Performance Evaluation Parameters Research Articles

A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients

Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot. In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other dataset. In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated. 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy. The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement for TCIA and BRaTS datasets respectively.

An Analytical Method for Timely Predicting of Coal Seam Pressure during Gas Production for Undersaturated Coalbed Methane Reservoirs

Coal seam pressure is an important parameter for production performance evaluation and prediction of coalbed methane (CBM). CBM production from undersaturated CBM reservoirs can be divided into two stages according to critical desorption pressure. At present, few prediction models of coal seam pressure performance consider the comprehensive influence of critical desorption pressure, dissolved gas, matrix shrinkage, and stress sensitivity. For the purpose of accurately predicting coal seam pressure during gas production for an undersaturated coalbed methane reservoir, the material balance principle is used to establish the analytical method for predicting coal seam pressure, considering the comprehensive influence of the critical desorption pressure, dissolved gas, matrix shrinkage, and stress sensitivity. Then, the proposed method is verified against a numerical simulation case using a computer modelling group (CMG) and two actual coalbed methane wells. Finally, the sensitivities of influencing factors on the coal seam pressure are analyzed. The results show that good agreements were obtained between the calculated coal seam pressures using the proposed method and those from the CMG-GEM simulation case and actual CBM wells, with the relative errors all being less than 1%. When ignoring the influence of critical desorption pressure and mistaking pd for pi as well as ignoring Cs, the relative error can reach as high as 31.3%. The main factors affecting the coal seam pressure are the critical desorption pressure and free gas saturation. The proposed method is simple to use, and without shutting-in the well, it can provide an important basis for production performance evaluation and development strategies.

Performance Evaluation of Government Section Employees at The Mappak District Office, Tana Toraja District

The aim of this research is to evaluate the performance of government section employees at the Mappak District office, Tana Torja Regency. In the context of the regional autonomy policy provided by Law Number 32 of 2014, regional governments have a greater responsibility to accelerate regional development and improve community welfare. One important aspect of implementing the regional autonomy policy is improving the performance of public services, which is realized through evaluating the performance of government officials. The Mappak District Office has a significant role in providing administrative and government services at the local level. Evaluation of the performance of the government section is carried out to identify successes, obstacles and improvements in the delivery of public services. These performance evaluation parameters include the smoothness of the administrative process related to service operational standards, the ability of the government section to work in accordance with existing duties and functions, the response from the surrounding community regarding government services, the relationship that exists between the government section and the surrounding community and the availability of facilities to support activities and government section services at the Mappak sub-district office, Tana Toraja Regency. With continuous evaluation and continuous improvement efforts, it is hoped that the performance of government can continue to improve in providing effective and efficient services to the community. The results of this evaluation will be the basis for continuous improvement and development, so that government section employees at the Mappak sub-district office, Tana Toraja Regency can provide better services to the community on an ongoing basis.

Dynamic behavior of three-wheel vehicle with inerter-based dynamic absorber suspension system

Three-wheeled vehicles may be a better choice as compared with four-wheeler cars provided they are properly designed to improve the rollover stabilities and ride comfort. The design modifications need improvement in geometry and suspension. This article presents the dynamic behavior of three-wheeled vehicle equipped with an inerter-based dynamic absorber and compares the same with the conventional suspension system. The performance parameters analyzed are vertical-lateral acceleration, front & rear suspension deflection, and front & rear dynamic tyre loads. The analytical rigid body model is formulated by assigning 9 degrees of freedom to the system for the evaluation of performance parameters to compare the inerter-based absorber suspension with the conventional passive suspension system.

Flow boiling heat transfer performance of R245fa in a vertical enhanced evaporator tube with sintering and electroplating composite coats

The R245fa flow boiling performance was investigated in a vertical evaporator tube of which inner wall surface is coated by a composite sintering and electroplating technology. The effects of vapor quality, heat flux, mass flux, and flow direction were compared and analyzed in the bare tube (BT) and the sintering and electroplating treated tube (SET). The results showed that the heat transfer coefficient increased and then decreased with the rise of vapor quality and heat flux. The heat transfer performance in downward flow was better than that in upward flow, while an opposite trend was observed for frictional pressure drop. With the increased of vapor quality, the frictional pressure drop increased and then decreased, but it monotonically increased with the increasing mass flux. The heat transfer enhancement mechanism was explored through flow pattern visualization. The annular flow pattern conversion line of SET was earlier than that of BT. Due to the strong wettability of SET, the thickness of liquid film was thinner in downward and the mother bubble was bigger in upward, which had a positive effect on flow boiling heat transfer. The maximum the heat transfer enhancement factor (EF) and the performance evaluation parameter (PEC) were 1.83 and 1.75, respectively.

