets of the following types: healthcare, finance, social media, and more. Results have proved great improvement against conventional performance metrics, for example, the classification accuracy with an SVM classifier over the Cleveland Heart Disease dataset increases from 83.5% to 87.2%, and of a Random Forest classifier over a financial dataset from 76.4% to 81.9%. For instance, the accuracy of social media sentiment analysis changed to 82.7% from 78.3%. All these findings establish that the neutrosophic set-based method holds good advantages in addressing the limitations of classical alternatives. The proposed approach of neutrosophism, through an explicit model, enhances performances in classifications and, at the same time, augments overall robustness and reliability in big data analytic. The importance of this study lies in establishing the groundwork for further research and practical applications, thus indicating possible further development in this field. © 2025, American Scientific Publishing Group (ASPG). All rights reserved.

Serum proteomics needs well biomarker discovery along with improved disease diagnosis methods because proteins show natural expression variation combined with imprecise measurement techniques. The standard analytical threshold determination methods neglect proteomic dataset uncertainties that produce incorrect decision outcomes with substantial numbers of false positives and false negatives. Neutrosophic logic serves as the proposed solution in this study to handle uncertainty by enabling the evaluation of truth and indeterminacy and falsity degrees. The proposed research approach applies Neutrosophic set modeling techniques for protein concentration analysis and includes receiver operating characteristic (ROC) curve threshold optimization and entropy-based uncertainty evaluation. Two case studies are introduced, the first study analyzed protein mass spectrometry data identification, and the second investigated into cancer biomarker detection thresholds using Neutrosophic Logic along with principal component analysis (PCA). With n=500 patients from the breast and lung cancer groups and controls our Neutrosophic Logic system performed better than standard ROC-based thresholds by demonstrating 7% higher sensitivity (92% compared to 85%) and an 8% increase in specificity (88% to 80%) which was confirmed statistically significant with p<0.05 through t-test. Model quantification of uncertainty at less than 15% percent allowed reduced errors from incorrect classification of proteins at borderline threshold levels. Analysis threshold optimization in serum proteomics may experience revolutionary changes through Neutrosophic Logic which leads to enhanced diagnostic instruments for clinical applications. © This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/

Oxidative stress has been identified as a potent factor in the pathogenesis of human diseases ranging from the neurodegenerative and cardiovascular diseases. Biomarkers of oxidative stress are complicated to measure due to biological variability, limitations of the measurements. Traditional spectrophotometric and deterministic models tend to ignore experimental errors giving unreliable diagnostic results. This work presents a new numeric framework in Neutrosophic form for overcoming these challenges. A quantitative model is formed to assess three biomarkers, namely malondialdehyde (MDA), superoxide dismutase (SOD), and catalase, based on Neutrosophic measures that consider indeterminacy and impreciseness of data in experiments. The presented method embeds weighted sum calculations and confidence intervals for the quantification of oxidative stress levels in a robust way. Two case studies are conducted: The first one measures neurodegenerative diseases and the second one measures cardiovascular risk in the patients with metabolic syndrome. The findings suggest major improvements over existing spectrophotometric methods and deterministic statistical models (linear regression analysis). According to Katzman (1993) criteria, as well as Alberti et al. (2006) rules, the proposed neutrosophic model performed better when applied to clinical datasets containing 50 metabolic syndrome patients and 100 neurodegenerative patients with Alzheimer’s or Parkinson’s. The proposed neutrosophic framework achieved superior performance with mean squared error (MSE) of 0.0091 (neurodegenerative) and 0.0129 (cardiovascular) compared to 0.0153 and 0.0214 from conventional methods, along with higher correlation coefficients (R²) of 0.8198 and 0.7494 versus 0.7021 and 0.6532. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/

Stability and operational performance in uncertain and time-varying nonlinear dynamic systems, such as robotic manipulators and autonomous vehicles, is an important problem in modern nonlinear dynamical systems. This study presents a hybrid model that integrates Long Short-Term Memory (LSTM)-based artificial intelligence (AI) to identify adaptive system changes with Model Predictive Control (MPC), provided with the help of an autoencoder that leads to dimensionality reduction. The proposed AI-MPC system offers conditional stability and real-time feasibility (guaranteed by a broad Lyapunov-based theoretical framework with a clear definition of AI in the stability guarantees). Among the contributions to research are scalable and computationally efficient control architecture that adaptively models complex system dynamics, minimizes control computation time, and remains robust to disturbances and uncertainties. It is also known that the implemented framework can be useful to a wide range of simulated and experimental case studies such as robotic arms and autonomous vehicle platforms, the framework has offered improved tracking performances with a root mean square error (RMSE) by more than 58 times compared to conventional MPC and PID controllers, uses less energy by up to 50 times less and disturbances with an order of magnitude (more than 20 times) reduced. The control iterations use 30-60 milliseconds that is why it is better to use it in real time. The extension of this AI-MPC system is that the control systems can be predictive in an unpredictable and nonlinear environment that may represent the interface to resource-constrained and safety-critical systems. © 2025, Department of Agribusiness, Universitas Muhammadiyah Yogyakarta. All rights reserved.

