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Go to Editorial ManagerIn recent years, there has been a considerable rise in the applications in which object or image categorization is beneficial for example, analyzing medicinal images, assisting persons to organize their collections of photos, recognizing what is around self-driving vehicles, and many more. These applications necessitate accurately labeled datasets, in their majority involve an extensive diversity in the types of images, from cats or dogs to roads, landscapes, and so forth. The fundamental aim of image categorization is to predict the category or class for the input image by specifying to which it belongs. For human beings, this is not a considerable thing, however, learning computers to perceive represents a hard issue that has become a broad area of research interest, and both computer vision techniques and deep learning algorithms have evolved. Conventional techniques utilize local descriptors for finding likeness between images, however, nowadays; progress in technology has provided the utilization of deep learning algorithms, especially the Convolutional Neural Networks (CNNs) to auto-extract representative image patterns and features for classification The fundamental aim of this paper is to inspect and explain how to utilize the algorithms and technologies of deep learning to accurately classify a dataset of images into their respective categories and keep model structure complication to a minimum. To achieve this aim, must focus precisely and accurately on categorizing the objects or images into their respective categories with excellent results. And, specify the best deep learning-based models in image processing and categorization. The developed CNN-based models have been proposed and a lot of pre-training models such as (VGG19, DenseNet201, ResNet152V2, MobileNetV2, and InceptionV3) have been presented, and all these models are trained on the Caltech-101 and Caltech-256 datasets. Extensive and comparative experiments were conducted on this dataset, and the obtained results demonstrate the effectiveness of the proposed models. The obtained results demonstrate the effectiveness of the proposed models. The accuracy for Caltech-101 and Caltech-256 datasets was (98.06% and 90%) respectively.
The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models (ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated moving average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro- Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared with the traditional ARIMA method.
Current automatic writing feedback systems cannot distinguish between different discourse elements in students' writing. This is a problem because, without this ability, the guidance provided by these systems is too general for what students want to achieve on arrival. This is cause for concern because automated writing feedback systems are a great tool for combating student writing declines. According to the National Assessment of Educational Progress, less than 30 percent of high school graduates are gifted writers. If we can improve the automatic writing feedback system, we can improve the quality of student writing and stop the decline of skilled writers among students. Solutions to this problem have been proposed, the most popular being the fine-tuning of bidirectional encoder representations from Transformers models that recognize various utterance elements in student written assignments. However, these methods have their drawbacks. For example, these methods do not compare the strengths and weaknesses of different models, and these solutions encourage training models over sequences (sentences) rather than entire articles. In this article, I'm redesigning the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements corpus so that models can be trained for the entire article, and I've included Transformers, the Long Document Transformer's bidirectional encoder representation, and the Generative Improving a pre trained Transformer 2 model for utterance classification in the context of a named entity recognition token classification problem. Overall, the bi-directional encoder representation of the Transformers model railway using my sequence-merging preprocessing method outperforms the standard model by 17% and 41% in overall accuracy. I also found that the Long Document Transformer model performed the best in utterance classification with an overall f-1 score of 54%. However, the increase in validation loss from 0.54 to 0.79 indicates that the model is overfitting. Some improvements can still be made due to model overfittings, such as B. Implementation of early stopping techniques and further examples of rare utterance elements during training.
The Leader detecting and following are one of the main challenges in designing a leader-follower multi-robot system, in addition to the challenge of achieving the formation between the robots, while tracking the leader. The biological system is one of the main sources of inspiration for understanding and designing such multi-robot systems, especially, the aggregations that follow an external stimulus such as light. In this paper, a multi-robot system in which the robots are following a spotlight is designed based on the behavior of the Artemia aggregations. Three models are designed: kinematic and two dynamic models. The kinematic model reveals the light attraction behavior of the Artemia aggregations. The dynamic model will be derived based on the newton equation of forces and its parameters are evaluated by two methods: first, a direct method based on the physical structure of the robot and, second, the Least Square Parameter Estimation method. Several experiments are implemented in order to check the success of the three proposed systems and compare their performance. The experiments are divided into three scenarios of simulation according to three paths: the straight line, circle, zigzag path. The V-Rep software has been used for the simulation and the results appeared the success of the proposed system and the high performance of tracking the spotlight and achieving the flock formation, especially the dynamic models.
Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.
Recently, numerous researches have emphasized the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, different machine learning models for PV fault detection using a simulated 0.25MW PV power system were developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to-ground faults. Multiple electrical and environmental variables were measured and exploited as features, such as current, voltage, power, temperature, and irradiance. Four algorithms (Tree, LDA, SVM, and ANN) were tested using 5-fold cross-validation to identify errors in the PV system. The performance evaluation of the models revealed promising results, with all algorithms demonstrating high accuracy. The Tree and LDA algorithms exhibited the best performance, achieving accuracies of 99.544% on the training data and 98.058% on the testing data. LDA achieved perfect accuracy (100%) on the testing data, while SVM and ANN achieved 95.145% and 89.320% accuracy, respectively. These findings underscore the potential of machine learning algorithms in accurately detecting and classifying various types of PV faults. .
