RSS 1.0Faculty Research and Publications
Recent Submissions
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75th Annual Meeting of the Division of Fluid DynamicsNanosecond repetitively pulsed (NRP) discharges are a promising technique to enhance combustion efficiency and control. Numerical studies are essential to improve the understanding of complex plasma-combustion interaction. Limited by the prohibitive computational cost of fully coupled detailed plasma mechanism and combustion chemistry, a phenomenological model taken from literature is further developed to study the behavior of a laminar methane-air flame under NRP discharges. The phenomenological model focuses on two channels through which the electric energy is deposited: 1) the ultrafast heating and ultrafast dissociation of O2 coming from the relaxation of electronically excited N2; and 2) the slow gas heating coming from the relaxation of vibrational states of N2. The energy fraction deposited to these two channels is governed by the reduced electric field (E/N) which cannot be accurately predicted without resolving ion transport. Electric field is instead determined by solving the static poisson equation between two pin electrodes with three tested geometries. The predicted flame displacement under plasma qualitatively matches the experimental result. The roles played by chemical and thermal effects are strongly dependent on the E/N profile. Higher prediction of E/N magnitude at the preheating zone results in stronger dissociation effect and modifies the flame morphology more than what a lower E/N prediction concludes.
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A phenomenological model for the impact of nanosecond repetitively pulsed discharges on a laminar methane-air flameNanosecond repetitively pulsed (NRP) discharges are a promising technique to enhance combustion efficiency and control. Numerical studies are essential to improve the understanding of complex plasma-combustion interaction. Limited by the prohibitive computational cost of fully coupled detailed plasma mechanism and combustion chemistry, a phenomenological model taken from literature is further developed to study the behavior of a laminar methane-air flame under NRP discharges. The phenomenological model focuses on two channels through which the electric energy is deposited: 1) the ultrafast heating and ultrafast dissociation of O2 coming from the relaxation of electronically excited N2; and 2) the slow gas heating coming from the relaxation of vibrational states of N2. The energy fraction deposited to these two channels is governed by the reduced electric field (E/N) which cannot be accurately predicted without resolving ion transport. Electric field is instead determined by solving the static poisson equation between two pin electrodes with three tested geometries. The predicted flame displacement under plasma qualitatively matches the experimental result. The roles played by chemical and thermal effects are strongly dependent on the E/N profile. Higher prediction of E/N magnitude at the preheating zone results in stronger dissociation effect and modifies the flame morphology more than what a lower E/N prediction concludes.
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Response of one-dimensional ionised layer to oscillatory electric fieldsTo provide fundamental insights into the response of laminar flames to alternating current (AC) electric fields, a simplified one-dimensional model using an ionised layer model is formulated with the conservation equations for the ion species with ionisation, recombination, and transport due to molecular diffusion and electric mobility. A parametric study is conducted to investigate the response of the ion layer at different voltages and oscillation frequencies, and the results are examined mainly in terms of the net current–voltage (I–V) characteristics. As the oscillation frequency is increased, a nonmonotonic response in the I–V curve is seen such that the current may exceed the saturation condition corresponding to the steady DC condition. In general the current reaches a peak as the unsteady time scale becomes comparable to the ion transport time scale, which is dictated by the mobility, and eventually becomes attenuated at higher frequencies to behave like a low-pass filter. The extent of the peak current rise and the cut-off frequency are found to depend on the characteristic time scales of the ion chemistry and mobility-induced transport. The simplified model serves as a framework to characterise the behaviour of complex flames in terms of the dominant ionisation and transport processes. The current overshoot behaviour may also imply that the overall effect of the electric field may be further magnified under the AC conditions, motivating further studies of multi-dimensional flames for the ionic wind effects.
