Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and ...Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.展开更多
Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and h...Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and health,but is challenging due to the unique characteristics of EEG and lack of standardized protocols.This study aims to develop and compare 18 machine learning models using Automated Machine Learning(autoML)technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification.The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning.The data is obtained from neonates at post-menstrual age 37±05 weeks.352530-s EEG segments from 19 infants are used to train and test the proposed models.There are twelve time and frequency domain features extracted from each channel.Each model receives the common features of nine channels as an input vector of size 108.Each model’s performance was evaluated based on a variety of evaluation metrics.The maximum mean accuracy of 84.78%and kappa of 69.63%has been obtained by the AutoML-based Random Forest estimator.This is the highest accuracy for EEG-based sleep-wake classification,until now.While,for the AutoML-based Adaboost Random Forest model,accuracy and kappa were 84.59%and 69.24%,respectively.High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates.展开更多
Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and e...Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.展开更多
A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancero...A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.展开更多
Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diab...Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.展开更多
A compact,reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this work.It was achieved by designi...A compact,reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this work.It was achieved by designing a Th-Shaped frequency reconfigurable multi-band microstrip planar antenna,based on use of a single switch within the radiating structure of the antenna.Three frequency bands(i.e.,2007–2501 MHz,3660–3983MHz and 9341–1046 MHz)can be operated with the switch in the ON switch state.In the OFF state of the switch,the antenna operates within the 2577–3280MHz and 9379–1033MHz Bands.The proposed antenna shows an acceptable input impedance match with Voltage Standing Wave Ratio(VSWR)less than 1.2.The peak radiation efficiency of the antenna is 82%.A reasonable gain is obtained from 1.22 to 3.31 dB within the operating bands is achieved.The proposed antenna supports UniversalMobile Telecommunication System(UMTS)-1920 to 2170 MHz,Worldwide Interoperability and Microwave Access(WiMAX)/Wireless Broadband/(Long Term Evolution)LTE2500–2500 to 2690 MHz,Fifth Generation(5G)-2500/3500 MHz,Wireless Fidelity(Wi-Fi)/Bluetooth-2400 to 2480 MHz,and Satellite communication applications in X-Band-8000 to 12000 MHz.The overall planar dimension of the proposed antenna is 40×20mm2.The antennawas designed,along with the parametric study,using Electromagnetic(EM)simulation tool.The antenna prototype is fabricated for experimental validation with the simulated results.The proposed antenna is low profile,tunable,lightweight,cheap to fabricate and highly efficient and hence is deemed suitable for use in modern wireless communication electronic devices.展开更多
The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is consider...The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.展开更多
The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intellige...The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.展开更多
This paper presents,a novel cactus shaped frequency reconfigurable antenna for sub 10 GHz wireless applications.PIN diode is utilized as an electrical switch to achieve reconfigurability,enabling operation in four dif...This paper presents,a novel cactus shaped frequency reconfigurable antenna for sub 10 GHz wireless applications.PIN diode is utilized as an electrical switch to achieve reconfigurability,enabling operation in four different frequency ranges.In the switch ON state mode,the antenna supports 2177-3431 and 6301-8467 MHz ranges.Alternatively,the antenna resonates within 2329-3431 and 4951-6718 MHz while in the OFF state mode.Radiation efficiency values,ranging from 68%to 84%,and gain values,ranging from 1.6 to 4 dB,in the operating frequency bands.the proposed antenna satisfy the practical requirements and expectations.The overall planner dimensions of the proposed antenna model is 40×21 mm^(2).Moreover,the measurement results from the prototype support the simulation results.Based on the frequency ranges supported by the antenna,it can be used for multiple wireless standards and services,including Worldwide interoperability and Microwave Access(WiMAX),Wireless Fidelity(Wi-Fi),Bluetooth,Long Term Evolution(LTE)and satellite communications.This increases its applicability for use in mobile terminals.展开更多
The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is record...The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare application.In this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modified version of mel fre-quency cepstral coefficients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ficient,hurst exponent,and root mean square.Several state-of-the-art classifiers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features.Comparison-based analyses of these classifiers have also been considered based on total classi-fication accuracy,prediction speed(observations/sec),and processing time.The proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG channels.SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.展开更多
Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform thei...Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.展开更多
The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisi...The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.展开更多
Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design ...Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV system.The electrode placement locations are modified under a 10–20 standard system.The fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and cost.First,the data is recorded from 20 subjects who used the EEG Headset,and signals were recorded.Secondly,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide changing.The raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,thesefindings have been compared to previously reported studies.We concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 percent.We also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.展开更多
Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.W...Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.When the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is difficult.Using several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early stages.As a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early on.The dataset was picked from the University of California,Irvine repository.RF’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and G-measure.RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is 72.1739.The widely acknowledged result of this work can be used as a starting point for subsequent research.As a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.展开更多
Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body.It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’...Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body.It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and platelets.Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia cells.Furthermore,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for leukemia.However,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and dedication.Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations.They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells.However,these systems are more efficient,reliable,and fast than previous manual diagnosing methods.However,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity.In this paper,we have proposed a robust automated system to diagnose leukemia and its sub-types.We have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better performance.We have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.展开更多
Climate anomalies can cause natural disasters such as severe fires and floods on peatlands in South Sumatra.Factors that affect the natural disasters on peatlands include rainfall,groundwater level,and soil moisture.T...Climate anomalies can cause natural disasters such as severe fires and floods on peatlands in South Sumatra.Factors that affect the natural disasters on peatlands include rainfall,groundwater level,and soil moisture.This paper aims to study the effect of the climate anomalies in 2019 and 2020 and effects of these influencing factors on peatlands in South Sumatra.The data used in this study was derived from insitu measurement at two SESAME’s measurement stations in the study area.The results indicate that in the 2019 dry season,the rainfall was minimal,the lowest groundwater table depth was-1.14 m and the lowest soil moisture was 3.4%.In the 2020 dry season,rainfall was above the monthly average of 100 mm,the lowest groundwater level was-0.44 m,and the lowest soil moisture was 26.64%.There is also a strong correlation between soil moisture and groundwater table depth.The correlation between the two is stronger when there is less rainfall.展开更多
In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illn...In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.展开更多
BACKGROUND Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019(COVID-19)pandemic.The pandemic affected,and in many cases t...BACKGROUND Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019(COVID-19)pandemic.The pandemic affected,and in many cases threatened,the health and lives of millions of people across the globe and within the first year,global prevalence of anxiety and depression increased by 25%with the greatest influx in places highly affected by COVID-19.AIM To explore the psychological impact of the pandemic and resultant restrictions in different countries using an opportunistic sample and online questionnaire in different phases of the pandemic.METHODS A repeated,cross-sectional online international survey of adults,16 years and above,was carried out in 10 countries(United Kingdom,India,Canada,Bangladesh,Ukraine,Hong Kong,Pakistan,Egypt,Bahrain,Saudi Arabia).The online questionnaire was based on published approaches to understand the psychological impact of COVID-19 and the resultant restrictions.Five standardised measures were included to explore levels of depression[patient health questionnaire(PHQ-9)],anxiety[generalized anxiety disorder(GAD)assessment],impact of trauma[the impact of events scale-revised(IES-R)],loneliness(a brief loneliness scale),and social support(The Multidimensional Scale of Perceived Social support).RESULTS There were two rounds of the online survey in 10 countries with 42866 participants in Round 1 and 92260 in Round 2.The largest number of participants recruited from the United Kingdom(112985 overall).The majority of participants reported receiving no support from mental health services throughout the pandemic.This study found that the daily cumulative COVID-19 cases had a statistically significant effect on PHQ-9,GAD-7,and IES-R scores.These scores significantly increased in the second round of surveys with the ordinary least squares regression results with regression discontinuity design specification(to control lockdown effects)confirming these results.The study findings imply that participants’mental health worsened with high cumulative COVID-19 cases.CONCLUSION Whist we are still living through the impact of COVID-19,this paper focuses on its impact on mental health,discusses the possible consequences and future implications.This study revealed that daily cumulative COVID-19 cases have a significant impact on depression,anxiety,and trauma.Increasing cumulative cases influenced and impacted education,employment,socialization and finances,to name but a few.Building a database of global evidence will allow for future planning of pandemics,particularly the impact on mental health of populations considering the cultural differences.展开更多
基金the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,for funding this work under the Research Groups Funding Program Grant Code Number(NU/RG/SERC/12/43).
文摘Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.