Smart transactive energy based approach for planning and scheduling in multi-looped microgrid distribution network across planning horizon

In this paper, an innovative transactive energy approach is proposed as viable option for coordinated distribution system planning across a certain horizon. The proposed approach is evaluated across a multi-looped (meshed) test system and is implemented with load growth having prosumers participating in the electrical market in transactive energy system aiming at evaluation on techno-economic basis. Apart from prosumer sensitivity analysis, evaluations have been carried across reducing total production cost of energy, reduction in per unit price, active power losses. Whereas improving voltage profile, cost of scheduling and consumer per kWh purchase and sales in comparison with traditional counterpart. The proposed framework includes optimization algorithm aiming at sources scheduling and IEEE 69 system for validation. The algorithm minimizes cost, maximizes energy efficiency, increases renewable energy mix and reduces consumers cost of energy purchase. Reduction of 51.44 % in cost of energy is achieved, whereas loss reduction of 12.6% is achieved. The comparison of IEEE 69-bus base case with the 10 %, 15% and 20% transactive energy applied with simulations to evaluate performance parameters that will directly benefit both prosumers and utility alike in-terms of low bills and further reduction of stress on the grid amid load growth across multiple years.

Modeling thermoelectric figure of merit of BiCuSeO oxyselenide ceramic using extreme learning machine computational method

BiCuSeO ceramic is an eco-friendly oxyselenide thermoelectric material that provides alternative directions for refrigeration and power generation through direct conversion between electrical and thermal energy. Aside from environmental friendliness of BiCuSeO ceramic, relatively cheap input materials, low toxicity, good thermal and chemical stability are among the unique features that contribute significantly to wider applicability of this material for energy conversion. The energy conversion efficiency of un-doped and pristine BiCuSeO thermoelectric material is very low while enhancement of its energy conversion efficiency has attracted serious attention from both experimental and theoretical perspectives thorough modulation doping, dual-vacancies creation and dual doping among others. This work models the thermoelectric figure of merit (energy conversion efficiency, ECE) of doped BiCuSeO based ceramics using extreme learning machine (ELM) with ionic radii and elemental concentrations predictors. ECE-SNELM model with sine activation function performs better than ECE-SGELM with sigmoid activation function using root mean square error (RMSE), correlation coefficient (CC) and mean absolute error (MAE) performance evaluation and assessment parameters with improvement of 20.61 %, 5.51 % and 5.77 %, respectively when computed using testing samples of BiCuSeO-based ceramics. Comparison of the developed ECE-SNELM and ECE-SGELM model with the existing machine learning (ML) based model shows that ECE-SNELM and ECE-SGELM model respectively outperforms ML (2022) model with improvement of 70.89 % and 35.8 % using RMSE metric. The developed ECE-T-SNELM model that includes temperature among its descriptors further outperforms the existing ML based model with improvement of 39.39 % on the basis of RMSE metric. The predictions of the developed models agree well with the measured values. The developed ECE-SNELM was further employed for investigating the behavior of BiCuSeO-based ceramics after dopant incorporation. The precision demonstrated by the developed ECE-SNELM and ECE-SGELM models in determining thermoelectric figure of merit of BiCuSeO-based ceramics is highly commendable and meritorious in strengthen energy conversion efficiency of the ceramics for addressing the global challenges of energy crisis.

Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works

BackgroundHeart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. ObjectivePropose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web interface was developed with the best model proposed in this study. MethodsFirst, the dataset used was from the Heart Disease Cleveland ICU, which was 918 patient records and 12 attributes. Therefore, the paper was composed of the following stages: Cleaning and Pre-processing; Describe data; Training and testing data; Cross validation; Calibration of models; and modelling and evaluation, also compare the different techniques proposed to predict heart disease using Stacking ensemble based on hyperparameters taking into account the performance evaluation parameters. ResultsStacking 1 (Logistic regression) with oversampling and AdaBoost-SVM with hyperparameter in the test obtained higher Accuracy (88.24%), and ROC Curve (92.00%), while too Stacking 1 (Logistic regression) with oversampling reached a better Precision (88.54%), but the AdaBoost-SVM algorithm using hyperparameter achieved a high value of Sensitivity (88.14%) and F1-Score (88.19%). ConclusionsImplementing 4 stacking models based on hyperparameters, it helps to make an early diagnosis of heart disease and greater precision, and decrease the quantity of deceases caused by it. Therefore, by using the combined method, an improvement in heart disease prediction was observed, surpassing the performance of the independent algorithms used.

