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.

Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

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.

Security Concerns with IoT Routing: A Review of Attacks, Countermeasures, and Future Prospects

Today’s Internet of Things (IoT) application domains are widely distributed, which exposes them to several security risks and assaults, especially when data is being transferred between endpoints with constrained resources and the backbone network. Numerous researchers have put a lot of effort into addressing routing protocol security vulnerabilities, particularly regarding IoT RPL-based networks. Despite multiple studies on the security of IoT routing protocols, routing attacks remain a major focus of ongoing research in IoT contexts. This paper examines the different types of routing attacks, how they affect Internet of Things networks, and how to mitigate them. Then, it provides an overview of recently published work on routing threats, primarily focusing on countermeasures, highlighting noteworthy security contributions, and drawing conclusions. Consequently, it achieves the study’s main objectives by summarizing intriguing current research trends in IoT routing security, pointing out knowledge gaps in this field, and suggesting directions and recommendations for future research on IoT routing security.

Research on the Application of PBL+SPOC Blended Teaching Model in Probability and Statistics Course

To cultivate talents with an exploratory spirit and practical skills in the era of information technology,it is imperative to reform teaching methods and approaches.In the teaching process of the Probability and Statistics course,an application-oriented blended teaching model combining problem-based learning and small private online course was explored.By organizing and implementing online and offline teaching activities based on problem-based learning,a multidimensional process-oriented learning assessment system was established.Practice has shown that this model can effectively enhance classroom teaching effectiveness,benefiting the improvement of students’overall skills and mathematical literacy.

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Dynamical Interaction Between Information and Disease Spreading in Populations of Moving Agents

Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of service(QoS)requirements.The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data.Generally,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers.Fog devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task.In addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more complicated.In this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data volume.Compared with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the algorithm.The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption.Therein,latency and execution time is insensitive to the number of sensors.

Segmentation and Classification of Stomach Abnormalities Using Deep Learning

An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep learning.The first bleeding region is segmented using a hybrid approach.The threshold is applied to each channel extracted from the original RGB image.Later,all channels are merged through mutual information and pixel-based techniques.As a result,the image is segmented.Texture and deep learning features are extracted in the proposed classification task.The transfer learning(TL)approach is used for the extraction of deep features.The Local Binary Pattern(LBP)method is used for texture features.Later,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors.The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier.The experimental process is evaluated on the basis of two datasets:Private and KVASIR.The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set.It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

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.

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.

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.

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.

Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems

5G technology can greatly improve spectral efficiency(SE)and throughput of wireless communications.In this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge antennas and user equipment(UE).However,the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency(EE).In this regard,this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency.The research work is based on theWyner model of uplink(UL)and downlink(DL)transmission under the multi-cell model scenario.The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs,while the approximation method based on the logarithmic function is used for optimization.In this paper,we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput.The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions.It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.

Securing Consumer Internet of Things for Botnet Attacks: Deep Learning Approach

DDoS attacks in the Internet of Things(IoT)technology have increased significantly due to its spread adoption in different industrial domains.The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments.Conspicuously,an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory(BLRNN)is presented using a unique Deep Learning(DL)technique.For text identification and translation of attack data segments into tokenized form,word embedding is employed.The performance analysis of the presented technique is performed in comparison to the state-of-the-art DL techniques.Specifically,Accuracy(98.4%),Specificity(98.7%),Sensitivity(99.0%),F-measure(99.0%)and Data loss(92.36%)of the presented BLRNN detection model are determined for identifying 4 attacks over Botnet(Mirai).The results show that,although adding cost to each epoch and increasing computation delay,the bidirectional strategy is more superior technique model over different data instances.

Design of Intelligent Drunk Driving Detection System Based on Internet of Things

In recent years,with the rapid development of China’s economy and the continuous improvement of people’s living standards,the number of motor vehicles and the number of drivers in the country have grown rapidly.Due to the increase in the number of vehicles and the number of motorists,the traffic accident rate is increasing,causing serious economic losses to society.According to the traffic accident statistics of the Ministry of Communications of China in 2009,more than 300,000 car accidents occurred in the year,most of which were caused by drunk driving.Therefore,this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system.The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system,combined with alcohol sensor MQ-3 circuit,LCD1602 liquid crystal display circuit,buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system.The main functions of the system are as follows:the alcohol sensor in the car detects the driver’s alcohol concentration value,and displays the value on the LCD screen.The buzzer alarm is exceeded and the information is sent to the traffic police department and the family’s mobile phone through the GPRS module.The system can effectively make up for the shortcomings of traffic police detection,which has certain research significance.

A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

Assuring medical images protection and robustness is a compulsory necessity nowadays.In this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform.Afterwards,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark.Lastly,a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT.Thus,the watermarked image is generated by enclosing both keys into DWT and DCT coefficients.The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods.In order to validate the proposed technique,a standard dataset of medical images is used.Simulation results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical images.In addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.