Formation of Natural Melanin/TiO_(2) Nanostructure Hybrids with Enhanced Optical,Thermal and Magnetic Properties as a Soft Material

The natural Melanin/TiO_(2) was synthesized by the use of ultrasonication under UV radiation.The influence of natural melanin on the structural,optical and thermal properties of TiO_(2) nanoparticles was investigated by using Fourier transform infrared spectroscopy,thermogravimetric analysis and UV-Vis spectroscopy.It was observed that incorporating natural melanin on TiO_(2) nanoparticles(TiO_(2)-Mel)occurred at 2.01 eV with a low value of Urbach energy around 100 meV indicating improvement in the crystalline structure.Magnetic measurement at room temperature showed diamagnetic behavior.Furthermore,thermal results showed that TiO_(2)-Mel is stable even at temperatures up to 400℃.According to the results obtained by the thermal stability of melanin with titanium dioxide,it can be a good candidate in many applications such as solar cells and optoelectronics.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniques in terms of accuracy,precision,and recall.This superiority stems from ACIDS-PELT’s ability to overcome limitations associated with approximation and imprecision in change point detection while offering a more accurate and precise approach to detecting security threats in dynamic cloud environments.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

FlowBreakdown of Hybrid Nanofluid on a Rigid Surface with Power Law Fluid as Lubricated Layers

Thiswork investigates an oblique stagnation point flowof hybrid nanofluid over a rigid surface with power lawfluidas lubricated layers. Copper (Cu) and Silver (Ag) solid particles are used as hybrid particles acting in water H2O asa base fluid. The mathematical formulation of flow configuration is presented in terms of differential systemthat isnonlinear in nature. The thermal aspects of the flow field are also investigated by assuming the surface is a heatedsurface with a constant temperature T. Numerical solutions to the governing mathematical model are calculatedby the RK45 algorithm. The results based on the numerical solution against various flow and thermal controllingparameters are presented in terms of line graphs. The specific results depict that the heat flux increases over thelubricated-indexed parameter.

Einstein-Podolsky-Rosen Steering and Nonlocality in Open Quantum Systems

We investigate the dynamical behavior of quantum steering (QS), Bell nonlocality, and entanglement in open quantum systems. We focus on a two-qubit system evolving within the framework of Kossakowski-type quantum dynamical semigroups. Our findings reveal that the measures of quantumness for the asymptotic states rely on the primary parameter of the quantum model. Furthermore, control over these measures can be achieved through a careful selection of these parameters. Our analysis encompasses various cases, including Bell states, Werner states, and Horodecki states, demonstrating that the asymptotic states can exhibit steering, entanglement, and Bell nonlocality. Additionally, we find that these three quantum measures of correlations can withstand the influence of the environment, maintaining their properties even over extended periods.

Ontology-Based Cyber Security Policy Implementation in Saudi Arabia

Cyber security is an important element of national security and the safekeeping of a nation’s constituency and assets. In Saudi Arabia, the point of interest on cyber security is particularly outstanding due to the fact that Saudi Arabia has a highly cyber attacks all over the Arab countries. This paper displays on contemporary studies done in Saudi Arabia in regards to cyber security policy coverage. The point of interest of this paper is the use of ontology to identify and suggest a formal, encoded description of the cyber security strategic environment, and propose the development of ontology to be able to permit the implementation of the sort of policy. The intention of the ontology is to become aware of and constitute the multi-layered company of gamers and their related roles and obligations within the cyber security environment. This could make contributions in large part to the improvement, implementation and rollout of a country wide cyber security policy in Saudi Arabia.

