Rational manipulation of electrolyte to induce homogeneous SEI on hard carbon anode for sodium-ion battery

Sodium-ion batteries (SIBs) have great potential to be the next major energy storage devices due to their obvious advantages and developing advanced electrodes and electrolytes is urgently necessary to promote its future industrialization.However,hard carbon as a state-of-the-art anode of SIBs still suffers from the low initial Coulomb efficiency and unsatisfactory rate capability,which could be improved by forming desirable solid electrolyte interphases (SEI) to some extent.Indeed,the chemistry and morphology of these interfacial layers are fundamental parameters affecting the overall battery operation,and optimizing the electrolyte to dictate the quality of SEI on hard carbon is a key strategy.Hence,this review summarizes the recent research on SEI design by electrolyte manipulation from solvents,salts,and additives.It also presents some potential mechanisms of SEI formation in various electrolyte systems.Besides,the current advanced characterization techniques for electrolyte and SEI structure analyses have been comprehensively discussed.Lastly,current challenges and future perspectives of SEI formation on hard carbon anode for SIBs are provided from the viewpoints of its compositions,evolution processes,structures,and characterization techniques,which will promote SEI efficient manipulation and improve the performance of hard carbon,and further contribute to the development of SIBs.

Classification and detection of dental images using meta-learning

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input.Instead of just memorizing a task,this is accomplished through teaching a model how to learn.Algorithms for meta-learning are typically trained on a collection of training problems,each of which has a limited number of labelled instances.Multiple Xray classification tasks,including the detection of pneumonia,coronavirus disease 2019,and other disorders,have demonstrated the effectiveness of meta-learning.Meta-learning has the benefit of allowing models to be trained on dental X-ray datasets that are too few for more conventional machine learning methods.Due to the high cost and lengthy collection process associated with dental imaging datasets,this is significant for dental X-ray classification jobs.The ability to train models that are more resistant to fresh input is another benefit of meta-learning.

Current updates on the epidemiology,pathogenesis and development of small molecule therapeutics for the treatment of Ebola virus infections

Ebola virus disease(EVD)is a rare,highly contagious and a deadly disease with a variable fatality rate ranging from 30%to 90%.Over the past two decades,Ebola pandemic has severely affected the sub-Sahara region including Democratic Republic of the Congo(DRC),and Uganda.The causative agents of the most EVD cases are three distinct species out of six Ebolaviruses namely Zaire Ebolavirus(ZEBOV),Sudan Ebolavirus(SUDV)and Bundibugyo Ebolavirus(BDBV).In recent years,significant strides have been made in therapeutic interventions.Notably,the US Food and Drug Administration has approved two monoclonal antibodies:InmazebTM(REGN-EB3)and Ansuvimab or EbangaTM.Additionally,many small molecules are currently in the developmental stage,promising further progress in medical treatment.Addressing the critical need for preventive measures,this review provides an in-depth analysis of the licensed Ebola vaccines-Ervebo and the combination of Zabdeno(Ad26.ZEBOV)and Mvabea(MVA-BN-Filo)as well as the vaccines which are currently being tested for their efficacy and safety in clinical studies.These vaccines might play an important role in curbing the spread and mitigating the impact of this lethal disease.The current treatment landscape for EVD encompasses both nutritional(supportive)and drug therapies.The review comprehensively details the origin,pathogenesis,and epidemiology of EVD,shedding light on the ongoing efforts to combat this devastating disease.It explores small molecules in various stages of the development,discusses patents filed or granted,and delves into the clinical and supportive therapies that form the cornerstone of EVD management.This review aims to provide the recent developments made in the design and synthesis of small molecules for scientific community to facilitate a deeper understanding of the disease and fostering the development of effective strategies for prevention,treatment,and control of EVD.

Comprehensive Assessment of Anxiolytic Properties in 4-HPAA Derivatives: Bridging in Vivo Validation and Molecular Docking Analyses