Worldwide, cardiovascular disease is the leading cause of death. Based on clinical data, a Machine Learning (ML) system can detect cardiac disease in its early stages, which enables a reduction in mortality rates. However, imbalanced and high dimensionality data have been a persistent challenge in ML, impeding accurate predictive data analysis in many real-world applications, such as the detection of cardiovascular disease. To address this, computational methods targeting heart disease detection have been developed. However, their performance is still inadequate. Hence, this study presents a new stack predictor for the heart disease model (termed SPFHD). SPFHD employs five common tree-based ensemble learning algorithms as base models for heart disease detection. In addition, the predictions from the base models are integrated using a support vector machine algorithm to enhance the accuracy of heart disease detection. A new conditional variational autoencoder (CVAE) based method is developed to overcome the imbalance issue, which performs better than the conventional balancing methods. Finally, the SPFHD model is tuned by Bayesian optimization. The results show that the proposed SPFHD model outperforms the state-of-art methods over four datasets achieving higher f1-score of 4.68 %, 4.55 %, 2 %, and 1 % for HD clinical, Z-Alizadeh Sani, Statlog, and Cleveland, respectively. Moreover, this new framework offers vital interpretations which assist in understanding model success by leveraging the powerful SHapley Additive explanation (SHAP) algorithm. This highlights the most significant attributes for detecting heart disease and overcoming the limitations of current 'Black-box' methods that cannot reveal causal relationships between features. © 2023 Elsevier Ltd

The Internet of Vehicles (IoV) emerges as a pivotal extension of the Internet of Things (IoT), specifically geared towards transforming the automotive landscape. In this evolving ecosystem, the demand for a seamless end-to-end system becomes paramount for enhancing operational efficiency and safety. Hence, this study introduces an innovative method for real-time driver identification by integrating cloud computing with deep learning. Utilizing the integrated capabilities of Google Cloud, Thingsboard, and Apache Kafka, the developed solution tailored for IoV technology is adept at managing real-time data collection, processing, prediction, and visualization, with resilience against sensor data anomalies. Also, this research suggests an appropriate method for driver identification by utilizing a combination of Convolutional Neural Networks (CNN) and multi-head self-attention in the proposed approach. The proposed model is validated on two datasets: Security and collected. Moreover, the results show that the proposed model surpassed the previous works by achieving an accuracy and F1 score of 99.95%. Even when challenged with data anomalies, this model maintains a high accuracy of 96.2%. By achieving accurate driver identification results, the proposed end-to-end IoV system can aid in optimizing fleet management, vehicle security, personalized driving experiences, insurance, and risk assessment. This emphasizes its potential for road safety and managing transportation more effectively. © 2024 The Authors

Medical image processing is indispensable for correct diagnosis and planning of treatment. However, it is susceptible to many errors due to noise, artifacts, and the variability innate in anatomical structures themselves. Traditional image analysis methods hence suffer from these complexities in the images themselves and lead to probable inaccuracies in image analysis. This paper probes into the role of neutrosophic logic in the domain of medical image processing to seek better handling of these problems. The main objectives of the work were to optimize the noise reduction, image segmentation, feature extraction, and classification using the special capabilities of neutrosophic logic directed toward handling uncertainty and indeterminacy. Contributions The contributions of this study are multifaceted: it contributes by introducing detailed support for applying neutrosophic logic in a number of medical image processing tasks and integrates neutrosophic logic with prior techniques and evaluates their performance with traditional methods. The experimental results in the study are complete and demonstrate significant improvements in key metrics. For example, applying neutrosophic logic in noise reduction increased the peak signal-to-noise ratio of MRI images from 25 dB to 35 dB. In some segmentation tasks, the Dice coefficient for liver CT scans increased from 0.85 to 0.92. It increases the accuracy of feature extraction in breast cancer detection from 88% to 95%, while integrating neutrosophic logic with convolutional neural networks improves the accuracy in retinal image classification from 92% to 97%. All these results underline the strong role that neutrosophic logic can play in enhancing accuracy, robustness, and reliability in the processing of medical images. The result of the study concludes that neutrosophic logic not only improves the current limitations but also holds great promise for handling uncertainty in many medical fields, opening a promising way for future advancements in the field of medical imaging and health applications. © 2024, American Scientific Publishing Group (ASPG). All rights reserved.