This research paper presents an innovative approach to wind nowcasting, addressing specific performance parameters through advanced machine learning techniques. The research aims to overcome inherent challenges in capturing intricate spatiotemporal relationships within wind data. Our novel methodology integrates Conv-LSTM-3D models, emphasizing the prediction of next-frame wind patterns. The Conv-LSTM-3D architecture, combining 3D convolutions and LSTM networks, is specifically tailored to effectively learn temporal dependencies and spatial features in wind data. The introduction outlines the pressing issues associated with traditional wind nowcasting methods, emphasizing the need for improved accuracy and prediction reliability. The primary objectives of this study are to explore the potential of Conv-LSTM-3D models in enhancing wind nowcasting and to assess their performance against traditional methods. Through comprehensive experiments, our approach demonstrates significant improvements in critical performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Specifically, improvements of 0.01, 19.23, 0.11, and 0.64 are observed, highlighting the enhanced accuracy and prediction reliability in the context of next-frame wind nowcasting. Notably, the system achieves these advancements within a reduced time frame, taking only 1149 seconds. This research contributes significantly to the advancement of meteorological prediction techniques, offering a refined short-term wind forecasting tool with potential applications across various fields. The improved clarity and organization of our methodology and findings pave the way for more effective utilization of Conv-LSTM-3D models in enhancing wind nowcasting capabilities.
Aiming to enhance the accuracy of sign classification in sign language (SL), this research presents an innovative approach that combines hand-engineered characteristics with deep learning (DL) algorithms. The focus is on American Sign Language (ASL), a critical communication tool for the deaf and hard-of-hearing community. The goal is to bridge the existing communication chasm between SL users and the general public by designing a real-time SL recognition system that allows non- SL users to converse with the hearing-impaired individuals. The application and assessment of various machine learning (ML) models, such as VGG19, DenseNet, ResNet50, MobileNet, and NASNetMobile, yielded promising outcomes with superior evalu- ation metrics. These models exhibit utility in the classification of ASL signs as they can differentiate between diverse hand gestures with high accuracy (ACC). The paper highlights the potential of these models across an array of ASL recognition applica- tions, considering factors like computational resources, model dimension, and real-time functionality. The findings endorse the application of ML techniques in SL interpretation, promoting inclusive communication for those with hearing impairment.
In this paper new semi-empirical formulas are developed to evaluate the variation of both real and imaginary parts of soil complex permittivity with depth inside the earth's surface. Computed values using these models show good agreement with published measured values for soils of the same textures and same frequency band. Use of these models may serve to handle more accurate results especially in the ground probing radar (GPR) applications and other applications relating the detection of buried objects inside the earth's surface, where the use of a single average value of the soil complex permittivity had not necessarily led, for most of the times, to accurate results for the electromagnetic fields propagated inside the earth's surface.
WiFi-based human activity and gesture recognition explore the interaction between the human hand or body movements and the reflected WiFi signals to identify various activities. This type of recognition has received much attention in recent years since it does not require wearing special sensors or installing cameras. This paper aims to investigate human activity and gesture recognition schemes that use Channel State Information (CSI) provided by WiFi devices. To achieve high accuracy in the measurement, deep learning models such as AlexNet, VGG 19, and SqueezeNet were used for classification and extracting features automatically. Firstly, outliers are removed from the amplitude of each CSI stream during the preprocessing stage by using the Hampel identifier algorithm. Next, the RGB images are created for each activity to feed as input to Deep Convolutional Neural Networks. After that, data augmentation is implemented to reduce the overfitting problems in deep learning models. Finally, the proposed method is evaluated on a publicly available dataset called WiAR, which contains 10 volunteers, each of whom executes 16 activities. The experiment results demonstrate that AlexNet, VGG19, and SqueezeNet all have high recognition accuracy of 99.17 %, 96.25%, and 100 %, respectively.
Soft robots, which are often considered safer than rigid robots when interacting with humans due to the reduced risk of injury, have found utility in various medical and industrial fields. Pneumatic artificial muscles (PAMs), one of the most widely used soft actuators, have proven their efficiency in numerous applications, including prosthetic and rehabilitation robots. PAMs are lightweight, responsive, precise, and capable of delivering a high force-to-weight ratio. Their structure comprises a flexible, inflatable membrane reinforced with fibrous twine and fitted with gas-sealing fittings. For the optimal design and integration of these into control systems, it is crucial to develop mathematical models that accurately represent their functioning mechanisms. This paper introduces a general concept of PAM’s construction, its various types, and operational mechanisms, along with its key benefits and drawbacks, and also reviews the most common modeling techniques for PAM representation. Most models are grounded in PAM architecture, aiming to calculate the actuator’s force across its full axis by correlating pressure, length, and other parameters that influence actuator strength.