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

Analysis of Performance Parameters of Engines with Spark Ignition with Variable Regulations of the Fuel-Injection System, Powered by E100 Fuel

Emission limits imposed on gas-powered vehicles, which have gradually been introduced by the European Commission, as well as the exhaustibility of the planet’s natural resources involve the need to search for alternative solutions and restore renewable fuel markets. In terms of ecology, using biofuels for powering spark-ignition engines has a positive impact on the natural environment. This kind of fuel, however, has a negative effect on the drive unit performance parameters, such as power and torque. This study presents test results of performance parameters of a spark-ignition engine fueled by E100 (pure ethanol). Parameters that have been analyzed are power, torque, exhaust emission components (oxygen, carbon dioxide, hydrocarbons), solid particles included in exhaust gases and emission of noise generated by the drive unit. The vehicle control system software adjustments have also been tested. Twelve regulations have been tested to evaluate performance parameters of a spark-ignition engine powered by E100 fuel. An analysis of the test results has confirmed the advisability of the introduction of control system software regulation. The aim of the work was to develop a model that will help select the appropriate software adjustment for an engine powered by E100 fuel. Due to the introduced emission restrictions, there is an important issue that concerns users of combustion vehicles. The research presented in the manuscript concerns a joint analysis of the engine’s fueling with alternative fuel and the introduction of changes to its software in order to reduce emissions.

Technical NEMA NU2-2018 Performance Assessment of Time-of-Flight-Integrated Digital PET-CT System.

Aim The objective of this study includes the NEMA (National Electrical Manufacturer Association) NU2-2018 performance evaluation of the uMIvista PET-CT (positron emission tomography-computed tomography) system. Methods The latest NEMA NU2-2018 guidelines have been followed for the evaluation of performance parameters of this PET-CT scanner: axial, tangential, and radial spatial resolution, sensitivity, counting losses, scatter, randomness, random and counting loss correction, image quality, time and energy resolution, image uniformity, and image registration alignment post installation of country first uMIvista PET-CT. Results The measured NEMA sensitivity of the uMIvista PET scanner was 12.053 cps/kBq. The spatial resolutions of the PET were measured as tangential, radial, and transaxial spatial resolutions at 10 mm, with 3.01 mm, 2.95 mm, and 2.93 mm, respectively; at 100 mm, with 3.17 mm, 3.42 mm, and 3.05 mm, respectively; and at 200 mm, with 3.65 mm, 4.54 mm, and 3.17 mm, respectively, at full-width half-maximum (FWHM); while at full-width tenths-maximum (FWTM), the values at 10 mm were 5.79 mm, 5.57 mm, and 5.69 mm, respectively, and at 100 mm were 5.59 mm, 5.96 mm, and 5.91 mm, respectively. The measured time-of-flight (TOF) timing resolution was 302.294 ps and the measured energy resolution was 11.76% with FWHM and FWTM. Conclusion The NEMA NU2-2018 performances of this TOF-integrated digital PET-CT system are extremely good in all parameters.

Flow characteristics of dispersed two-phase flows in an 8 × 8 rod bundle

Two-phase flows in rod bundles or tube bundles appear in heat transfer systems. The bundle-average (or one-dimensional) void fraction is a critical parameter in overall system design, performance evaluation, and safety assessment. Accurate bundle-average void fraction prediction requires a precise interfacial momentum transfer term formulated using the distribution parameter and drift velocity, two essential drift-flux parameters in the drift-flux model. The currently utilized modeling approach is based on the distribution parameter and drift velocity determined together through drift-flux plots. This results in possible compensation errors between the distribution parameter and drift velocity in the model validation process. The independent validation of the constitutive equations for the distribution parameter and drift velocity has been challenging due to experimental difficulty in measuring detailed two-phase flow structures in rod bundles. The present study proposes an approximation methodology to obtain subchannel average two-phase flow parameters using local two-phase flow data measured at two local points: the subchannel center and minimum gap center in a rod bundle. The distributions of subchannel average two-phase flow parameters are invaluable in benchmarking subchannel analysis codes. Comprehensive subchannel average void fraction and gas velocity mappings are given to discuss the effects of gas and liquid velocities, pressure, spacer grid, and developing length on the two-phase flow characteristics in the rod bundle. The bundle-average distribution parameter values calculated by subchannel average two-phase flow parameters are used for independent validation of the constitutive equations of the distribution parameter. The bundle-average drift velocity values back-calculated using the distribution parameter values are also used for independent validation of the constitutive equation of the drift velocity.