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Comparison Between Different MPPT Methods Applied to a Three-Port ConverterRecently, the interest in renewable energy has gained interest since these sources are promising for generating electricity. Solar energy tops the list of renewable energy sources. Solar photovoltaic (PV) panels are used to capture the solar energy radiated from the sun. Since solar energy is unavailable throughout the day, a battery is added. In a PV/battery system, a three-port converter is needed to interface the PV and battery with the load. This paper applies Maximum Power Point Tracking (MPPT) methods to a system with a three-port converter. These methods are Perturb and Observe (P&O) and Incremental Conductance (IC). MATLAB/SIMULINK software is used to perform the simulation. The temperature and irradiance are varied to simulate environmental changes in a real-world environment. Based on the results, the IC method performs slightly better than P&O. This indicates that a three-port converter is more stable regarding environmental changes than regular two-port converters. The usage of a three-port converter has gained recent interest. The significance of this paper is that it compares different MPPT methods applied to a three-port converter to be able to determine the suitable MPPT for a specific application.
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Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge SystemsThe emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad’s Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities.
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Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural NetworkThe detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects. The effectiveness of the proposed method is confirmed by testing its performance in processing an industrial dataset. The intended dataset contains 4479 X-ray images and belongs to six groups: cavity, cracks, inclusion slag, lack of fusion, shape defects, and normal defects. The devised technique achieved an average accuracy of 92%. This indicates that the approach is promising and can be used in contemporary solutions for the automated detection and categorization of welding defects.
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An Affordable EOG-based Application for Eye Dystonia EvaluationThis paper evaluates the risk of ocular dystonia-a condition marked by excessive blinking-using electrooculography. A commercial bioamplifier is employed to capture the electrical activity of the eyes using dispensed surface electrodes. The continuous wavelet transform of the electrooculogram was estimated to identify the features related to involuntary eye-blinking behavior and make the classification. The signal processing is integrated into a novel application with a simple graphical user interface oriented to be used by physicians. The performance is evaluated using multiple evaluation measures. Results show that the proposed method succeeded in identifying an abnormal frequency of blinks with respective accuracy, precision, sensitivity, and specificity scores of 98.46%,96.51%,99.13%, and 96.41%.
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EEG Signal based Schizophrenia Recognition by using VMD Rose Spiral Curve Butterfly Optimization and Machine LearningSchizophrenia is a mental illness that can negatively impact a patient's mental abilities, emotional propensities, and the standard of their private and social lives. Processing EEG data has evolved into a useful tool for tracking and identifying psychological brain states. In this framework, this paper focus on developing an automated approach for recognizing schizophrenia using non-invasive EEG signals. The EEG signals are segmented and onward decomposed by using the Variational Mode Decomposition (VMD). Each mode is termed a variational mode function (VMF). Onward, features from each intended VMF are mined based on a Rose Spiral Curve (RSC). The mined features are concatenated to present an instance. Afterward, the most pertinent features are selected using the Butterfly Optimization Algorithm (BOA). The selected feature set is conveyed to the classification module. Two classification approaches are applied in this study namely, the k-nearest neighbor (k-NN) and Random Forest (RF). The applicability is tested by using a publicly available EEG schizophrenia dataset. The highest accuracy of 89.0 % is secured for the case of RF.
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Causal speech enhancement using dynamical-weighted loss and attention encoder-decoder recurrent neural networkSpeech enhancement (SE) reduces background noise signals in target speech and is applied at the front end in various real-world applications, including robust ASRs and real time processing in mobile phone communications. SE systems are commonly integrated into mobile phones to increase quality and intelligibility. As a result, a low-latency system is required to operate in real-world applications. On the other hand, these systems need effi cient optimization. This research focuses on the single-microphone SE operating in real time systems with better optimization. We propose a causal data-driven model that uses attention encoder-decoder long short-term memory (LSTM) to estimate the time-frequency mask from a noisy speech in order to make a clean speech for real-time applications that need low-latency causal processing. The encoder-decoder LSTM and a causal attention mechanism are used in the proposed model. Furthermore, a dynamical-weighted (DW) loss function is proposed to improve model learning by varying the weight loss values. Experi ments demonstrated that the proposed model consistently improves voice quality, intelligi bility, and noise suppression. In the causal processing mode, the LSTM-based estimated suppression time-frequency mask outperforms the baseline model for unseen noise types. The proposed SE improved the STOI by 2.64% (baseline LSTM-IRM), 6.6% (LSTM-KF), 4.18% (DeepXi-KF), and 3.58% (DeepResGRU-KF). In addition, we examine word error rates (WERs) using Google’s Automatic Speech Recognition (ASR). The ASR results show that error rates decreased from 46.33% (noisy signals) to 13.11% (proposed) 15.73% (LSTM), and 14.97% (LSTM-KF).