Gait as a biometric can be used to identify subjects at a distance and thus it receives great attention from the research community for security and surveillance applications. One of the challenges that affects gait recognition performance is view variation. Much work has been done to tackle this challenge. However, the majority of the work assumes that gait silhouettes are captured by affine cameras where only the height of silhouettes changes and the difference in viewing angle of silhouettes in one gait cycle is relatively small. In this paper, we analyze the variation in gait recognition performance when using silhouettes from projective cameras and from affine cameras with different distance from the center of a walking path. This is done by using 3D models of walking people in the gallery set and 2D gait silhouettes from independent (single) cameras in the probe set. Different factors that affect matching 3D human models with 2D gait silhouettes from single cameras for view-independent gait recognition are analyzed. In all experiments, we use 258 multi-view sequences belong to 46 subjects from Multi-View Soton gait dataset. We evaluate the matching performance for 12 different views using Gait Energy Image (GEI) as gait features. Then, we analyze the effect of using different camera configurations for 3D model reconstruction, the GEI from cameras with different settings, the upper and lower body parts for recognition and different GEI resolutions. The results illustrate that low recognition performance is achieved when using gait silhouettes from affine cameras while lower recognition performance is obtained when using gait silhouettes from projective cameras.
COVID-19 is an infectious viral disease that mostly affects the lungs. That quickly spreads across the world. Early detection of the virus boosts the chances of patients recovering quickly worldwide. Many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease. Because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models. Another problem with a limited dataset is that training models suffer from over-fitting, and the predictions are not generalizable to address these problems. In this paper, we developed Conditional Generative Adversarial Networks (CGAN) to produce synthetic images close to real images for the COVID-19 case and traditional augmentation that was used to expand the limited dataset then used to train by Customized deep detection model. The Customized Deep learning model was able to obtain excellent detection accuracy of 97% accurate with only ten epochs. The proposed augmentation outperforms other augmentation techniques. The augmented dataset includes 6988 high-quality and resolution COVID-19 X-rays images. At the same time, the original COVID-19 X-rays images are only 587.
Due to the changing flow conditions during the pipeline's operation, several locations of erosion, damage, and failure occur. Leak prevention and early leak detection techniques are the best pipeline risk mitigation measures. To reduce detection time, pipeline models that can simulate these breaches are essential. In this study, numerical modeling using COMSOL Multiphysics is suggested for different fluid types, velocities, pressure distributions, and temperature distributions. The system consists of 12 meters of 8-inch pipe. A movable ball with a diameter of 5 inches is placed within. The findings show that dead zones happen more often in oil than in gas. Pipe insulation is facilitated by the gas phase's thermal inefficiency (thermal conductivity). The fluid mixing is improved by 2.5 m/s when the temperature is the lowest. More than water and gas, oil viscosity and dead zones lower maximum pressure. Pressure decreases with maximum velocity and vice versa. The acquired oil data set is utilized to calibrate the Support Vector Machine and Decision Tree techniques using MATLAB R2021a, ensuring the precision of the measurement. The classification result reveals that the Support Vector Machine (SVM) and Decision Tree (DT) models have the best average accuracy, which is 98.8%, and 99.87 %, respectively.
The reliance on networks and systems has grown rapidly in contemporary times, leading to increased vulnerability to cyber assaults. The Distributed Denial-of-Service (Distributed Denial of Service) attack, a threat that can cause great financial liabilities and reputation damage. To address this problem, Machine Learning (ML) algorithms have gained huge attention, enabling the detection and prevention of DDOS (Distributed Denial of Service) Attacks. In this study, we proposed a novel security mechanism to avoid Distributed Denial of Service attacks. Using an ensemble learning methodology aims to it also can differentiate between normal network traffic and the malicious flood of Distributed Denial of Service attack traffic. The study also evaluates the performance of two well-known ML algorithms, namely, the decision tree and random forest, which were used to execute the proposed method. Tree in defending against Distributed Denial of Service (DDoS) attacks. We test the models using a publicly available dataset called TIME SERIES DATASET FOR DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION. We compare the performance of models using a list of evaluation metrics developing the Model. This step involves fetching the data, preprocessing it, and splitting it into training and testing subgroups, model selection, and validation. When applied to a database of nearly 11,000 time series; in some cases, the proposed approach manifested promising results and reached an Accuracy (ACC) of up to 100 % in the dataset. Ultimately, this proposed method detects and mitigates distributed denial of service. The solution to securing communication systems from this increasing cyber threat is this: preventing attacks from being successful.
The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.
Animating human face presents interesting challenges because of its familiarity as the face is the part utilized to recognize individuals. This paper reviewed the approaches used in facial modeling and animation and described their strengths and weaknesses. Realistic face animation of computer graphic models of human faces can be hard to achieve as a result of the many details that should be approximated in producing realistic facial expressions. Many methods have been researched to create more and more accurate animations that can efficiently represent human faces. We described the techniques that have been utilized to produce realistic facial animation. In this survey, we roughly categorized the facial modeling and animation approach into the following classes: blendshape or shape interpolation, parameterizations, facial action coding system-based approaches, moving pictures experts group-4 facial animation, physics-based muscle modeling, performance driven facial animation, visual speech animation.
