Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

Breast cancer resistance protein(BCRP)is an important resistance protein that significantly impacts anticancer drug discovery,treatment,and rehabilitation.Early identification of BCRP substrates is quite a challenging task.This study aims to predict early substrate structure,which can help to optimize anticancer drug development and clinical diagnosis.For this study,a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning(TL)for automatic deep feature(DF)extraction followed by classification with linear discriminant analysis algorithm(TLRNDF-LDA).This study utilized structural fingerprints,which are exploited by DF contrary to conventional molecular descriptors.The proposed in silico model achieved an outstanding accuracy performance of 98.56%on test data compared to other state-of-the-art approaches using standard quality measures.Furthermore,the model’s efficacy is validated via a statistical analysisANOVAtest.It is demonstrated that the developedmodel can be used effectively for early prediction of the substrate structure.The pipeline of this study is flexible and can be extended for in vitro assessment efficacy of anticancer drug response,identification of BCRP functions in transport experiments,and prediction of prostate or lung cancer cell lines.

Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

An effective communication application necessitates the cancellation of Impulsive Noise(IN)from Orthogonal Frequency Division Multiplexing(OFDM),which is widely used for wireless applications due to its higher data rate and greater spectral efficiency.The OFDM system is typically corrupted by Impulsive Noise,which is an unwanted short-duration pulse with random amplitude and duration.Impulsive noise is created by humans and has non-Gaussian characteristics,causing problems in communication systems such as high capacity loss and poor error rate performance.Several techniques have been introduced in the literature to solve this type of problem,but they still have many issues that affect the performance of the presented methods.As a result,developing a new hybridization-based method is critical for accurate method performance.In this paper,we present a hybrid of a state space adaptive filter and an information coding technique for cancelling impulsive noise from OFDM.The proposed method is also compared to Least Mean Square(LMS),Normalized Least Mean Square(NLMS),and Recursive Least Square(RLS)adaptive filters.It has also been tested using the binary phase-shift keyed(BPSK),four quadrature amplitude modulation(QAM),sixteen QAM,and thirty-two QAM modulation techniques.Bit error Rate(BER)simulations are used to evaluate system performance,and improved performance is obtained.Furthermore,the proposed method is more effective than recent methods.

Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to civilian.Despite the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring region.The clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision avoidance.But clustering protocol has several challenges.In a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more transmission.This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in WSN.The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack prey.The presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network lifespan.The JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain this.In addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the work.The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy.The comparison study shows the significance of the JSOUCPMHP technique over other models.

Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

Human Interaction Recognition in Surveillance Videos Using Hybrid Deep Learning and Machine Learning Models

Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.

Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases

Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects.However,multiple major eye diseases,such as DR,GLC,and AMD,could not be detected simultaneously by computer-aided systems to date.There were just high-performance-outcome researches on a pair of healthy and eye-diseased group,besides of four categories of fundus image classification.To have a better knowledge of multi-categorical classification of fundus photographs,we used optimal residual deep neural networks and effective image preprocessing techniques,such as shrinking the region of interest,iso-luminance plane contrast-limited adaptive histogram equalization,and data augmentation.Applying these to the classification of three eye diseases from currently available public datasets,we achieved peak and average accuracies of 91.16%and 85.79%,respectively.The specificities for images from the eyes of healthy,GLC,AMD,and DR patients were 90.06%,99.63%,99.82%,and 91.90%,respectively.The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss.This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications.

Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

While the usage of digital ocular fundus image has been widespread in ophthalmology practice,the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly.We explored a robust deep learning system that detects three major ocular diseases:diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD).The proposed method is composed of two steps.First,an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance,a technique that has not been explored previously.Second,the transfer learning technique is used with various convolutional neural networks(CNN)models that automatically learn a thousand features in the digital retinal image,and are based on those features for diagnosing eye diseases.Comparison performance of many models is conducted to find the optimal model which fits with fundus classification.Among the different CNN models,DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99.Furthermore,the corresponding specificities for healthy,DR,GLC,andAMDpatients are found to be 89.52%,96.69%,89.58%,and 100%,respectively.These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools.

Recognition and Tracking of Objects in a Clustered Remote Scene Environment

Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision.Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision.This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shapemoving objects while accommodating the shift and scale invariances that the object may encounter.The first part uses the Maximum Average Correlation Height(MACH)filter for object recognition and determines the bounding box coordinates.In case the correlation based MACH filter fails,the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e.,affine scale invariant feature transform(ASIFT).ASIFT is used to accommodate object shift and scale object variations.ASIFT extracts certain features from the object of interest,providing invariance in up to six affine parameters,namely translation(two parameters),zoom,rotation and two camera axis orientations.However,in this paper,only the shift and scale invariances are used.The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient(APG)technique to periodically update the coordinates of the object encapsulated in the bounding box.At the end,a comparison of the proposed algorithm with other stateof-the-art tracking algorithms has been presented,which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.

Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning

This paper focuses on detecting diseased signals and arrhythmias classification into two classes:ventricular tachycardia and premature ventricular contraction.The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person.The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency(IF).Once a signal taken from a patient is detected,then the classifier takes that signal as input and classifies the target disease by predicting the class label.While applying the classifier,templates are designed separately for ventricular tachycardia and premature ventricular contraction.Similarities of a given signal with both the templates are computed in the spectral domain.The empirical analysis reveals precisions for the detector and the applied classifier are 100%and 77.27%,respectively.Moreover,instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08–0.6 Hz.This indicates a serious loss of high-frequency contents in the spectrum,implying that the heart’s overall activity is slowed down.This study may help medical practitioners in detecting the heart disease type based on signal analysis.

An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique.

Evaluation of Pencil Lead Based Electrodes for Electrocardiogram Monitoring in Hot Spring

Electrocardiogram(ECG)electrodes are conductive pads applied to the skin to measure cardiac activity.Ag/AgCl electrodes are the commercial product which widely used to obtain ECGs.When monitoring the ECG in a hot spring,Ag/AgCl electrodes must be waterproofed;however,this is time-consuming,and the adhesive may tear the skin on removal.For solving the problem,we developed the carbon pencil lead(CPL)electrodes for use in hot springs.Both CPL and Ag/AgCl electrodes were connected to ECG100C’s cables.The Performance was evaluated in three conditions as following:hot spring water with and without bubble,and in cold water.In each environment,the procedure was followed by three different protocols that are recording from the dry condition,hot spring water immersion with and without movement,post hot spring water condition.Under dry and wet conditions,both electrodes can obtain the waveform of the ECG signal in which all PQRST waves were identifiable.Nevertheless,the signal quality of both types of electrodes was different in water immersion with and without movement.The overall morphology obtained by Ag/AgCl electrodes was unstable higher than that of CPL electrodes in immersion without movement condition.The CPL electrodes provided better ECG waveform quality compared to Ag/AgCl electrodes in which the ECG signal had high waveforms distortion in water immersion with movement condition.

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the grids.The TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them.At the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)models.Though some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,etc.into account.In this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in TEM.The proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence rate.Moreover,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading systems.In order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome.The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

Improved Shark Smell Optimization Algorithm for Human Action Recognition

Human Action Recognition(HAR)in uncontrolled environments targets to recognition of different actions froma video.An effective HAR model can be employed for an application like human-computer interaction,health care,person tracking,and video surveillance.Machine Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature fusion.The accuracy and effectiveness of these models continue to be the biggest challenge in this field.In this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted features.This proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search space.The proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of FV.Once local optimal features are selected,a global search is conducted to further optimize these features.The proposed iSSO algorithm is employed on nine(9)selected CNN models.These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition.To evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected.

Data Augmentation Using Contour Image for Convolutional Neural Network

With the development of artificial intelligence-related technologies such as deep learning,various organizations,including the government,are making various efforts to generate and manage big data for use in artificial intelligence.However,it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage.There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology.Therefore,this study proposes a mixed contour data augmentation technique,which is a data augmentation technique using contour images,to solve a problem caused by a lack of data.ResNet,a famous convolutional neural network(CNN)architecture,and CIFAR-10,a benchmark data set,are used for experimental performance evaluation to prove the superiority of the proposed method.And to prove that high performance improvement can be achieved even with a small training dataset,the ratio of the training dataset was divided into 70%,50%,and 30%for comparative analysis.As a result of applying the mixed contour data augmentation technique,it was possible to achieve a classification accuracy improvement of up to 4.64%and high accuracy even with a small amount of data set.In addition,it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets.

A Universal Activation Function for Deep Learning

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning ability.Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity.Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process.However,it takes a lot of time and effort for researchers to use the existing activation function in their research.Therefore,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural networks.UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters.The famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this study.We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is applied.In addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of theUA.The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

The coronavirus(COVID19),also known as the novel coronavirus,first appeared in December 2019 in Wuhan,China.After that,it quickly spread throughout the world and became a disease.It has significantly impacted our everyday lives,the national and international economies,and public health.However,early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system.Clinical radiologists primarily use chest X-rays,and computerized tomography(CT)scans to test for pneumonia infection.We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study.We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization.We begin by extracting standard features such as discrete wavelet transforms(DWT),discrete cosine transforms(DCT),and dominant rotated local binary patterns(DRLBP).In addition,we extracted Shanon Entropy and Kurtosis features.In the following step,a Max-Covariance-based maximization approach for feature fusion is proposed.The fused features are optimized in the preliminary phase using Particle Swarm Optimization(PSO)and the ELM fitness function.For final prediction,PSO is used to obtain robust features,which are then implanted in a Support Vector Data Description(SVDD)classifier.The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients.These images are from the Radiopaedia website.For the proposed scheme,the fusion and selection process accuracy is 88.6%and 93.1%,respectively.A detailed analysis is conducted,which supports the proposed system efficiency.

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image generation.This system consists of two components.The first component contains two processes,encryption process,and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,respectively.The results were obtained using the bit error rate(BER)metric.These results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.