Intelligent Transportation Systems (ITS) have extensively utilized driver behavior monitoring systems to mitigate the risk of traffic accidents caused by factors such as aggression and distraction. However, existing methods often rely on computer vision techniques, raising concerns about privacy violations and vulnerability to spoofing attacks. These attacks can potentially result in inaccurate analysis of driver behavior and compromise the effectiveness of the system. To mitigate this issue, the proposed system relies on in-vehicle sensors and the driving signal obtained from the CAN-BUS, which provide direct and reliable measurements of driver behavior. By analyzing real-time data collected from multiple drivers, the hybrid deep learning model is trained to recognize patterns and characteristics indicative of safe and unsafe driving behavior. The driving signal obtained from the Controller Area Network bus (CAN-BUS), including acceleration, RPM, speed, accelerator pedal value, and throttle position signal, etc., is utilized to recognize safe and unsafe driver behavior. The utilization of a hybrid deep learning model, which combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), is a deliberate choice in order to harness the respective advantages of both methods. This decision is driven by the aim to overcome the challenges encountered by previous approaches by capitalizing on the strengths of CNN and LSTM. The model is trained and tested on a real-time dataset collected from multiple drivers. Experimental results demonstrate the effectiveness of the proposed method in accurately detecting driver behavior, addressing the public health concern of traffic accidents. © 2024 International Information and Engineering Technology Association. All rights reserved.

Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time. © The Author 2022. Published by ARDA.

Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT). To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

COVID-19 gains from the research and technology component's establishment of information science, artificial intelligence, and computer understanding. The article aims to discuss the numerous facets of today's modern technology utilized to combat COVID-19 emergencies on various scales, such as medicinal picture handling, illness tracking, expected outcomes, computational science, and medications. Techniques: A complex search of the knowledge base associated with existing COVID-19 innovation is conducted. Furthermore, a concise survey of the excluded data is conducted, analyzing the various aspects of current developments for dealing with the COVID-19 pandemic. The below are the outcomes: We have a window of musings on the audit of the tech propellers used to mitigate and mask the significant impact of the upheaval. Even though several investigations into current innovation in COVID-19 have surfaced, there are still required implementations and contributions of innovation in this war. Consequently, a thorough presentation of the available data is given, and several modern technology implementations for combating the pandemic of COVID-19. Continuous advancements of advanced technologies have aided in improving the public's lives, and there is a strong belief that proven study plans utilizing AI would be of great benefit in assisting people in combating this infection. © 2021

In elastic mobile cloud computing (EMCC), mobile devices migrate some computing tasks to the cloud for execution according to current needs and seamlessly and transparently use cloud resources to enhance their functions. First, based on the summary of existing EMCC schemes, a generic EMCC framework is abstracted; it is pointed out that the migration of sensitive modules in the EMCC program can bring security risks such as privacy leakage and information flow hijacking to EMCC; then, a generic framework of elastic mobile cloud computing that incorporates risk management is designed, which regards security risks as a cost of EMCC and ensures that the use of EMCC is. Finally, it is pointed out that the difficulty of risk management lies in risk quantification and sensitive module labeling. In this regard, risk quantification algorithms are designed, an automatic annotation tool for sensitive modules of Android programs is implemented, and the accuracy of the automatic annotation is demonstrated through experiments. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Due to the poor accessibility, poor operating conditions, high failure rate, long maintenance time, and difficult maintenance of wind hybrid generators, the economic loss is huge once the failure stops. To this end, the fault adaptive fault-tolerant control of distributed wind and wind hybrid generators is studied, the historical operation data of offshore wind and wind hybrid generators and onshore wind and wind hybrid generators are counted and compared, and the fault characteristics of key components of offshore wind and wind hybrid generators are analyzed. The generator sets are summarized, and the common electrical faults of wind turbines and their impacts on the system are analyzed. This paper summarizes the current research status of fault-tolerant operation of existing offshore wind and wind complementary generators in terms of software fault tolerance and hardware fault tolerance, summarizes the current fault tolerance schemes for offshore wind and wind complementary generators, and analyzes the application feasibility of existing fault tolerance schemes. In addition, the main problems of fault-tolerant offshore wind and solar complementary generator sets are pointed out, and future research hotspots are foreseen. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Protecting electronic documents, especially those containing sensitive data, is a major challenge in an open web. The data security industry has long struggled to manage the security of e-books, data shows that information security issues can cause significant economic losses after three years Although cryptographic methods have been proposed as a solution in of these challenges Focusing on speed and efficiency The shortcomings of traditional encryption methods have been thoroughly examined Although a number of network management techniques assure retention privacy and integrity though, when it comes to encryption. Digital signature algorithms, although effective in detecting unauthorized changes and limiting the scope for copyright protection, do not ensure the confidentiality of shared electronic documents When addressing research gaps addressing this issue, the paper proposes a security framework for electronic documents that combines three important security mechanisms: encryption, digital signatures, and watermark algorithms Dhishu By matching their strengths, constraints are overcome. The combination of encryption and digital signatures is explored as a promising approach to protecting electronic documents, ensuring authenticity and confidentiality Importantly, the need to explore security mechanisms such as digital is highlighted emphasis on handwriting, encryption, and watermarking systems in depth. © The Author 2023.