Detecting pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54%, while the classification precision among the three classes was 98.63%. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.
Among all control methods for induction motor drives, Direct Torque Control (DTC) seems to be particularly interesting being independent of machine rotor parameters and requiring no speed or position sensors. The DTC scheme is characterized by the absence of PI regulators, coordinate transformations, current regulators and PWM signals generators. In spite of its simplicity, DTC allows a good torque control in steady state and transient operating conditions to be obtained. However, the presence of hysterics controllers for flux and torque could determine torque and current ripple and variable switching frequency operation for the voltage source inverter. This paper is aimed to analyze DTC principles, and the problems related to its implementation, especially the torque ripple and the possible improvements to reduce this torque ripple by using a proposed fuzzy based duty cycle controller. The effectiveness of the duty ratio method was verified by simulation using Matlab/Simulink software package. The results are compared with that of the traditional DTC models.
Due to their vital applications in many real-world situations, researchers are still presenting bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) signals. However, in general, EEG signals are complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification. In this paper, several hybrid classification models are built and their performance is compared. Three famous wavelet mother functions are used for generating scalograms from the raw signals. The scalograms are used for transfer learning of the well-known VGG-16 deep network. Then, one of six classifiers is used to determine the class of the input signal. The performance of different combinations of mother functions and classifiers are compared on two MI EEG datasets. Several evaluation metrics show that a model of VGG-16 feature extractor with a neural network classifier using the Amor mother wavelet function has outperformed the results of state-of-the-art studies.
Wind Energy Conversion Systems (WECSs) have experienced significant growth in recent years.Among various types of generators employed in WECSs,Permanent Magnet Synchronous Generators (PMSGs) are an attractive choice among the wide variety of wind generators due to several advantages.The growing penetration of PMSG-based WEGSs into the worldwide electrical grid raises the concern that the failure of wind turbine generators may potentially result in the collapse of the system.This prompted several countries to adopt the Low-Voltage Ride-Through (LVRT) for wind farms.LVRT is the capability to maintain the connection between the wind farm and the grid during certain periods of voltage sag.This paper presents an efficient LVRT control strategy for a 12.0MW (6*2MW) grid-connected PMSG-based Wind Farm (PMSG-WF).The proposed strategy aims to enhance the power quality and amount of injected power to achieve the grid code requirements by integrating a Braking Chopper (BC) and a Dynamic Voltage Restorer (DVR) with the conventional structure of PMSG-WF. The detailed mathematical models for a wind turbine, PMSG, power converters, DVR system, and grid model are utilized to analyze the dynamic behavior and operation of PMSG-WF.For DVR, a PI controller is used for voltage sag mitigation to inject reactive power during grid faults, while a hysteresis controller-based BC system is utilized to keep DC-link voltage within its permissible limits.The proposed system is exposed to three scenarios of symmetrical and asymmetrical grid fault conditions (single-phase, two-phase, and three-phase faults) at the point of common coupling to evaluate its dynamic response.MATLAB/SIMULINK environment is used to validate the effectiveness of the proposed strategy during the studied scenarios.The results show the superiority of DVR in improving the voltage stability of PMSG-WF and maintaining the uninterrupted operation of the grid during different grid faults.
CMOS stack circuits find applications in multi-input exclusive-OR gates and barrel-shifters. Specifically, in wide fan-in CMOS NAND/NOR gates, the need arises to connect a relatively large number of NMOS/PMOS transistors in series in the pull-down network (PDN)/pull-up network (PUN). The resulting time delay is relatively high and the power consumption accordingly increases due to the need to deal with the various internal capacitances. The problem gets worse with increasing the number of inputs. In this paper, the performance of conventional static CMOS stack circuits is investigated quantitatively and a figure of merit expressing the performance is defined. The word “performance” includes the following three metrics; the average propagation delay, the power consumption, and the area. The optimum scaling factor corresponding to the best performance is determined. It is found that under the worst-case low-to-high transition at the output (that is, the input combination that results in the longest time delay in case of logic “1” at the output), there is an optimum value for the sizing of the PDN in order to minimize the average propagation delay. The proposed figure of merit is evaluated for different cases with the results discussed. The adopted models and the drawn conclusions are verified by comparison with simulation results adopting the 45 nm CMOS technology.
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential featuresfrom electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integratesa feature extraction module, densely connected residual block (DCRB), longshort-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decisionmaking processes, making it a valuable tool for stakeholders in the electricity sector.
This paper deals with the application of Fuzzy-Neural Networks (FNNs) in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM) controlled by indirect field-oriented control (IFO). The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.