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Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learningThe Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and “Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are “Fractal Dimension” (FD), “Higher Order Spectra” (HOS), “Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the “Naïve Bayes” (NB), “Support Vector Machine” (SVM), “Random Forest” (RF), and “K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.
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Noise Effects on a Proposed Algorithm for Signal Reconstruction and Bandwidth OptimizationThe development of wireless technology in recent years has increased the demand for channel resources within a limited spectrum. The system's performance can be improved through bandwidth optimization, as the spectrum is a scarce resource. To reconstruct the signal, given incomplete knowledge about the original signal, signal reconstruction algorithms are needed. In this paper, we propose a new scheme for reducing the effect of adding additive white Gaussian noise (AWGN) using a noise reject filter (NRF) on a previously discussed algorithm for baseband signal transmission and reconstruction that can reconstruct most of the signal’s energy without any need to send most of the signal’s concentrated power like the conventional methods, thus achieving bandwidth optimization. The proposed scheme for noise reduction was tested for a pulse signal and stream of pulses with different rates (2, 4, 6, and 8 Mbps) and showed good reconstruction performance in terms of the normalized mean squared error (NMSE) and achieved an average enhancement of around 48%. The proposed schemes for signal reconstruction and noise reduction can be applied to different applications, such as ultra-wideband (UWB) communications, radio frequency identification (RFID) systems, mobile communication networks, and radar systems.
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A complexity efficient PAPR reduction scheme for FBMC-based VLC systemsVisible light communication based on a filter bank multicarrier holds enormous promise for optical wireless communication systems, due to its high-speed and unlicensed spectrum. Moreover, visible light communication techniques greatly impact communication links for small satellites like cube satellites, and pico/nano satellites, in addition to inter-satellite communications between different satellite types in different orbits. However, the transmitted visible signal via the filter bank multicarrier has a high amount of peak-toaverage power ratio, which results in severe distortion for a light emitting diode output. In this work, a scheme for enhancing the peak-to-average power ratio reduction amount is proposed. First, an algorithm based on generating two candidates signals with different peakto-average power ratio is suggested. The signal with the lowest ratio is selected and transmitted. Second, an alternate direct current-biased approach, which is referred to as the addition reversed method, is put forth to transform transmitted signal bipolar values into actual unipolar ones. The performance is assessed through a cumulative distribution function of peak-to-average power ratio, bit error rate, power spectral density, and computational complexity. The simulation results show that, compared to other schemes in literature, the proposed scheme attains a great peak-to-average power ratio reduction and improves the bit the error rate performance with minimum complexity overhead. The proposed approach achieved about 5 dB reduction amount compared to companding technique, 5.5 dB compared to discrete cosine transform precoding, and 8 dB compared to conventional direct current bias of an optical filter bank multicarrier. Thus, the proposed scheme reduces the complexity overhead by 15.7% and 55.55% over discrete cosine transform and companding techniques, respectively
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Effect of MPPT Technique in Solar System Efficiency: A Case StudyWorldwide energy demand is growing fast because of the population explosion. Technological advancement paved the way toward utilizing renewable energy sources instead of fossil fuels as they cause harmful effects on the environment. Among all renewable sources, solar energy is a promising source. Solar energy is captured by photovoltaic (PV) arrays that convert the sunlight into electricity that powers the load. A vital component in the PV system is the DC-DC converter. Maximum Power Point Tracking (MPPT) techniques can be applied to the DC-DC converter to deliver maximum power from the PV array. Thus, the efficiency of the PV system increases. This paper compares the output of a PV system with and without applying MPPT techniques. The PV system installed at Effat university library's rooftop is considered a case study in this paper. Matlab/Simulink software is used for simulation. The results show that applying MPPT leads to a more significant PV power. The output PV power when applying MPPT has an efficiency of 99.93%, and when there is no MPPT, the output power has an efficiency of 21.21%. Also, the power, voltage, and current waveforms in the system with MPPT achieved less harmonic distortion than the system without MPPT
