Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

Automatic deception recognition has received considerable atten-tion from the machine learning community due to recent research on its vast application to social media,interviews,law enforcement,and the mil-itary.Video analysis-based techniques for automated deception detection have received increasing interest.This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection(SAPFF-DLADD)model for analyzing facial cues.Ini-tially,the input video is separated into a set of video frames.Then,the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features.The long short-term memory(LSTM)model is exploited for deception detection and classification.In the final stage,the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model,showing the novelty of the work.The experimental validation of the SAPFF-DLADD model is tested using the Miami University Deception Detection Database(MU3D),a database comprised of two classes,namely,truth and deception.An extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches,with a higher accuracy of 99%.

Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole world.Since then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease.Several researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the coronavirus.This paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose COVID-19.Six different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL methods.These approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,Brazil.This data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive cases.The dataset includes 18 attributes that characterize COVID-19.We used accuracy,f1-score,recall and precision to evaluate the different developed systems.The obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based models.Among all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,respectively.Besides these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%accuracies.The LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.

Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

IoT devices rely on authentication mechanisms to render secure message exchange.During data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT devices.The application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped agreement.This paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT devices.PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication.An IoT network gathers information of interest from multiple cluster members selected by the proposed framework.In addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform.Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance ratio.By enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely used.Froma couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial success.However,they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction.In this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition.The proposed architecture consists of five steps.In the first step,data augmentation is performed to increase the numbers of training samples.In the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning.Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm.The best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification results.The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village.The proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,respectively.Acomparison with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness.

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause accidents.As typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network environments.Machine learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy.This article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT platform.The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform.The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain this.Next,to detect intrusions,the WNN model is utilized.At last,theWNNparameters are optimally modified by the use of BSA.Awidespread experiment is performed to depict the better performance of the BSAWNNID technique.The resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to employ are two main questions.In this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance.The suggested method is based on a deep learning snapshot ensemble method of the Transformer model.To examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price forecasting.Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods.More precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.

Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

SoftwareDefined Networks(SDN)introduced better network management by decoupling control and data plane.However,communication reliability is the desired property in computer networks.The frequency of communication link failure degrades network performance,and service disruptions are likely to occur.Emerging network applications,such as delaysensitive applications,suffer packet loss with higher Round Trip Time(RTT).Several failure recovery schemes have been proposed to address link failure recovery issues in SDN.However,these schemes have various weaknesses,which may not always guarantee service availability.Communication paths differ in their roles;some paths are critical because of the higher frequency usage.Other paths frequently share links between primary and backup.Rerouting the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system throughput.Therefore,there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link failure.This paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT,increasing network throughput while minimizing post-recovery congestion.The affected flows are redirected through a path with minimal risk of failure,while Bayesian probability is used to predict post-recovery congestion.Both the former and latter path with a minimal score is chosen.The simulation results improved throughput by(45%),reduced packet losses(87%),and lowered RTT(89%)compared to benchmarking works.

A Component Selection Framework of Cohesion and Coupling Metrics

Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality,increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper,proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software sys-tem.This cohesion measurement has been taken into two parameters for analyz-ing the result of complexity,with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.Thefinal process of coupling and cohesion,the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in compo-nent-based software engineering.

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

Price Prediction of Seasonal Items Using Time Series Analysis

The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too weak.In this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi networks.An association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance.To implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator.The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.