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayesian classifier (NBC) have been enhanced as compared to the dataset before applying the proposed method. Moreover, the results indicated that issa was performed better than the statistical imputation techniques such as deleting the samples with missing values, replacing the missing values with zeros, mean, or random values. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

This survey talks about the problem of security and privacy in the blockchain ecosystem which is currently a hot issue in the blockchain community. The survey intended to study this problem by considering different types of attacks in the blockchain network with respect to algorithms presented. After a preliminary literature review it seems that some focus has been given to study the first use case while, to the best of my knowledge, the second use case requires more attention when blockchain is applied to study it. The research is also interested in exploring the link between these two use cases to study the overall data ownership preserving accountable system which will be a novel contribution of this work. However, due to the subsequent government mandated secrecy around the implementation of data encryption standard (DES), and the distrust of the academic community because of this, a movement was spawned that put a premium on individual privacy and decentralized control. This movement brought together the top minds in encryption and spawned the technology we know of as blockchain today. This survey explores the genesis of encryption, its early adoption, and the government meddling which eventually spawned a movement which gave birth to the ideas behind blockchain © This is an open access article under the CC BY-SA license

Novel coronavirus infection (COVID-19) has severely threatened public health. Frequent hospital visits are required for COVID-19 therapy and monitoring, which raises the load on hospitals and patients. Current advancements in wearable sensors and communication protocols are contributing to improving the healthcare system and will soon transform healthcare delivery. Future patient monitoring and health information delivery systems will be profoundly impacted by current and upcoming communication breakthroughs and microelectronics and embedded systems technology improvements. Bandwidth constraints, power consumption, and skin or tissue protection are significant obstacles. This study offers a comprehensive analysis of wireless body area networks, details their use in the COVID-19 treatment process, and proposes a paradigm for integrating body area networks into telemedicine systems. This study also addresses current developments, the WBAN-telemedicine system, and the scope of future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In this paper, the proposed system to control the robotic by using an adaptive controller and using the artificial neural network with optimized PID by using the PSO is used to regulate the motion of a robot. The problems of reverse kinematic is solved by using a proposed method which is important to determine the robotic arm joints angle values, when traced in a different path. The D-H approach Devavit-Hartenberg can be used to solve the Forward Kinematics problem. The dynamic model was computedusing the Lagrange model, which is a mathematical model. Computing a dynamic model was a crucial step in designing a robotics control. The proposed adaptive controller, which is based on a PSO-optimized artificial neural network, is utilized to increase system response. In this paper designing a GUI by using MATLAB to compute the inverse and forward kinematics and to compute the trajectory planning. Derived the inverse kinematic and the forward kinematic by traditional methods is complicated, by applying the proposed method is an easier and fast way. The main problem in the dynamic model was the non-linearity, so by using the proposed method, this method will selects optimal parameters of the PID controller that overcome the plant non-linearity. The performance of the system show good response when using the proposed method. Rom the simulation results the overshot approaches to the zero the raising time reduced to 0.11 for the first joint1, the best settling time is 0.24 in the first joint, in joint 3 the delay time was reduced to the 0.1. Based on these findings, the suggested method outperforms other standard methods such as PID controller in terms of system performance. © 2023 Author(s).

Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient's severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient's current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy. © 2013 IEEE.

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively. © 2022 by the authors.

Since the deregulation of the power markets, accurate short term Electricity Price Forecasting (EPF) has become crucial in maximizing economic benefits and mitigating power market risks. Due to the challenging characteristics of electricity price, which comprise high volatility, rapid spike, and seasonality, developing robust machine learning prediction tools becomes cumbersome. This work proposes a new hybrid machine learning method for a day-ahead EPF, which involves linear regression Automatic Relevance Determination (ARD) and ensemble bagging Extra Tree Regression (ETR) models. Considering that each model of EPF has its own strengths and weaknesses, combining several models gives more accurate predictions and overcomes the limitations of an individual model. Therefore, the linear ARD model is applied because it can efficiently deal with trend and seasonality variations; on the other hand, the ensemble ETR model is employed to learn from interactions, and thus combining ARD with ETR produces robust forecasting outcomes. The effectiveness of the proposed method was validated using a data set from the Nord Pool electricity market. The proposed model is compared with other models to demonstrate its superiority using performance matrices, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiment results show that the proposed method achieves lower forecasting errors than other individual and hybrid models. Additionally, a comparative study has been performed against previous works, where forecasting measurement of the proposed method outperforms previous works' accuracy in forecasting electricity price. © 2013 IEEE.