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Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication NetworksThe intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all network administrators. Several existing methods have been proposed for early intrusion detection systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced long-short term memory (ELSTM) technique with recurrent neural network (RNN) (ELSTM-RNN) to enhance security in IDS. Intrusion detection technology has been associated with various problems, such as gradient vanishing, generalization, and overfitting issues. The proposed system solves the gradient-clipping issue using the likely point particle swarm optimization (LPPSO) and enhanced LSTM classification. The proposed method was evaluated using the NSL-KDD dataset (KDD TEST PLUS and KDD TEST21) for validation and testing. Many efficient features were selected using an enhanced technique, namely, the particle swarm optimization. The selected features serve for effective classification using an enhanced LSTM framework, where it is used to efficiently classify and detect the attack data from the normal data. The proposed system has been applied to the UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, and BOT _DATASET datasets for further verification. Results show that the training time of the proposed system is much less than that of other methods for different classes. Finally, the performance of the proposed ELSTM-RNN framework is analyzed using various metrics, such as accuracy, precision, recall, and error rate. Our proposed method outperformed LPBoost and DNNs methods.
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Throughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding SchemesOn fifth-generation wireless networks, a potential massive MIMO system is used to meet the ever-increasing request for high-traffic data rates, high-resolution streaming media, and cognitive communication. In order to boost the trade-off between energy efficiency (EE), spectral efficiency (SE), and throughput in wireless 5G networks, massive MIMO systems are essential. This paper proposes a strategy for EE 5G optimization utilizing massive MIMO technology. The massive MIMO system architecture would enhance the trade-off between throughput and EE at the optimum number of working antennas. Moreover, the EE-SE tradeoff is adjusted for downlink and uplink massive MIMO systems employing linear precoding techniques such as Multiple - Minimum Mean Square Error (M-MMSE), Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio (MR). Throughput is increased by adding more antennas at the optimum EE, according to the analysis of simulation findings. Next, utilizing M MMSE instead of RZF and ZF, the suggested trading strategy is enhanced and optimized. The results indicate that M-MMSE provides the best tradeoff between EE and throughput at the determined optimal ratio between active antennas and ctive users equipment’s (UE)
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On the inverse power law-normal model for life prediction of organic light emitting diodesIn accelerated life testing analysis with nonthermal accelerating stress, the inverse power law (IPL) is often solely merged with a particular lifetime probability distribution with a shape parameter. Although many fundamental lifetime distributions, such as the normal distribution, are excellent fits to the experimental lifetime data, they have not been considered as they lack the shape parameter. As such, this paper, for the first time, demonstrates that the shape parameter can be replaced by the coefficient of variation, allowing the use of normal distributions in this context. The work further introduces the IPL-normal model in a rigorous mathematical setup that precisely leads to the least squares estimating equations and maximum likelihood estimates of the IPL-normal accelerating parameters and the general coefficient of variation. The proposed model uses accelerated experimental data to successfully predict the lifetime of organic light-emitting diodes (OLEDs) at use conditions. Based on these fundamentals, the predictions are benchmarked with prior works that were validated by market studies.