Anxiety is a significant mental health issue that substantially affects an individual’s quality of life. Feelings of uneasiness, irritability, and sleep disturbances characterize it. 4-Hydroxyphenyl acetic acid (4-HPAA) is identified in brain cells as a physiological byproduct of tyramine. This study hypothesizes that 4-HPAA may regulate anxiety due to its anxiolytic properties, acting as a modulator of the GABAergic system, which plays a crucial role in the pathophysiology of anxiety disorders. Our study aims to enhance the anxiolytic effects of 4-HPAA through chemical modification to improve its pharmacokinetic properties. Three derivatives, namely Isopropyl-4-hydroxy-[phenyl] acetate (IHPA), Isopropyl-4-hydroxy-[phenyl] acetate (MPAA), and 4-methoxyphenyl acetate (MPHA), have been synthesized from 4-HPAA. This assessment will use well-established animal models, specifically the Elevated Plus-Maze (EPM) and Zero Maze (EZM) tests, selected for their validity in replicating anxiety-like symptoms in animals. Chronic caffeine administration via drinking water (0.3 g/l for 14 days) was employed to induce an anxiety state for testing purposes. IHPA and MPAA demonstrated significant anxiolyticactivity when tested in the EPM and EZM experiments. Molecular docking simulations using AutoDock Vina indicated that 4-HPAA derivatives had docking scores ranging from −5.8 to −4.8 kcal/mol, compared to the standard anxiolytic medication Diazepam, which scored −7.1 kcal/mol. These scores suggest a potential for 4-HPAA derivatives to interact effectively with the Gamma-aminobutyric acid (GABA_A) receptor. In conclusion, our in vivo and in silico analyses indicate a promising anxiolytic potential for 4-HPAA derivatives.

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

Fabrication of chitosan/silica hybrid coating on AZ31 Mg alloy for orthopaedic applications

Biocompatible conversion of chitosan and chitosan/silica hybrid coating were prepared to enhance the biocompatibility and corrosion resistance of biodegradable AZ31 Mg alloy. The coatings were optimized and analysed with potentiodynamic polarization, SEM, ATR-IR and XPS studies. Potentiodynamic polarization studies, revealed that the coatings exhibited high corrosion resistance. The surface morphology of the Ch-3/Si coating showed small globular rough structure. The presence of functional groups was confirmed by ATR-IR. For a better understanding of chitosan/silica hybrid coating, the chemical states were examined by XPS studies. The in-vitro bioactivity of the coated samples was evaluated in Earle’s solution, which formed a dense layer of coral-like structure and calcium-deficient apatite with less stoichiometric ratio than the hydroxyapatite. In-vitro cell culture studies exhibited a good cell proliferation rate and the fabricated Ch-3/Si coating was found to be non-hemolytic. The bacterial studies proved that Ch-3/Si coating possessed inherent antibacterial activity.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

At the present time,the Industrial Internet of Things(IIoT)has swiftly evolved and emerged,and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data.The use of image sensors as an automa-tion tool for the IIoT is increasingly becoming more common.Due to the fact that this organisation transfers an enormous number of photographs at any one time,one of the most significant issues that it has is reducing the total quantity of data that is sent and,as a result,the available bandwidth,without compromising the image quality.Image compression in the sensor,on the other hand,expedites the transfer of data while simultaneously reducing bandwidth use.The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties.The image encryp-tion model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system.This helps to ensure that image datasets are kept safe.The Linde–Buzo–Gray(LBG)methodology is an example of a vector quantization algorithm that is extensively used and a rela-tively new form of picture reduction known as vector quantization(VQ).As a result,the purpose of this research is to create an artificial humming bird optimi-zation approach that combines LBG-enabled codebook creation and encryption(AHBO-LBGCCE)for use in an IIoT setting.In the beginning,the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ.The Burrows-Wheeler Transform(BWT)model is used in order to accomplish codebook compression.In addition,the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained.A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms.The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

Hybridized Artificial Neural Network for Automated Software Test Oracle

Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality.These two characteristics are very critical in the software applications of present times.When testers want to perform scenario evaluations,test oracles are generally employed in the third phase.Upon test case execution and test outcome generation,it is essential to validate the results so as to establish the software behavior’s correctness.By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application,leads to a reduction in the fault detection work with minimal loss of information and would also greatly reduce the cost for clearing up.A hybrid Particle Swarm Optimization(PSO)with Stochastic Diffusion Search(PSO-SDS)based Neural Network,and a hybrid Harmony Search with Stochastic Diffusion Search(HS-SDS)based Neural Network has been proposed in this work.Further to evaluate the performance,it is compared with PSO-SDS based artificial Neural Network(PSO-SDS ANN)and Artificial Neural Network(ANN).The Misclassification of correction output(MCO)of HS-SDS Neural Network is 6.37 for 5 iterations and is well suited for automated testing.