Heart disease is the leading cause of death worldwide. A Machine Learning (ML) system can detect heart disease in the early stages to mitigate mortality rates based on clinical data. However, the class imbalance and high dimensionality issues have been a persistent challenge in ML, preventing accurate predictive data analysis in many real-world applications, including heart disease detection. In this regard, this work proposes a new method to address these issues and improve the predict the presence of heart disease and patients' survival, including supervised infinite feature selection (Inf-FSs) to find the most significant features and Improved Weighted Random Forest (IWRF) to predict heart disease, and Bayesian optimization to tune the new hyperparameters for IWRF. Two public datasets, including Statlog and heart disease clinical records, were used to develop and validate the proposed model. The proposed model is compared with other hybrid models to show its superiority using performance metrics like accuracy and f-measure to evaluate the models' performance. The results have shown that the proposed Inf-FSs-IWRF achieved better results than other models in attaining higher accuracy and F-measure on both datasets. Additionally, a comparative study has been performed to compare with previous studies, where the proposed model outperformed the others by an accuracy improvement of 2.4% and 4.6% on both datasets, respectively. © 2013 IEEE.

The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to −10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis. © 2022 by the authors.

As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients' fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks. © 2022 Israa Al-Barazanchi et al.

Many applications, such as interactive data analysis and sign detection, can benefit from hand gesture recognition. We offer a low-cost approach based on human-computer interaction for predicting hand movements in real time. Our technique involves using a color glove to train a random forest classifier and then predicting a naked hand at the pixel level. Our algorithm anticipates all pixels at a rate of around 3 frames per second and is unaffected by differences in the surroundings. It's also been proven that HCI-based data augmentation is more effective than any other way for enhancing interactive data. In addition, the augmentation experiment was carried out on multiple subsets of the original hand skeleton sequence dataset, each with a different number of classes, as well as on the entire dataset. On practically all subsets, the proposed base architecture improved classification accuracy. When the entire dataset was used, there was even a modest improvement. Correct identification could be regarded as a quality indicator. The best accuracy score was 94.02 percent for the HCI-model with support vector machine (SVM) classifier. © 2022 The Author. This work is licensed under a Creative Commons Attribution License. All Rights Reserved.

Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS; however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS. © 2022 Zahraa A. Jaaz et al., published by De Gruyter.

Several years ago, an unknown person / group proposed the coined term Bitcoin, described how the innovation of the blockchain, a peer-to-peer correlated structure, could be used to shed light on the issue of maintaining order of exchanges and maintaining a strategic distance from the issue of double spending. Bitcoin orders are exchanged and combined into a structure of limited size called squares that share the same timestamp. Arrangement axes (miners) are keen to connect the pieces to each other in chronological order, as each block has a hash of the previous square to form the blockchain. In this way, the blockchain architecture oversees the containment of a robust and auditable registry for all exchanges. The objective of this paper is to investigate how blockchain innovation can be utilized in digital payment of drug purchase supply chains. we chose this zone to center on, since it is exceptionally dependent on believe, contracts, arrangements, overseeing, human interaction and installments through a third party. Item forging, generation and dissemination issues, robberies and fraudulent drugs cause multi-billion-dollar income misfortunes within the world and posture a serious threat to open wellbeing Blockchain innovation can tremendously move forward execution in all these zones and diminish the chance of the issues. © 2022 IEEE.

This study illustrates the role of artificial intelligence in enhancing administrative decision support systems by depend on knowledge management. As per new technologies are evolving and the workflow need more concious approach of implementation, thus the role of artificial intelligence is evolved in support to decision making. The study takes privates college administration as a varible on which the results rely. The upgrades in innovation have upgraded most techniques for leading business tasks that further develop organizations and administration conveyance. Companies in this area need to wander into digitizing of all industry cycles, business sequences linked to administration and more essential services in educational institutes over time. The need for a proper decision-making support using knowledge management stills create a big gap in the foundation of an effective and efficient eductaional system for the good governance and to improve the image of some institute. The examination interaction has been intended to follow an iterative methodology of information revelation chose for the review. Using the statistical package for social sciences (IBM-SPSS) version 23 logic instrument, the illustrative research was completed with insights into the segment profile of the respondents. Hayes' process macro v3.3 with SPSS was used to analyze the interceding effect. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

The Wireless body area network (WBAN) is now and again referred to as the physique place sensor community (BASN). It is a conversation community targeted at the human physique that uses community aspects linked with the human physique to notice and acquire imperative physiological data. As an essential branch of wireless sensor networks, wireless body area networks have received much attention in various fields because of their portable and movable characteristics. However, the problem of energy consumption is becoming more and more prominent due to the difficulty of recharging or replacing nodes. Based on the structure and characteristics of wireless body area networks, we analyze and summarize the energy-saving strategies from the physical layer, MAC layer, and network layer based on the existing research. Finally, some research ideas are proposed according to the application requirements. © 2022 IEEE.