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Optimizing Energy Efficiencies of IoT-based Wireless Sensor Network Components for Metaverse Sustainable Development using Carry Resist Adder based Booth Recoder (CRABRA)Wireless sensing is now the spine of diverse Internet of Things (IoT) applications. In the Metaverse, the Internet of Things (IoT) can offer wireless and seamlessly integrated immersive digital experiences. Because the Metaverse's enabling technologies are considered to be energy-hungry, questions have been raised concerning the sustainability of its widespread adoption and development. IoT-based wireless sensor networks (WSN) readings are contaminated and distorted by noise. The noise in the signal causes the sensor node's (SN) computations and power consumption to rise, shortening the sensor node's longevity. To reduce noise, an efficient technique is therefore crucial. Finite-impulse response (FIR) filter is commonly employed in IoT-based WSN as a signal pre-processing stage in eliminating noise from the sensor measurements. The multiplication operation's number of adders (logic operators) and the adder steps (logic depths) determine the hardware complexities of FIR filters. The speed of the related application is determined by the multiplier's speed. By reducing the partial product (PP) row, the Booth method speeds up multiplication. The coefficients used by R8BR are ±0,±1 ,±2,±3,and ±4. As a result of the formation of odd multiples ±3, there will be a delay. The adder is required to add ±1 and ±2 for its calculations. This slowdown the multiplication procedures and reduces the recoding performance. To reduce the delay brought on by the creation of odd multiples, a carry resists adder (CRA) is used. CRA was explicitly built to achieve adding of ±2 and ±1 without carry propagation. Theoretically, it is observed that the CRA minimizes delay to 86.26% compared to carry propagation adder (CPA) approaches. Additionally, compared to a typical R8BR multiplier, the experimental findings indicated delay, area, and power reductions of 48.98%, 56.66%, and 31.2%, respectively. Without carry propagating, the CRA does addition faster, with less energy, and occupies less area.
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Design of Fuzzy Controller for a Hybrid Active/Passive Cooling System in Smart Homes with a WindcatcherGlobally, most building energy consumption is associated with heating, ventilation, and air conditioning systems (HVAC). Building energy consumption increased from 115 EJ in 2010 to nearly 135 EJ in 2021, accounting for 30% of global final energy consumption. In 2021, electricity will account for approximately 35% of building energy use, up from 30% in 2010. Space cooling, in particular, saw the greatest increase in demand across all building end uses in 2021, increasing by more than 6.5% over 2020. This study aims to set design guidelines to reduce energy consumption in building sector by proposing a hybrid active/passive cooling smart system. This can reduce energy consumed by the electricity grid by achieving natural ventilation through wind catchers. The later is a historical architectural element used in buildings to provide cross ventilation and passive cooling. The architectural modeling of the proposed system's design is conducted using Autodesk Revit. The smart controlling system is implemented with Fuzzy logic in MATLAB Simulink. Moreover, the accuracy of the system is improved by a PID tuning based on Backpropagation Neural Network. The results confirmed the effectiveness of the methodology used.
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Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid RobotsMetaheuristics optimization techniques are significant to search methods that are used to solve challenging Artificial intelligence (AI) problems. In hybrid robot control systems, Meta-heuristic optimization methods are widely applied. The major goal of this paper is to develop optimized PID control parameters to improve the performance of the hybrid robot control system. For that purpose, two optimization techniques followed by fine-tuning are proposed and simulated to get the optimized PID parameters. The first proposed optimization method applies the Satin Bowerbird (SB) optimization technique to optimize the PID parameters. The Crow Search Optimization (CSO) technique is applied to the SB results to improve the algorithm's performance and the PID parameters. The second proposed method applies the Emperor Penguin Optimization (EPO) technique for the optimization of the PID parameters. The results of both methods are fine-tuned. Moreover, a Kalman filter is used to improve the outcomes after and before tuning the PID parameters. Simulation results show that the proposed first method is more effective for the optimization methods of the PID controller, and its results outperform the results given by previously published research.
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A Machine learning-driven IoT architecture for predicting the growth and trend of Covid-19 epidemic outbreaks to identify high-risk locationsCovid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology.