Dynamic convolution layer based optimization techniques for object classification and semantic segmentation

<p>Providing meaningful classification for each pixel in an image is a primary goal of computer vision, and the tasks of object classification and semantic segmentation are among the field’s greatest challenges. To improve object classification, this study presents a novel method that combines semantic segmentation with dynamic convolution layer-based optimization techniques. In the proposed method, a Refined Convolution Neural Network (R-CNN) is used, which uses non-extensive entropy to dynamically increase the size of its convolutional layers. The Common Objects in Context (COCO) dataset is used to assess the performance of the model. The model performs exceptionally well at different Intersections over Union (IoU) cutoffs, with average precision values of 40.1, 61.9, and 45.4, respectively, for Average Precision (AP), AP<sub>50</sub>, and AP<sub>75</sub>. These results demonstrate the model’s efficiency in discriminating between various image contents. Additionally, the model predicts an image’s outcome on average in just 0.901 s. The model has been proven to be superior through various performance evaluation parameters, showing an average mean precision of 91.78%. This study demonstrates the power of combining dynamic convolution layers with semantic segmentation to improve object classification accuracy, a key component in the development of computer vision applications.</p>

Performance investigation and evaluation of a low-temperature solar thermal energy storage system under dynamic weather conditions

Designed and operational parameters matter greatly in how successful an energy storage system performs. However, a current challenge in matching suitable these parameters lies in the poor understanding of such systems’ performance under dynamic weather conditions. In this paper, we investigated a phase change material (PCM) storage unit that is particularly aimed for poor-solar areas, and connected the unit to a flat plate solar collector to establish a complete solar thermal energy storage system (STESS). To optimize the system performance, different grades of solar radiation and outdoor ambient temperatures are introduced, and the effects of heat transfer fluid (HTF) flow rates and solar collecting areas are investigated numerically through multiple performance evaluation parameters. The results indicate that the solar collecting area has a significant effect on the performance of the STESS compared to that of the water flow rate. And different collecting areas of 1 m2, 1.5 m2, and 3 m2 with an optimal water flow rate of 0.06 m3/h are matched to different average daily solar radiation of 203.4 W/m2, 155.2 W/m2, 92.7 W/m2, respectively. Further for the heat release process, different heat release efficiencies from 88.32 % to 97.12 % with different energy consumption of the fan from 0.003 kWh to 0.063 kWh are obtained when the air flow rate is increased from 30 m3/h to 90 m3/h. One of the air flow rates of 60 m3/h is superior for the STESS considering higher heat release efficiency and lower energy consumption of the fan. The findings of this paper will contribute to optimizing the performance of a STESS in practical applications.

Performance evaluation of machine learning algorithms for Parkinson’s disease identification model

Though Medical sector is referred to as one of the most advance sector in the Country, there are some implications which shows disorientation of medical sector and early prediction of various ailments. There are some diseases which does not have any preventive measures and are incurable till date. Parkinson’s disease is one of those disease that is incurable till date and its clinical diagnosis requires early detection of symptoms which may vary according to age parameter. Parkinson’s disease is one of the incurable diseases which affects the Central Nervous System (CNS) of Human brain which leads to various symptoms like barely noticeable tremor on limbs, slowness in movement, improper balance and posture of body, Loss of automated movement and sometimes change in vocal frequency. With theadvent of Machine Learning (ML) and Artificial Intelligence (AI) technique are used for diagnosis of brain diseaseswhich is helping the doctor for early detection of the diseases.Considering the performance evaluation parameters, we have given equal importance to every performance measures used. The dataset for the Parkinson’s disease detection is taken from Oxford UCI Machine repository. Analysis and Comparative study show that all the algorithms can detect the person is affected by the Parkinson’s disease or not. Out of them random forest algorithm yielded highest performance measures.

Sentiment Analysis Using Machine Learning

In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms’ performance.