This paper researches and designs an automatic charging control and monitoring system for PV electric vehicle batteries, which can automatically charge the low-voltage and high-voltage batteries by obtaining the operation mode of PV electric vehicles, avoiding battery maintenance to the maximum extent, preventing damage to the battery cell caused by over-discharge, and increasing the range of the whole vehicle. Suppose the PV EV is in parking mode. In that case, it will obtain the power of the low-voltage battery in real-Time and charge the low-voltage battery according to the request, and then it will obtain the power of the high-voltage battery and send a power-up request to charge the high-voltage battery according to the request; if the PV EV is in driving mode, it will obtain the power of the low-voltage battery in real-Time and generate a power-up request according to the power-up request. When the PV electric vehicle is in driving mode, it will obtain the low voltage battery power condition in real-Time and send a request to recharge the low voltage battery. © 2022 IEEE.

COVID-19 is unquestionably one of the most hazardous health issues of our century, and it is a significant cause of mortality for both men and women throughout the globe. Even with the most advanced pharmacological and technical innovations, cancer oncologists, and biologists still have a substantial problem treating COVID-19. For patients with COVID-19, it is critical to offer initial, precise, and effective indicative procedures to increase their survival and minimize morbidity and mortality, which is currently lacking. A COVID-19 detection method has been presented in this paper for the initial identification of COVID-19 hazard factors. Features from accelerated segment test (FAST), a robust feature was used to extract features in this suggested method. The experiments show that it is possible to identify FAST traits efficiently. A consequence was a high success rate (98%) for accuracy performance. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

The smart Internet of Things-based system suggested in this research intends to increase network and application accuracy by controlling and monitoring the network. This is a deep learning network. The invisible layer's structure permits it to learn more. Improved quality of service supplied by each sensor node thanks to element-modified deep learning and network buffer capacity management. A customized deep learning technique can be used to train a system that can focus better on tasks. The researchers were able to implement wireless sensor calculations with 98.68 percent precision and the fastest execution time. With a sensor-based system and a short execution time, this article detects and classifies the proxy with 99.21 percent accuracy. However, we were able to accurately detect and classify intrusions and real-time proxy types in this study, which is a significant improvement over previous research. © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal.

Cloud services are the cutting edge technology, however the growing demand for the internet of things has certain limitations which are high latency expectation and high cost of cloud resources, and this is caused by long-distance between application and cloud. Fog computing is a distributed extension of the cloud, which provide storage and computation at the network level. It consists of an internet of things (IoT) application, a fog control node, and a fog access node. This research works towards minimizing the cloud cost in scheduling. For this purpose, a cost-effective task and user scheduling algorithm are performed. The first task scheduling model is composed based on composers' roles after that task scheduling algorithm is performed to handle the various task at the fog access node in an optimized manner. Finally, the reallocation mechanism reduces the time and service delay. For the analysis purpose extensive simulation is carried out and performance statistics were compared with other existing algorithms. It was observed that the proposed algorithm provides highly cost-optimized user and task scheduling with better performance statistics and reduces the delay in the task by providing optimization in the concurrent task at the fog node. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Character recognition is one of the most rapidly developing areas of computer technology. People have the greatest ability to identify any image or item. People can quickly understand the hand transcription. The vast wide variety writer handwriting makes selecting suitable component sets much more difficult, and this has been extensively researched in the context of handwriting recognition. While encouraging, the current findings have many disadvantages, including computation time, dependency on the used algorithms, and uncertainty assessing function interfaces. Many areas, such as medical imaging, science, and archaeology, rely heavily on handwriting. Forensic medicine, for instance, may deduce details from handwriting, such as age bracket and hand used in certain instances. Because of the rapid growth of handheld devices, virtual textbooks, and specialized communication devices, it has gotten a lot of attention recently. Human-machine interaction has typically concentrated on keyboards and targeting tools. For the deep learning networks training process, we construct a dataset of our own handwriting, which contains 2200 illustrations of each alphabet and is combined with another readily viewable database. Multidimensional Long Short-Term Memory networks are heavily used in emerging state-of-the-art strategies to fine Handwritten Text Classification. However, these frameworks have a significant limitation, and we find that they remove feature vectors that are close to those extracted by convolution layer, which are computational complexity less costly. © 2022 IEEE.