An Analysis of Renewable Water Sources in Saudi Arabia

Kingdom of Saudi Arabia (KSA) is located in a very harsh natural desert environment with no rivers or lakes and an average yearly rainfall of less than 100 mm. The country is under extreme water shortage conditions. KSA utilizes conventional (natural) and unconventional water resources to satisfy the ever increasing water demand. The purpose of this paper is to analyze two approaches to obtaining fresh water from renewable water resources, namely they are seawater distillation using solar energy and gathering liquid water from fog. In order to conduct the study for seawater distillation a solar still basin has been designed, manufactured and tested in selected day for saline water in month of April, 2012. The solar still consists of insulated metal box with channels. Pyramidal glass covers attached to the basin at an angle (=45°), and the basin area of the still is 0.25 m2 and filled with 6 liters of seawater. The average daily output was found to be 3.924 liters/day. Further, to harvesting water from fog a Standard Fog Collector (SFC) was designed and manufactured in Asir region with locally available materials and imported mesh. This SFC was installed in April 2012. The site was chosen based on topography and altitude and data from April 2012 to March 2013 were obtained. Measurements with the SFC were made for region with 3200 m elevation. The results indicate that the average water production was 6.225 L/(m2·day) over the studied period and the highest average water production was recorded in December 11.20 L/(m2·day). The highest water collection was 20 L/(m2·day) and recorded in Jan. 05, 2013 at Rayda site, and furthermore for the same site, the best average water production of 10.52 L/(m2·day) was obtained in winter three months period namely (December, November and January).

Zinc–Bromine Rechargeable Batteries:From Device Configuration,Electrochemistry,Material to Performance Evaluation

Zinc–bromine rechargeable batteries(ZBRBs)are one of the most powerful candidates for next-generation energy storage due to their potentially lower material cost,deep discharge capability,non-flammable electrolytes,relatively long lifetime and good reversibility.However,many opportunities remain to improve the efficiency and stability of these batteries for long-life operation.Here,we discuss the device configurations,working mechanisms and performance evaluation of ZBRBs.Both non-flow(static)and flow-type cells are highlighted in detail in this review.The fundamental electrochemical aspects,including the key challenges and promising solutions,are discussed,with particular attention paid to zinc and bromine half-cells,as their performance plays a critical role in determining the electrochemical performance of the battery system.The following sections examine the key performance metrics of ZBRBs and assessment methods using various ex situ and in situ/operando techniques.The review concludes with insights into future developments and prospects for high-performance ZBRBs.

A Semantic Adversarial Network for Detection and Classification of Myopic Maculopathy

The diagnosis of eye disease through deep learning (DL) technologyis the latest trend in the field of artificial intelligence (AI). Especially indiagnosing pathologic myopia (PM) lesions, the implementation of DL is adifficult task because of the classification complexity and definition system ofPM. However, it is possible to design an AI-based technique that can identifyPM automatically and help doctors make relevant decisions. To achieve thisobjective, it is important to have adequate resources such as a high-qualityPM image dataset and an expert team. The primary aim of this research isto design and train the DLs to automatically identify and classify PM intodifferent classes. In this article, we have developed a new class of DL models(SAN-FSL) for the segmentation and detection of PM through semanticadversarial networks (SAN) and few-short learning (FSL) methods, respectively.Compared to DL methods, the conventional segmentation methodsuse supervised learning models, so they (a) require a lot of data for trainingand (b) fixed weights are used after the completion of the training process.To solve such problems, the FSL technique was employed for model trainingwith few samples. The ability of FSL learning in UNet architectures is beingexplored, and to fine-tune the weights, a few new samples are being providedto the UNet. The outcomes show improvement in the detection area andclassification of PM stages. Betterment in the result is observed by sensitivity(SE) of 95%, specificity (SP) of 96%, and area under the receiver operatingcurve (AUC) of 98%, and the higher F1-score is achieved using 10-fold crossvalidation.Furthermore, the obtained results confirmed the superiority of theSAN-FSL method.

A Detailed Mathematical Analysis of the Vaccination Model for COVID-19

This study aims to structure and evaluate a newCOVID-19modelwhich predicts vaccination effect in theKingdom of Saudi Arabia(KSA)under Atangana-Baleanu-Caputo(ABC)fractional derivatives.On the statistical aspect,we analyze the collected statistical data of fully vaccinated people from June 01,2021,to February 15,2022.Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic,based on daily series data from February 16,2022,to April 15,2022.The results of data analysis show that the appropriate model is autoregressive integratedmoving average ARIMA(1,1,2),and hence,a forecast about the evolution of the COVID-19 vaccination in 60 days is presented.The theoretical aspect provides equilibrium points,reproduction number R0,and biologically feasible region of the proposed model.Also,we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform.On the numerical aspect,we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model.Moreover,numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model.Finally,the concerned numerical simulations are compared with the exact real available date given in the statistical aspect.