Prediction of ECG fiducial parameters from PPG signals for the analysis of cardiovascular diseases: A novel Gaussian process regression-based approach

PurposeCardiologists use 12-lead electrocardiograph (ECG) and prolonged continuous Holter monitoring to detect abnormalities in heart rhythm, which could diagnose or hint at different cardiac diseases. However, the availability of a costly ECG machine and the expertise to interpret ECG is suboptimal in our country. The current state of the art proposes mitigating the cost and easy diagnosis by physicians with the use of low cost photoplethysmograph (PPG), which will deliver ECG information in terms of ECG fiducial parameters. MethodsThe MIMIC-III matched paired datasets of PPG/Lead-II ECG signals with the six classes (right bundle branch block (RBBB), hypertension (HYPER), arrhythmia-atrial fibrillation (ARYTH-ATRFAB), dilated cardiomyopathy (DCM), complete heart block (CHB) and NORMAL) associated cardiovascular diseases (CVDs) syndromes of 30 patients with 5 minutes duration each were taken from Physionet. The proposed work tested by predicting ECG fiducial parameters (P, Q, R, S and T) through Gaussian process regression(GPR)-based predictor by using the PPG fiducial parameters with a training and testing ratio of 70:30. The performance was analyzed by different performance evaluation parameters e.g., RMSE, rRMSE, etc. ResultsAlthough the shape of the ECG may differ in various ways from that of the normal ECG, the methodology can predict the ECG fiducials from PPG for a variety of CVD patients with a performance accuracy of 95–99% by evaluating through seven performance metrics. ConclusionThe ECG fiducial parameters can be used to analyze CVDs captured from a low-cost, handy, skin-compatible continuous and non-invasive optical recording device well known as a PPG sensor.

Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques

Aspect-based sentiment analysis (ABSA) is the subfield of natural language processing that deals with essentially splitting data into aspects and finally extracting the sentiment polarity as positive, negative, or neutral. ABSA has been widely investigated and developed for many resource-rich languages such as English and French. However, little work has been done on indigenous African languages like Afaan Oromoo both at the document and sentence levels. In this paper, ABSA for Afaan Oromoo movie reviews was investigated and developed. To achieve the proposed objective, 2800 Afaan Oromoo movie reviews were collected from YouTube using YouTube Data API. Following the data preprocessing, predetermined aspects of the Afaan Oromoo movie were extracted and labeled into positive or negative aspects by domain experts. For implementation, different machine learning algorithms including random forest, logistic regression, SVM, and multinomial naïve Bayes in combination with BoW and TF-IDF were applied. To test and measure the proposed system, accuracy, precision, recall, and f1-score were used. In the case of random forest, the accuracy obtained in combination with both BoW and TF-IDF was 88%. Using the SVM, the accuracy generated with BoW and TF-IDF was 88% and 87%, respectively. Applying logistic regression, the accuracy generated with both BoW and TF-IDF was 87%. Using multinomial naïve Bayes, the accuracy generated in combination with both BoW and TF-IDF was 88%. To improve the optimal performance evaluation parameters, different hyperparameter tuning settings were applied. The implementation result shows that the optimal values of models’ performance evaluation parameters were generated using different hyperparameter tuning settings.

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

<span lang="EN-US">Gait recognition has become a developing technology in various security, industrial, medical, and military applications. This paper proposed a deep convolutional neural network (CNN) model to authenticate humans via their walking style. The proposed model has been applied to two commonly used standardized datasets, Chinese Academy of Sciences (CASIA) and Osaka University-Institute of Scientific and Industrial Research (OU-ISIR). After the silhouette images have been isolated from the gait image datasets, their features have been extracted using the proposed deep CNN and the traditional ones, including AlexNet, Inception (GoogleNet), VGGNet, ResNet50, and Xception. The best features were selected using genetic, grey wolf optimizer (GWO), particle swarm optimizer (PSO), and chi-square algorithms. Finally, recognize the selected features using the proposed deep neural network (DNN). Several performance evaluation parameters have been estimated to evaluate the model’s quality, including accuracy, specificity, sensitivity, false negative rate (FNR), and training time. Experiments have demonstrated that the suggested framework with a genetic feature selector outperforms previous selectors and recent research, scoring accuracy values of 99.46% and 99.09% for evaluating the CASIA and OU-ISIR datasets, respectively, in low time (19 seconds for training).</span>