Enhancing Critical Path Problem in Neutrosophic Environment Using Python

In the real world,one of the most common problems in project management is the unpredictability of resources and timelines.An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach,often known as neutrosophic logic.Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number.This innovative approach evaluates the inherent uncertainty in project durations of the planning phase,which enhances the potential significance of the decision-making process in the project.Our proposed method,for the first time in the neutrosophic set literature,not only solves existing problems but also introduces a new set of problems not yet explored in previous research.A comparative study using Python programming was conducted to examine the effectiveness of responsive and adaptive planning,as well as their differences from other existing models such as the classical critical path problem and the fuzzy critical path problem.The study highlights the use of neutrosophic logic in handling complex projects by illustrating an innovative dynamic programming framework that is robust and flexible,according to the derived results,and sets the stage for future discussions on its scalability and application across different industries.

Correlation Composition Awareness Model with Pair Collaborative Localization for IoT Authentication and Localization

Secure authentication and accurate localization among Internet of Things(IoT)sensors are pivotal for the functionality and integrity of IoT networks.IoT authentication and localization are intricate and symbiotic,impacting both the security and operational functionality of IoT systems.Hence,accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges.To overcome these challenges,recent approaches have used encryption techniques with well-known key infrastructures.However,these methods are inefficient due to the increasing number of data breaches in their localization approaches.This proposed research efficiently integrates authentication and localization processes in such a way that they complement each other without compromising on security or accuracy.The proposed framework aims to detect active attacks within IoT networks,precisely localize malicious IoT devices participating in these attacks,and establish dynamic implicit authentication mechanisms.This integrated framework proposes a Correlation Composition Awareness(CCA)model,which explores innovative approaches to device correlations,enhancing the accuracy of attack detection and localization.Additionally,this framework introduces the Pair Collaborative Localization(PCL)technique,facilitating precise identification of the exact locations of malicious IoT devices.To address device authentication,a Behavior and Performance Measurement(BPM)scheme is developed,ensuring that only trusted devices gain access to the network.This work has been evaluated across various environments and compared against existing models.The results prove that the proposed methodology attains 96%attack detection accuracy,84%localization accuracy,and 98%device authentication accuracy.

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.

MAIPFE:An Efficient Multimodal Approach Integrating Pre-Emptive Analysis,Personalized Feature Selection,and Explainable AI

Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

LuNet-LightGBM:An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the retina.If not treated early,DR becomes a significant reason for blindness.To identify the DR and determine the stages,medical tests are very labor-intensive,expensive,and timeconsuming.To address the issue,a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper.Our proposal is based on lesion segmentation of the fundus images based on the LuNet network.Then a Refined Attention Pyramid Network(RAPNet)is used for extracting global and local features.To increase the performance of the classifier,the unique features are selected from the extracted feature set using Aquila Optimizer(AO)algorithm.Finally,the LightGBM model is applied to classify the input image based on the severity.Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets(MESSIDOR,APTOS,and IDRiD)using several performance metrics such as accuracy,precision,recall,and f1-score.The proposed classifier achieves 99.29%,99.35%,and 99.31%accuracy for these three datasets respectively.The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.

A Unique Discrete Wavelet&Deterministic Walk-Based Glaucoma Classification Approach Using Image-Specific Enhanced Retinal Images

Glaucoma is a group of ocular atrophy diseases that cause progressive vision loss by affecting the optic nerve.Because of its asymptomatic nature,glaucoma has become the leading cause of human blindness worldwide.In this paper,a novel computer-aided diagnosis(CAD)approach for glaucomatous retinal image classification has been introduced.It extracts graph-based texture features from structurally improved fundus images using discrete wavelet-transformation(DWT)and deterministic tree-walk(DTW)procedures.Retinal images are considered from both public repositories and eye hospitals.Images are enhanced with image-specific luminance and gradient transitions for both contrast and texture improvement.The enhanced images are mapped into undirected graphs using DTW trajectories formed by the image’s wavelet coefficients.Graph-based features are extracted fromthese graphs to capture image texture patterns.Machine learning(ML)classifiers use these features to label retinal images.This approach has attained an accuracy range of 93.5%to 100%,82.1%to 99.3%,95.4%to 100%,83.3%to 96.6%,77.7%to 88.8%,and 91.4%to 100%on the ACRIMA,ORIGA,RIM-ONE,Drishti,HRF,and HOSPITAL datasets,respectively.The major strength of this approach is texture pattern identification using various topological graphs.It has achieved optimal performance with SVM and RF classifiers using biorthogonal DWT combinations on both public and patients’fundus datasets.The classification performance of the DWT-DTW approach is on par with the contemporary state-of-the-art methods,which can be helpful for ophthalmologists in glaucoma screening.

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).

Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous repercussions.An Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks.Although extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still challenging.Therefore,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm rate.Initially,to improve training data quality,various preprocessing techniques are utilized.The Adaptive Synthetic oversampling technique generates synthetic samples for minority classes.In the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its rank.Then by simple plurality voting,the most optimal features are retrieved.Then the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the performance.The experiments were carried out on the original imbalanced data and balanced data.The outcomes demonstrate that the balanced data scenario knocked out the imbalanced data.Finally,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD),Canadian Institute for Cybersecurity(CIC-IDS 2017),and UNSW-NB15,datasets respectively.In addition,the suggested technique has been examined and contrasted with the state of art methods on three datasets.

Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions.This research proposes a novel methodology through three stages.Firstly,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced data.Secondly,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature selection.BinHHO has performed well when compared with the other two.Thirdly,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud.The efficiency of BinHHO is analyzed with other popular MH algorithms.The BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.

A 2D numerical ocean model on the Coriolis and wind stress effects using Stochastics

Ocean basin is modeled as a two-dimensional closed,bounded domain in which the fluid flow is governed by the complex partial differential equations in the flow function.Keeping in view that the ocean currents are non-viscous,no normal flow conditions are used at the basin boundaries.The parameters investigated here are:Coriolis parameter,wind stress coefficient,and latitude.Stochastic differential equations in time scales are solved by deterministic and stochastic methods.Deterministic results concluded that streamlines are symmetric about stagnation point(no flow)for 0<R_(p)<6.57.Stochastic controls are introduced to account for variability in time scales.Euler-Maruyama(direct)and Fokker-Planck equation schemes(indirect)are proposed.It is concluded that stream functions in both direct and indirect methods are of the same qualitatively and quantitatively when 0<R_(p)<79.

Xception-Fractalnet:Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

Neurological disorders such as Alzheimer’s disease(AD)are very challenging to treat due to their sensitivity,technical challenges during surgery,and high expenses.The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods.Furthermore,conventional approaches take a lot of time and might not always be precise.Hence,a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD.Therefore in this paper,an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five classes.Initially,a network based on Unet++is built to segment the tissues of the brain.Then,using the segmented tissue components as input,the Xception-based deep learning technique is employed to extract high-level features.Finally,the optimized Fractalnet framework is used to categorize the disease condition using the acquired characteristics.The proposed strategy is tested on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset that accurately segments brain tissues with a 98.45%of dice similarity coefficient(DSC).Additionally,for themulticlass classification of AD,the suggested technique obtains an accuracy of 99.06%.Moreover,ANOVA statistical analysis is also used to evaluate if the groups are significant or not.The findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.

Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.

A Novel Hybrid Optimization Algorithm for Materialized View Selection from Data Warehouse Environments

Responding to complex analytical queries in the data warehouse(DW)is one of the most challenging tasks that require prompt attention.The problem of materialized view(MV)selection relies on selecting the most optimal views that can respond to more queries simultaneously.This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs.The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique(ECHT).The constraints such as self-adaptive penalty,epsilon(ε)-parameter and stochastic ranking(SR)are considered for constraint handling.These two constraints helped the proposed model select the finest views that minimize the objective function.Further,a novel and effective combination of Ebola and coot optimization algorithms named hybrid Ebola with coot optimization(CHECO)is introduced to choose the optimal MVs.Ebola and Coot have recently introduced metaheuristics that identify the global optimal set of views from the given population.By combining these two algorithms,the proposed framework resulted in a highly optimized set of views with minimized costs.Several cost functions are described to enable the algorithm to choose the finest solution from the problem space.Finally,extensive evaluations are conducted to prove the performance of the proposed approach compared to existing algorithms.The proposed framework resulted in a view maintenance cost of 6,329,354,613,784,query processing cost of 3,522,857,483,566 and execution time of 226 s when analyzed using the TPC-H benchmark dataset.

Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

The demand for cybersecurity is rising recently due to the rapid improvement of network technologies.As a primary defense mechanism,an intrusion detection system(IDS)was anticipated to adapt and secure com-puting infrastructures from the constantly evolving,sophisticated threat land-scape.Recently,various deep learning methods have been put forth;however,these methods struggle to recognize all forms of assaults,especially infrequent attacks,because of network traffic imbalances and a shortage of aberrant traffic samples for model training.This work introduces deep learning(DL)based Attention based Nested U-Net(ANU-Net)for intrusion detection to address these issues and enhance detection performance.For this IDS model,the first data preprocessing is carried out in three stages:duplication elimi-nation,label transformation,and data normalization.Then the features are extracted and selected based on the Improved Flower Pollination Algorithm(IFPA).The Improved Monarchy Butterfly Optimization Algorithm(IMBO),a new metaheuristic,is used to modify the hyper-parameters in ANU-Net,effectively increasing the learning rate for spatial-temporal information and resolving the imbalance problem.Through the use of parallel programming,the MapReduce architecture reduces computation complexity while signifi-cantly accelerating processing.Three publicly available data sets were used to evaluate and test the approach.The investigational outcomes suggest that the proposed technique can more efficiently boost the performances of IDS under the scenario of unbalanced data.The proposed method achieves above 98%accuracy and classifies various attacks significantly well compared to other classifiers.

Software Defect Prediction Based Ensemble Approach

Software systems have grown significantly and in complexity.As a result of these qualities,preventing software faults is extremely difficult.Software defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance costs.When it comes to lowering software costs and assuring software quality,SDP plays a critical role in software development.As a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more efficiently.Several methods for detecting and addressing such flaws at a low cost have been offered.These approaches,on the other hand,need to be significantly improved in terms of performance.Therefore in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are proposed.The proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative features.To find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository datasets.When compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

Automated Artificial Intelligence Empowered White Blood Cells Classification Model

White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.