Lumbar spine stenosis (LSS) is a common reason for low back pain, which refers to anatomical spinal canal stenosis. It often causes pressure on the nerve elements due to the surrounding soft tissue and bone. Due to the huge number of spinal injuries, manual diagnosis of lumbar spine stenosis by radiologists is tedious or time-consuming. Therefore, Deep learning techniques have become a more helpful tool to overcome this problem. For this purpose, this study employed the YOLO-v5 to develop an LSS detection model on a dataset of lumbar spine MRI scans from 153 patients with symptomatic low back pain. The dataset was filtered to include 84 mid-sagittal images using annotation techniques. The detection model is utilized to classify the intervertebral disc (IVD) condition as either bulging or normal. The results obtained showed that the model achieved an accuracy exceeding 88% in detecting and classifying the lumbar spine vertebra. The developed models showed that they are effective for lumbar intervertebral disc classification.
This paper presents the PLC-HMI based simulation of electrical-based PV cell/array model in laboratory platform to give the opportunity to students and users who haven't clear knowledge to study PV cell and array behavior with respect to change of environment conditions and electrical parameters. This simulation process covers the cell models under ideal and non-ideal ones. In non-ideal one, the series resistance and the shunt resistance are covered.
In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.
In this article, a robust control technique for 2-DOF helicopter system is presented. The 2-DOF helicopter system is 2 inputs and 2 outputs system that is suffering from the high nonlinearity and strong coupling. This paper focuses on design a simple, robust, and optimal controller for the helicopter system. Moreover, The proposed control method takes into account effects of the measurement noise in the closed loop system that effect on the performance of controller as well as the external disturbance. The proposed controller combines low pass filter with robust PID controller to ensure good tracking performance with high robustness. A low pass filter and PID controller are designed based H∞weighted mixed sensitivity. Nonlinear dynamic model of 2-DOF helicopter system linearized and then decoupled into pitch and yaw models. Finally, proposed controller applied for each model. Matlab program is used to check effectiveness the proposed control method. Simulation results show that the proposed controllers has best tracking performance with no overshot and the smallest settling time with respect to standard H∞and optimized PID controller.
This work deals with the simulation model of multi-machines system as cold rolling mill is considered as application. Drivers of rolling system are a set of DC motors, which have extend applications in factories as aluminum rolling. Interconnection of multi DC motors in such a way that they are synchronized in their rotational speed. In cold rolling, the accuracy of the strip exit thickness is a very important factors. To realize accuracy in the strip exit thickness, Automatic Gauge Control system is used. In this paper MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results were presented to verify proposed model of cold rolling mill.
Epilepsy, a neurological disorder characterized by recurring seizures, necessitates early and precise detection for effective management. Deep learning techniques have emerged as powerful tools for analyzing complex medical data, specifically electroencephalogram (EEG) signals, advancing epileptic detection. This review comprehensively presents cutting-edge methodologies in deep learning-based epileptic detection systems. Beginning with an overview of epilepsy’s fundamental concepts and their implications for individuals and healthcare are present. This review then delves into deep learning principles and their application in processing EEG signals. Diverse research papers to know the architectures—convolutional neural networks, recurrent neural networks, and hybrid models—are investigated, emphasizing their strengths and limitations in detecting epilepsy. Preprocessing techniques for improving EEG data quality and reliability, such as noise reduction, artifact removal, and feature extraction, are discussed. Present performance evaluation metrics in epileptic detection, such as accuracy, sensitivity, specificity, and area under the curve, are provided. This review anticipates future directions by highlighting challenges such as dataset size and diversity, model interpretability, and integration with clinical decision support systems. Finally, this review demonstrates how deep learning can improve the precision, efficiency, and accessibility of early epileptic diagnosis. This advancement allows for more timely interventions and personalized treatment plans, potentially revolutionizing epilepsy management.
Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.
Wind energy and its conversion is part of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.
A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.
The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.
Precise power sharing considered is necessary for the effective operation of an Autonomous microgrid with droop controller especially when the total loads change periodically. In this paper, reactive power sharing control strategy that employs central controller is proposed to enhance the accuracy of fundamental reactive power sharing in an islanded microgrid. Microgrid central controller is used as external loop requiring communications to facilitate the tuning of the output voltage of the inverter to achieve equal reactive power sharing dependent on reactive power load to control when the mismatch in voltage drops through the feeders. Even if central controller is disrupted the control strategy will still operate with conventional droop control method. additionally, based on the proposed strategy the reactive power sharing accuracy is immune to the time delay in the central controller. The developed of the proposed strategy are validated using simulation with detailed switching models in PSCAD/EMTDC.
Low-quality data can be dangerous for the machine learning models, especially in crucial situations. Some large-scale datasets have low-quality data and false labels, also, datasets with images type probably have artifacts and biases from measurement errors. So, automatic algorithms that are able to recognize low-quality data are needed. In this paper, Shapley Value is used, a metric for evaluation of data, to quantify the value of training data to the performance of a classification algorithm in a large ImageNet dataset. We specify the success of data Shapley in recognizing low-quality against precious data for classification. We figure out that model performance is increased when low Shapley values are removed, whilst classification model performance is declined when high Shapley values are removed. Moreover, there were more true labels in high-Shapley value data and more mislabeled samples in low-Shapley value. Results represent that mislabeled or poor-quality images are in low Shapley value and valuable data for classification are in high Shapley value.