In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management and efficient time series for casting of nuclear reactions is a problem to be addressed if nuclear power is to deliver a major part of our energy consumption. This problem explains how SI processing techniques can be used to automate accurate nuclear reaction forecasting. The goal of the study was to use swarm analysis to understand patterns and reactions in the dataset while forecasting nuclear reactions using swarm intelligence. The results obtained by training the SI algorithm for longer periods of time for predicting the efficient time series events of nuclear reactions with 94.58 percent accuracy, which is higher than the deep convolution neural networks (DCNNs) 93% accuracy for all predictions, such as the number of active reactions, to see how the results can improve. Our earliest research focused on determining the best settings and preprocessing for working with a certain nuclear reaction, such as fusion and fusion task: forecasting the time series as the reactions took 0-500 ticks being trained on 300 epochs. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

The aim of this paper is to integrate security and privacy in mmWave communications. MmWave communication mechanism access three major key components of secure communication (SC) operations. proposed design for mmWave communication facilitates the detection of the primary signal in physical (PHY) layer to find the spectrum throughput for primary user (PU) and secondary user (SU). The throughput of SC for PU with maximum throughput being recorded at 0.7934 while maximum throughput for SU is recorded at 0.7679. So, we will design a mmWave communication mechanism for solving this problem. The probability for sensing where the probability of detection (PD) is predicted at a defined range of 690 km with an estimated accuracy of 83.56% while the probability of false alarm (PFA) is predicted at a defined range of 230 km with an estimated accuracy of 81.39%. This conflicting but interrelated issue is investigated over three stages for the purpose of solving with a cross-layer model with MAC and PHY layers for a secure communication network (SCN) while reducing the collision effect concurrently with a 92.76% for both cross-layers. MATLAB 2019b would be forwarded in use as the increasing demand for augmenting the bandwidth in secure communications has actuated the evolutionary technology. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

Telemedicine platforms have emerged as one of the most harnessed studies in recent years, especially after the spread of pandemics. Due to the epidemic, telemedicine distribution to rural or isolated areas has become an urgent need. However, there are several obstacles to providing remote medical care, the most significant of which is protecting patient data while sustaining the speed of communication between the patient and the medical staff. The significant of this study is to provides a lightweight encryption/decryption technique that uses on the internet of medical things in stance of bio-sensors and bio-actuators. This study is one of the cybersecurity approaches, such type of encryption has little effect on the speed of data transmission. The Diffie Hellman technique is used as a lightweight encryption method because it includes four encryption-keys. The efficiency of the proposed encryption method has been compared with several equivalent methods. According to experimental results, the proposed encryption method represents a lightweight and secure method which can accomplish the level of protection that required to secure medical information despite the data’s disarray. © This is an open access article under the CC BY-SA license

The Traveling Salesman Problem is a story application of Atom Swarm Optimizations in this research. We have developed several novel techniques intended for solving TSP with PSO. Additionally, we introduced the notions to Swap Operative and Swap Chronological sequence and redefining the remaining operatives their foundation; that way, the study created unique PSO. Research prove it can produce satisfactory outcomes. The aim of this paper be there to assess the functioning of particle swarm optimization, for the going salesman issue TSP. The solution to this trouble is common to be NP-hard, it has N permutations. The study's goal is to examine the capacity of both algorithms to solve intercontinental and other benchmark problems. Overall, the results suggest that used algorithms know how to understand good quality explanations than PSO algorithm, but they are not good enough in terms of normal generation. © 2022 IEEE.

There is an increase in motorcycles traffic accidents, while the cause for such accidents has always been associated with aggressive driving behaviors. There has been considerable research attention on how to deal with such driving behavior that causes severe and fatal accidents from the academic perspective; these research works addressed technical, scientific, and social issues. This study systematically searches, reviews, and analyzes the literature associated with motorcycle accidents and driving behaviors. Between the years 2014 and 2021, the next four databases have been searched: ScienceDirect, Scopus, Web of Science, and IEEE Xplore. A total of 108 people were picked depending on certain inclusion and exclusion criteria. Approximately 68% (n=79/108) of the researchers looked at the challenges from a social science perspective, whereas 25% (n=26/108) concentrated on experimental research variables. Only 7% (n =3/108) explored the development of Apps & systems. Finally, our contribution comprehensively analyses most of the articles by highlighting challenges associated with motorcycle behavior, motivations, and recommendations. In addition, provide potential research gaps in current studies that require further investigation. © 2022 Little Lion Scientific. All rights reserved.

Regardless of new and emerging technologies and developments for vehicle safety, worldwide fatalities in car accidents have been on the rise in recent years, largely attributable to driver mistakes. As a result, a dependable real-time warning system capable of alerting drivers to a collision is necessary. V2C (Vehicle-to-Cloud) is a vast field of active research and development that has transformed the driving experience. Driver behavior is the topic of extensive work, focusing on the link between speeding and collisions, as drivers who speed often and excessively are more likely to be involved in crashes. To achieve this, in the framework of V2C, violation system-based driving behaviors have been developed. It was created with real-time V2C hardware for data collecting. The driver behavior in an urban context is acquired through an instrumented vehicle's OBD-II adapter and GPS data logger and is examined in different periods. By estimating the Vehicle's speed. Vehicle data (speed and location) will be transmitted to a cloud computing server (Things board) via an ESP8266 Wi-Fi network for speed analysis. The proposed system could email the driver who exceeded the street speed limit once a cloud computing server detects speed conditions. The driver's license will be revoked if they fail to respond to the email and frequently violate the road's speed limit. © 2022 IEEE.