文摘Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system.EEG based neonatal sleep staging provides valuable information about an infant’s growth and health,but is challenging due to the unique characteristics of EEG and lack of standardized protocols.This study aims to develop and compare 18 machine learning models using Automated Machine Learning(autoML)technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification.The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning.The data is obtained from neonates at post-menstrual age 37±05 weeks.352530-s EEG segments from 19 infants are used to train and test the proposed models.There are twelve time and frequency domain features extracted from each channel.Each model receives the common features of nine channels as an input vector of size 108.Each model’s performance was evaluated based on a variety of evaluation metrics.The maximum mean accuracy of 84.78%and kappa of 69.63%has been obtained by the AutoML-based Random Forest estimator.This is the highest accuracy for EEG-based sleep-wake classification,until now.While,for the AutoML-based Adaboost Random Forest model,accuracy and kappa were 84.59%and 69.24%,respectively.High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia。
文摘Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.
基金supported by the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,Project Number(NU/DRP/SERC/12/7).
文摘A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the Institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.
基金the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,for funding this work under the Research Collaborations funding program Grant Code Number(NU/RC/SERC//11/5).
文摘A compact,reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this work.It was achieved by designing a Th-Shaped frequency reconfigurable multi-band microstrip planar antenna,based on use of a single switch within the radiating structure of the antenna.Three frequency bands(i.e.,2007–2501 MHz,3660–3983MHz and 9341–1046 MHz)can be operated with the switch in the ON switch state.In the OFF state of the switch,the antenna operates within the 2577–3280MHz and 9379–1033MHz Bands.The proposed antenna shows an acceptable input impedance match with Voltage Standing Wave Ratio(VSWR)less than 1.2.The peak radiation efficiency of the antenna is 82%.A reasonable gain is obtained from 1.22 to 3.31 dB within the operating bands is achieved.The proposed antenna supports UniversalMobile Telecommunication System(UMTS)-1920 to 2170 MHz,Worldwide Interoperability and Microwave Access(WiMAX)/Wireless Broadband/(Long Term Evolution)LTE2500–2500 to 2690 MHz,Fifth Generation(5G)-2500/3500 MHz,Wireless Fidelity(Wi-Fi)/Bluetooth-2400 to 2480 MHz,and Satellite communication applications in X-Band-8000 to 12000 MHz.The overall planar dimension of the proposed antenna is 40×20mm2.The antennawas designed,along with the parametric study,using Electromagnetic(EM)simulation tool.The antenna prototype is fabricated for experimental validation with the simulated results.The proposed antenna is low profile,tunable,lightweight,cheap to fabricate and highly efficient and hence is deemed suitable for use in modern wireless communication electronic devices.
基金funded by the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,Grant Number NU/MID/18/035.
文摘The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.
基金The authors gratefully acknowledge the Deanship of Scientific Research at Najran University in the Kingdom of Saudi Arabia for funding this work through the Research Groups funding program with the Grant Code Number(NU/RG/SERC/11/7).
文摘The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.
基金support from the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,for funding this work under the research groups funding program Grant code number(NU/RG/SERC/11/3).
文摘This paper presents,a novel cactus shaped frequency reconfigurable antenna for sub 10 GHz wireless applications.PIN diode is utilized as an electrical switch to achieve reconfigurability,enabling operation in four different frequency ranges.In the switch ON state mode,the antenna supports 2177-3431 and 6301-8467 MHz ranges.Alternatively,the antenna resonates within 2329-3431 and 4951-6718 MHz while in the OFF state mode.Radiation efficiency values,ranging from 68%to 84%,and gain values,ranging from 1.6 to 4 dB,in the operating frequency bands.the proposed antenna satisfy the practical requirements and expectations.The overall planner dimensions of the proposed antenna model is 40×21 mm^(2).Moreover,the measurement results from the prototype support the simulation results.Based on the frequency ranges supported by the antenna,it can be used for multiple wireless standards and services,including Worldwide interoperability and Microwave Access(WiMAX),Wireless Fidelity(Wi-Fi),Bluetooth,Long Term Evolution(LTE)and satellite communications.This increases its applicability for use in mobile terminals.
基金support from the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,for funding this work under the research groups funding program Grant Code Number(NU/RG/SERC/11/3).
文摘The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare application.In this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modified version of mel fre-quency cepstral coefficients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ficient,hurst exponent,and root mean square.Several state-of-the-art classifiers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features.Comparison-based analyses of these classifiers have also been considered based on total classi-fication accuracy,prediction speed(observations/sec),and processing time.The proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG channels.SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.