Global agriculture employs central pivot irrigation system(CPIS) as a highly significant method for intelligent irrigation. Cultivating crucial crops like wheat and other strategically important crops that occupy extensive land areas contributes to global food security. The Central Pivot Irrigation System encounters technical issues that result in malfunctions in its automatic control system. These malfunctions occasionally cause damage to the primary pipes and towers that operate the system, resulting in significant material losses for farmers and agricultural crops. Moreover, the repair process is time-consuming. Therefore, to address this issue, this study employed the YOLOv5 models to accurately identify and detect defects in the CPIS machine by determining whether they are in a safe or dangerous state. The dataset that was used in this study was gathered from agricultural areas in Salah al-Din Governorate. The CPIS detection model yielded the following results: the grayscale color system with Yolov5n achieved a 98 % detection rate with accuracy and F1-score values of 0.866. Similarly, Yolov5m achieved a 98 % detection rate with accuracy and F1-score values of 0.804. In the RGB color system, the maximum results achieved with Yolov5n are 97 % for accuracy and 0.812 for F1-score. On the other hand, Yolov5s6 achieves a result of 95 % for accuracy and 0.82 for both F1-score and accuracy. Based on the aforementioned outcome, we can infer that yolov5s6 accurately detects the CPIS in both its safe and dangerous states. Therefore, they can be deployed in a real-time system for CPIS defect monitoring and control systems.
In this paper describes the operation of power system networks to be nearest to stability rated values limits. State estimation for monitoring and protection power system is very important because it provides a real-time (RT) Phase angle of different nodes of accuracy and then analysis and decided to choose control way (methods). In order to detect the exact situation (instant state) for power system networks parameters. In this paper proposes a new monitoring and analysis system state estimation method integrating with MATLAB environment ability, by using phasor measurement units (PMU's) technology, by this system the estimation problem, iterations numbers, and processing time will reduce. The measurements of phasors value of voltage signal and current estimated and analyzed. Mat lab/PSAT package use as a tool to design and simulate four electrical power systems networks such as INSG 24 buses, IEEE14 bus, Diyala city 10buses (IRAQ), and IEEE6 bus and then installed and applied PMU’s devices to each system. Simulation results show that the PMU's performances effectiveness appear clearly. All results show the validation of PMU’s devices as an estimator to power system networks states and a significant improvement in the accuracy of the calculation of network status. All results achieved and discussed through this paper setting up mathematical models with Graph Theoretic Procedure algorithm.
Since recent societies become more hooked into electricity, a higher level of power supply continuity is required from power systems. The expansion of those systems makes them liable to electrical faults and several failures are raised due to totally different causes, like the lightning strike, power system element failure caused by mechanical aging as well as human mistakes. These conditions impact the stability of the power as well as lead to costly maintenance and loss of output. This article examines the latest technologies and strategies to determine the location of faults in medium voltage distribution systems. The aim is to classify and assess different strategies in order to determine the best recommended models in practice or for further improvement. Several ways to locate failures in distribution networks have therefore been established. Because faults are unpredictable, quick fault location as well as isolating are necessary to reduce the impact of faults in distribution networks as well as removing the emergency condition from the entire system. This study also includes a comprehensive evaluation of several defect location methods depending on the algorithm employed, the input, the test system, the characteristics retrieved, and the degree of complexity. In order to gain further insight into the strengths and limitations of each method and also comparative analysis is carried out. Then the main problems of the fault location methods in distribution network are briefly expounded.
Alzheimer’s disease (AD), the most common form of dementia, affects over 55 million people worldwide. The most form of dementia progresses into three distinct stages: mild, moderate, and very mild compared to Cognitively Normal (CN). Early detection is crucial to prevent brain damage before the late stages. Convolutional Neural Networks (CNNs), a subfield of deep learning, have recently found remarkable applications in medical image processing and computer-aided diagnosis (CAD). To this end, this paper presents a new efficient multi-classification AlzCNN-Net model to enhance the accuracy and efficacy of MRI image classification for various Alzheimer’s disease conditions. Initially, the training process involves utilizing open-source Alzheimer’s disease datasets from the Kaggle database to classify the brain MRI into its corresponding category. To verify the model’s efficacy, a comparative analysis with three pre-trained models, namely VGG16, Incep-tionV3, and MobileNetV2, has been investigated via transfer learning applied to the same dataset. As a result, the findings reveal that the AlzCNN-Net model exhibits an optimal performance, attaining the best accuracy in training with 99.67%, validation with 98.24%, and testing with 98.9% accuracy at epoch 100 with batch size 32 compared to the existing pre-trained approaches.