Genetic Crossover Operators for the Capacitated Vehicle Routing Problem

We study the capacitated vehicle routing problem(CVRP)which is a well-known NP-hard combinatorial optimization problem(COP).The aim of the problem is to serve different customers by a convoy of vehicles starting from a depot so that sum of the routing costs under their capacity constraints is minimized.Since the problem is very complicated,solving the problem using exact methods is almost impossible.So,one has to go for the heuristic/metaheuristic methods and genetic algorithm(GA)is broadly applied metaheuristic method to obtain near optimal solution to such COPs.So,this paper studies GAs to find solution to the problem.Generally,to solve a COP,GAs start with a chromosome set named initial population,and then mainly three operators-selection,crossover andmutation,are applied.Among these three operators,crossover is very crucial in designing and implementing GAs,and hence,numerous crossover operators were developed and applied to different COPs.There are two major kinds of crossover operators-blind crossovers and distance-based crossovers.We intend to compare the performance of four blind crossover and four distance-based crossover operators to test the suitability of the operators to solve the CVRP.These operators were originally proposed for the standard travelling salesman problem(TSP).First,these eight crossovers are illustrated using same parent chromosomes for building offspring(s).Then eight GAs using these eight crossover operators without any mutation operator and another eight GAs using these eight crossover operators with a mutation operator are developed.These GAs are experimented on some benchmark asymmetric and symmetric instances of numerous sizes and various number of vehicles.Our study revealed that the distance-based crossovers are much superior to the blind crossovers.Further,we observed that the sequential constructive crossover with and without mutation operator is the best one for theCVRP.This estimation is validated by Student’s t-test at 95%confidence level.We further determined a comparative rank of the eight crossovers for the CVRP.

Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

Social media,like Twitter,is a data repository,and people exchange views on global issues like the COVID-19 pandemic.Social media has been shown to influence the low acceptance of vaccines.This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement.This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines.This paper introduces a sigmoidal particle swarm optimization(SPSO)algorithm.First,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing sentiment.The proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature selection.The Covid-19 vaccination tweets dataset was created and used for training,validating,and testing.The proposed approach outperformed considered algorithms in terms of accuracy.Additionally,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection algorithm.It shows that augmentation improves the overall accuracy of tweet analysis.After the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

Elucidating the promotion mechanism of the ternary cooperative heterostructure toward industrial-level urea oxidation catalysis

From the perspective of electronic structure modulation,it is highly desirable to rationally design the active urea oxidation reaction(UOR)catalysts through interface engineering.The binary cooperative heterostructure systems have been shown significant enhancement for catalyzing UOR,but their performance still remains unsatisfactory for industrialization because of the unfavorable intermediate adsorption/desorption and deficient electron transfer channels.In response,taking the ternary cooperative Ni_5P_(4)/NiSe_(2)/Ni_(3)Se_(4) heterostructure as the proof-of-concept paradigm,a catalytic model is rationally put forward to elucidate the UOR promotion mechanism at the molecular level.The rod-like Ni_5P_(4)/NiSe_(2)/Ni_(3)Se_(4) nanoarrays with three-phase heterojunction are experimentally fabricated on Ni foam(named as Ni_5P_(4)/NiSe_(2)/Ni_(3)Se_(4)/NF)via simple two-step processes.The density functional theory calculations disclose that construction of Ni_5P_(4)/NiSe_(2)/Ni_(3)Se_(4) heterostructure model not only induce charge redistribution at the interfacial region for creating innumerable electron transfer channels,but also endow it with a moderate d-band center that could help to build a balance between adsorption and desorption of diverse UOR intermediates.Benefiting from the unique rod-like nanoarrays with large specific surface area and the optimized electronic structure,the well-designed Ni_5P_(4)/NiSe_(2)/Ni_(3)Se_(4)/NF could act as a robust catalyst for driving UOR at industrial-level current densities under tough environments,offering great potential for commercial applications.