Numerous academic studies and government publications concur that careless driving behaviors on the road are the primary contributor to accidents. Therefore, studying driver behavior is seen as being of great use in the design and development of Intelligent Transportation Systems (ITS). The rising number of traffic accidents is ascribed to irresponsible driving, inadequate preventative measures, as well as dependence on sluggish and ineffective support systems. There are multiple smartphone applications available to help drivers become better drivers. Recently, vehicles have gained access to the computational capacity of cloud servers to run machine learning (ML) algorithms owing to mobile cloud computing. In order to address issues that have not yet been addressed by previous studies, this study intends to analyze and carefully examine the literature in the driver behavior-based Internet of Vehicles (IoV) domain. Thus, the development of intelligent transportation systems' crucial behavior analysis is covered. Sensors utilized in investigations, the impact of thresholds on labelling procedures, data balance and classification accuracy, and the thresholds in recognizing driving styles are among the topics covered in the examined studies. © 2022 IEEE.

Due to the rapid development of information technology and network technology, personal medical data information is increasingly challenged by privacy breaches and security issues at all life cycle stages. In this paper, we analyze the risk patterns of privacy information leakage of personal medical data in the comprehensive lifecycle of medical information in the context of the importance of hospital information system construction and discuss the risk factors of medical data privacy security in hospital information system based on the installation of the vulnerability scanning system, establishment of perfect access control mechanism, installation of the intrusion detection system (IDS) and data backup. The protection strategy of medical data privacy security under the hospital information system is discussed. The change strategy can physically separate the use and storage of patients personal information and use different algorithms to encrypt privacy information hierarchically. The hospital departments protect the security of patients personal privacy information from the source according to the mechanism of hierarchical grading and on-demand access without affecting the hospital business. © 2022 Institute of Advanced Engineering and Science (IAES).

Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians' workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector's dimension and, as a result, the classifier's complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network's training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Today the main concern for World is energy and power age. By and by, out of around 7 billion populaces, just 65-69% approaches power. Essentially to carry the populaces into the office of power access however much as could be expected inside the restricted assets, we have used the regular assets like sun oriented and wind to satisfy this assumption. Utilizing sun based and wind energy in relationship with the power gadgets, we can supply the power to the buyers inside their capacity and we will want to limit the power issue as could really be expected. Hydrogen Photovoltaic Fuel (HPF) cell is the mix of force gadgets which lessens the major sun-oriented emergency of expenses, where expenses are the enormous issue for non-industrial nations. Presently a-days, the coordinated circuits (IC) are entirely solid and modest, to the point that make the conveying and reversing or changing over components simplest than the massive and expensive instruments utilized in the traditional power supply framework. The examination expects that the lattice joining of the environmentally friendly power assets utilizing HPF inverter might cause a colossal comment in satisfying the absence of force use across the world. Solar energy is a rapidly growing resource, already providing 4.5% of electricity in the World and projected to supply up to 35% by 2050. On the other hand, the default model’s predictions were far from the actual metered HPF data. For renewability, the simulated renewable energy consumption with modified inputs is 3.9% below of actual metered renewable data while the default model’s prediction was more than 52% below actual renewable use. Using PV-HPF hybrid model indices to represent how well a simulated model describes the variability in the measured data; the modified model has achieved accurate renewability results; with a Solar of 10.99 % and Wind of 9.90%, while the hybrid model has a solar of 57.16% and a Wind of 57.20% in renewable energy comparison being performed in MATLAB. © 2022. All rights reserved.

The web speech API has made it possible to integrate audio data into web applications and make it a unique experience for all customers and users of modern applications. The website can only be accessed through devices equipped with a which stands for graphical user interface (GUI) and screen. For this to be done, there must be a physical attraction with such devices. This paper presents speech recognition using a web browser (SRWB) which permits browsing or surfing the internet with the use of a standard voice-only and vocal user interface (VUL) development. The SRWB system input from the users in form of vocal commands and covers these voice commands to HTTP requests. The SRWB system will send the voice commands to the web server for processing purposes and when the processing is done, the converted or translated HTTP response is outputted to the end-users in a voice format made audible with the attached loudspeakers. SAPI, developed by Microsoft, allows the use of SRWB in Windows applications. The algorithm is implemented by the system to achieve its goal for web content, classifying, analyzing, and sending important parts of web pages back to the end-user. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

In recent years the algorithms of machine learning were used for brain signals identification as a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals. The wavelet transform (WT) and the independent component analysis (ICA) were utilized for feature extraction. These algorithms were used to reduce the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize the EEG signals. The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized QNN provided us with somewhat faster and more realistic results. According to simulation results, the total classification for ICA is 82.4 percent, while the total classification for WT is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT. © 2019 Institute of Advanced Engineering and Science.