This research aims to understand the enhancing reading advancement using eye gaze tracking in regards to pull the increase of time interacting with such devices along. In order to realize that, user should have a good understanding of the reading process and of the eye gaze tracking systems; as well as a good understanding of the issues existing while using eye gaze tracking system for reading process. Some issues are very common, so our proposed implementation algorithm compensate these issues. To obtain the best results possible, two mains algorithm have been implemented: the baseline algorithm and the algorithm to smooth the data. The tracking error rate is calculated based on changing points and missed changing points. In [21], a previous implementation on the same data was done and the final tracking error rate value was of 126%. The tracking error rate value seems to be abnormally high but this value is actually useful as described in [21]. For this system, all the algorithms used give a final tracking error rate value of 114.6%. Three main origins of the accuracy of the eye gaze reading were normal fixation, regression, skip fixation; and accuracies are displayed by the tracking rate value obtained. The three main sources of errors are the calibration drift, the quality of the setup and the physical characteristics of the eyes. For the tests, the graphical interface uses characters with an average height of 24 pixels for the text. By considering that the subject was approximately at 60 centimeters of the tracker. The character on the screen represents an angle of ±0.88◦; which is just above the threshold of ±0.5◦ imposed by the physical characteristics of the eyeball for the advancement of reading using eye gaze tracking.
Studies indicate cardiac arrhythmia is one of the leading causes of death in the world. The risk of a stroke may be reduced when an irregular and fast heart rate is diagnosed. Since it is non-invasive, electrocardiograms are often used to detect arrhythmias. Human data input may be error-prone and time-consuming because of these limitations. For early detection of heart rhythm problems, it is best to use deep learning models. In this paper, a hybrid bio-inspired algorithm has been proposed by combining whale optimization (WOA) with adaptive particle swarm optimization (APSO). The WOA is a recently developed meta-heuristic algorithm. APSO is used to increase convergence speed. When compared to conventional optimization methods, the two techniques work better together. MIT-BIH dataset has been utilized for training, testing and validating this model. The recall, accuracy, and specificity are used to measure efficiency of the proposed method. The efficiency of the proposed method is compared with state-of-art methods and produced 98.25 % of accuracy.
Automatic handwriting recognition is a fundamental component of various applications in various fields. During the last three decades, it has become a challenging issue that has attracted much attention. Latin language handwriting recognition has been the primary focus of researchers. As for the Kurdish language, only a few researches have been conducted. This study uses a Kurdish character dataset, which contains 40,940 characters written by 390 native writers. We present an ensemble transfer learning-based model for automatically recognizing handwritten Kurdish letters using Densenet-201, InceptionV3, Xception, and an ensemble of these pre-trained models. The model’s performance and results obtained by the proposed ensemble model are promising, with a 97% accuracy rate, outperforming other studies on Kurdish character recognition.
Recent advancements in communication and wireless technologies have greatly increased the number of internet users. These users often share personal information online, making it vulnerable to attackers. Phishing, a common type of online fraud, involves tricking people into giving their personal information through spam or other deceptive methods. Even though this threat has been around for a long time, it is still very active and successful. Attackers have improved their methods over the years to make their attacks more convincing and effective. Therefore, it is important to carefully study this type of attack to raise awareness among both users and cybersecurity researchers. This review paper explains the basics, types, and methods of phishing and presents a unified attack lifecycle framework to provide users and researchers with a clear understanding of phishing. Additionally, anti-phishing methods are thoroughly analyzed to determine their strengths and weaknesses. Researchers use different strategies to develop anti-phishing solutions, including blacklisting, whitelisting, heuristics, machine learning, and deep learning techniques. To help readers choose the best anti-phishing solution, research studies using these strategies are categorized, evaluated, and compared using specific criteria to show their strengths and weaknesses. Furthermore, the datasets used to develop anti-phishing models are discussed and reviewed. Finally, this paper provides a detailed overview of current phishing challenges and suggests future research directions in this area.
The last few years Quadrotor became an important topic, many researches have implemented and tested concerning that topic. Quadrotor also called an unmanned Aerial Vehicle (UAV), it's highly used in many applications like security, civil applications, aid, rescue and a lot of other applications. It’s not a conventional helicopter because of small size, low cost and the ability of vertical and takeoff landing (VTOL). The models kept an eye on quadrotors were presented, the advancement of this new kind of air vehicle is hindered for a very long while because of different reasons, for example, mechanical multifaceted nature, enormous size and weight, and challenges in charge particularly. Just as of late a lot of interests and endeavors have been pulled in on it; a quadrotor has even become a progressively discretionary vehicle for useful application. Quadrotor can be used in variable, different , outdoor and indoor missions; these missions should be implemented with high value of accuracy and quality. In this work two scenarios suggested for different two missions. First mission the quadrotor will be used to reach different goals in the simulated city for different places during one flight using path following algorithm. The second mission will be an indoor arrival mission, during that mission quadrotor will avoid obstacles by using only Pure pursuit algorithm (PPA). To show the benefit of using the new strategy it will compare with a victor field histogram algorithm (VFH) which is used widely in robotics for avoiding obstacles, the comparison will be in terms of reaching time and distance of reaching the goal. The Gazebo Simulator (GS) is used to visualize the movement of the quadrotor. The gazebo has another preferred position it helps to show the motion development of the quadrotor without managing the mathematical model of the quadrotor. The Robotic Operating System (ROS) is used to transfer the data between the MATLAB Simulink program and the Gazebo Simulator. The diversion results show that, the proposed mission techniques win to drive the quarter on the perfect route similarly at the limit with regards to the quadrotor to go without hitting any obstacle in the perfect way.