基金funded this work(DSR),King Abdulaziz University,Jeddah,Saudi Arabia,under grant no.(RG-18-130-43).
文摘Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV system.The electrode placement locations are modified under a 10–20 standard system.The fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and cost.First,the data is recorded from 20 subjects who used the EEG Headset,and signals were recorded.Secondly,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide changing.The raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,thesefindings have been compared to previously reported studies.We concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 percent.We also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.
基金the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research at Najran University,Kingdom of Saudi Arabiathe support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.When the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is difficult.Using several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early stages.As a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early on.The dataset was picked from the University of California,Irvine repository.RF’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and G-measure.RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is 72.1739.The widely acknowledged result of this work can be used as a starting point for subsequent research.As a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.
基金The authors acknowledge the support from the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,for funding this work under theResearch Collaboration funding program grant code number(NU/RC/SERC/11/7).
文摘Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body.It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and platelets.Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia cells.Furthermore,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for leukemia.However,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and dedication.Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations.They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells.However,these systems are more efficient,reliable,and fast than previous manual diagnosing methods.However,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity.In this paper,we have proposed a robust automated system to diagnose leukemia and its sub-types.We have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better performance.We have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.
文摘Climate anomalies can cause natural disasters such as severe fires and floods on peatlands in South Sumatra.Factors that affect the natural disasters on peatlands include rainfall,groundwater level,and soil moisture.This paper aims to study the effect of the climate anomalies in 2019 and 2020 and effects of these influencing factors on peatlands in South Sumatra.The data used in this study was derived from insitu measurement at two SESAME’s measurement stations in the study area.The results indicate that in the 2019 dry season,the rainfall was minimal,the lowest groundwater table depth was-1.14 m and the lowest soil moisture was 3.4%.In the 2020 dry season,rainfall was above the monthly average of 100 mm,the lowest groundwater level was-0.44 m,and the lowest soil moisture was 26.64%.There is also a strong correlation between soil moisture and groundwater table depth.The correlation between the two is stronger when there is less rainfall.
基金support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.
基金Supported by MRC Global Health Research Program,No.MR.N006267/1.
文摘BACKGROUND Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019(COVID-19)pandemic.The pandemic affected,and in many cases threatened,the health and lives of millions of people across the globe and within the first year,global prevalence of anxiety and depression increased by 25%with the greatest influx in places highly affected by COVID-19.AIM To explore the psychological impact of the pandemic and resultant restrictions in different countries using an opportunistic sample and online questionnaire in different phases of the pandemic.METHODS A repeated,cross-sectional online international survey of adults,16 years and above,was carried out in 10 countries(United Kingdom,India,Canada,Bangladesh,Ukraine,Hong Kong,Pakistan,Egypt,Bahrain,Saudi Arabia).The online questionnaire was based on published approaches to understand the psychological impact of COVID-19 and the resultant restrictions.Five standardised measures were included to explore levels of depression[patient health questionnaire(PHQ-9)],anxiety[generalized anxiety disorder(GAD)assessment],impact of trauma[the impact of events scale-revised(IES-R)],loneliness(a brief loneliness scale),and social support(The Multidimensional Scale of Perceived Social support).RESULTS There were two rounds of the online survey in 10 countries with 42866 participants in Round 1 and 92260 in Round 2.The largest number of participants recruited from the United Kingdom(112985 overall).The majority of participants reported receiving no support from mental health services throughout the pandemic.This study found that the daily cumulative COVID-19 cases had a statistically significant effect on PHQ-9,GAD-7,and IES-R scores.These scores significantly increased in the second round of surveys with the ordinary least squares regression results with regression discontinuity design specification(to control lockdown effects)confirming these results.The study findings imply that participants’mental health worsened with high cumulative COVID-19 cases.CONCLUSION Whist we are still living through the impact of COVID-19,this paper focuses on its impact on mental health,discusses the possible consequences and future implications.This study revealed that daily cumulative COVID-19 cases have a significant impact on depression,anxiety,and trauma.Increasing cumulative cases influenced and impacted education,employment,socialization and finances,to name but a few.Building a database of global evidence will allow for future planning of pandemics,particularly the impact on mental health of populations considering the cultural differences.