Growing interests in nature-inspired computing and bio-inspired optimization techniques have led to powerful tools for solving learning problems and analyzing large datasets. Several methods have been utilized to create superior performance-based optimization algorithms. However, certain applications, like nonlinear real-time, are difficult to explain using accurate mathematical models. Such large-scale combination and highly nonlinear modeling problems are solved by usage of soft computing techniques. So, in this paper, the researchers have tried to incorporate one of the most advanced plant algorithms known as Venus Flytrap Plant algorithm(VFO) along with soft-computing techniques and, to be specific, the ANFIS inverse model-Adaptive Neural Fuzzy Inference System for controlling the real-time temperature of a microwave cavity that heats oil. The MATLAB was integrated successfully with the LabVIEW platform. Wide ranges of input and output variables were experimented with. Problems were encountered due to heating system conditions like reflected power, variations in oil temperature, and oil inlet absorption and cavity temperatures affecting the oil temperature, besides the temperature’s effect on viscosity. The LabVIEW design followed and the results figure in the performance of the VFO- Inverse ANFIS controller.
Arc problems are most commonly caused by electrical difficulties such as worn cables and improper connections. Electrical fires are caused by arc faults, which generate tremendous temperatures and discharge molten metal. Every year, flames of this nature inflict a great lot of devastation and loss. A novel approach for identifying residential series and parallel arc faults is presented in this study. To begin, arc faults in series and parallel are simulated using a suitable simulation arc model. The fault characteristics are then recovered using a signal processing technique based on the fault detection technique called Discrete Wavelet Transform (DWT), which is built in MATLAB/Simulink. Then came db2, and one level was discovered for obtaining arc-fault features. The suitable mother and level of wavelet transform should be used, and try to compare results with conventional methods (FFT-Fast Fourier Transform). MATLAB was used to build and simulate arc-fault models with these techniques.
A torsional rotating system is considered for the investigation of passive vibration control using dual loop controllers Proportional-Integral-Derivative (PID) with derivative (D) gain and Proportional – Derivative (PD) with Integral (I) controllers. The controllers are used as low pass filters. Simulation of the models using Matlab-Simulink have been built in this work for torsional vibration control. A comparison between the two controllers with uncontrolled system have been carried out. Results show that the PD – I control is the best method which gives better stability response than the PID – D control.
In this paper two theoretical models have been considered for the prediction of path loss for two different districts in Mosul city, using MATLAB 7.4 program. The Walfisch-Ikegami (W-I) model for uniform heights and similar buildings in the Karama district . The other model is Okumura-Hata (OH) model applied for irregular and dissimilar buildings in the Almajmoa'a district. The information buildings heights are obtained from the civil Eng. Depart. in Mosul university. In this paper it can be shown that The effect of distance in regular area (karama) on path loss is about 10 dB larger than irregular area (Almajmoa'a), and The effect of varying antenna height in regular area (karama) on path loss is about 7 dB greater than irregular area (Almajmoa'a) for 40 meter variation.
Federated learning (FL) is one of the newest and most significant fields for developing artificial intelligence applications. This technology trains its models in a distributed way, using data from different clients who work together in the system without sharing their data. The training process is kept local to protect the privacy of the data. Among the many difficulties that have arisen due to the novelty of this technology is the issue of heterogeneous data between typical clients. Client’s data may differ from each other in different respects, for example non identically and independent distribution (non-IID) between clients and the difference in the type of data used in each client. This can lead to inconsistencies in the model’s predictions and other undesirable outcomes. This paper discussed ways to solve this problem where clients with heterogeneous data were dealt with in terms of number and type. Because there are different types of image data through which doctors can diagnose coronavirus, such as x-rays, CT-scan. A hybrid convolution neural network (CNN ) and long short-term memory model (LSTM) has been proposed in a federated learning system to predict the incidence of this disease by using two clients, each with one of these different data. Good results were obtained with an accuracy of more than 99% in one customer and more than 95% in the second client while maintaining the privacy of this data.
Nowadays, cloud computing has attracted the attention of large companies due to its high potential, flexibility, and profitability in providing multi-sources of hardware and software to serve the connected users. Given the scale of modern data centers and the dynamic nature of their resource provisioning, we need effective scheduling techniques to manage these resources while satisfying both the cloud providers and cloud users goals. Task scheduling in cloud computing is considered as NP-hard problem which cannot be easily solved by classical optimization methods. Thus, both heuristic and meta-heuristic techniques have been utilized to provide optimal or near-optimal solutions within an acceptable time frame for such problems. In this article, a summary of heuristic and meta-heuristic methods for solving the task scheduling optimization in cloud-fog systems is presented. The cost and time aware scheduling methods for both bag of tasks and workflow tasks are reviewed, discussed, and analyzed thoroughly to provide a clear vision for the readers in order to select the proper methods which fulfill their needs.