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

Mesenchymal stem cell-derived apoptotic bodies alleviate alveolar bone destruction by regulating osteoclast differentiation and function

Periodontitis is caused by overactive osteoclast activity that results in the loss of periodontal supporting tissue and mesenchymal stem cells(MSCs)are essential for periodontal regeneration.However,the hypoxic periodontal microenvironment during periodontitis induces the apoptosis of MSCs.Apoptotic bodies(ABs)are the major product of apoptotic cells and have been attracting increased attention as potential mediators for periodontitis treatment,thus we investigated the effects of ABs derived from MSCs on periodontitis.MSCs were derived from bone marrows of mice and were cultured under hypoxic conditions for 72 h,after which ABs were isolated from the culture supernatant using a multi-filtration system.The results demonstrate that ABs derived from MSCs inhibited osteoclast differentiation and alveolar bone resorption.miRNA array analysis showed that miR-223-3p is highly enriched in those ABs and is critical for their therapeutic effects.Targetscan and luciferase activity results confirmed that Itgb1 is targeted by miR-223-3p,which interferes with the function of osteoclasts.Additionally,DC-STAMP is a key regulator that mediates membrane infusion.ABs and pre-osteoclasts expressed high levels of DC-STAMP on their membranes,which mediates the engulfment of ABs by pre-osteoclasts.ABs with knock-down of DC-STAMP failed to be engulfed by pre-osteoclasts.Collectively,MSC-derived ABs are targeted to be engulfed by pre-osteoclasts via DC-STAMP,which rescued alveolar bone loss by transferring miR-223-3p to osteoclasts,which in turn led to the attenuation of their differentiation and bone resorption.These results suggest that MSC-derived ABs are promising therapeutic agents for the treatment of periodontitis.

导电纤维材料在高柔性可穿戴超级电容器中的研究进展

采用纤维材料制备的超级电容器因柔韧性好、质量轻、能量密度高等优点,成为人们关注的电化学储能器件。目前,他们已广泛应用在信息传感、数据计算和通信等电子系统中。这些基于纤维材料的超级电容器(SCs)拥有比标准的平行平板电容器和电池具有更高的功率密度,因而再电子纺织品中大量使用。本文详细阐述了碳纳米管(CNTs)、石墨烯和聚(3,4-乙烯二氧噻吩):聚(苯乙烯磺酸)(PEDOT:PSS)的组成、纺丝和制备条件对可穿戴储能装置电化学性能的影响。

Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its treatment.The researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their areas.One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)scans.At the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical imaging.Particularly,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest radiographs.While the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly CNN.This study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and classification.The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19.To do so,the presented IFFODLEF model applies image pre-processing at the initial stage.In addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature extraction.Moreover,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify COVID-19.The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance.The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

Identification of miR-802-5p and its involvement in type 2 diabetes mellitus

MicroRNAs(miRNA)are recently discovered endogenous,small noncoding RNAs(of 22 nucleotides)that play pivotal roles in gene regulation.They are involved in post-transcriptional control of gene expression.miRNAs are emerging as important regulators of cell proliferation,development,cancer formation,stress responses,cell death and physiological conditions.Increasing evidence has demonstrated the human miRNAs bind to their target mRNA sequences with perfect or near-perfect sequence complementarily.This provides a powerful strategy for discovering potential type 2 diabetes mellitus(T2DM)targets and gives the probability to exploit them for diagnostic and therapeutic causes.About 6%of the world population is affected by T2DM,and it is recognized as a global epidemic by the World Health Organization.At present there is no valid biomarker to control or manage T2DM.Therefore,the present study applied a mature sequence of miRNAs from publicly accessible databases to identify the miRNA from T2DM expressed sequence tags,and the results are detailed and discussed below.

Evaluation of mechanical and wear properties of hybrid aluminium matrix composites

Hybrid metal matrix composites are important class of engineering materials used in automotive, aerospace and other applications because of their lower density, higher specific strength, and better physical and mechanical properties compared to pure aluminium. The mechanical and wear properties of hybrid aluminium metal matrix composites were investigated. Mica and SiC ceramic particles were incorporated into A1 356 alloy by stir-casting route. Microstructures of the samples were studied using scanning electron microscope （SEM）. The chemical composition was investigated through energy dispersive X-ray （EDX） detector. The results indicate that the better strength and hardness are achieved with A1/10SiC-3mica composites. The increase in mass fraction of mica improves the wear loss of the composites.

Optimization of machining parameters in turning of Al-SiC-Gr hybrid metal matrix composites using grey-fuzzy algorithm

Metal matrix composites reinforced with graphite particles provide better machinability and tribological properties. The present study attempts to find the optimal level of machining parameters for multi-performance characteristics in turning of Al-SiC-Gr hybrid composites using grey-fuzzy algorithm. The hybrid composites with 5%, 7.5% and 10% combined equal mass fraction of SiC-Gr particles were used for the study and their corresponding tensile strength values are 170, 210, 204 MPa respectively. Al-10%（SiC-Gr） hybrid composite provides better machinability when compared with composites with 5% and 7.5% of SiC-Gr. Grey-fuzzy logic approach offers improved grey-fuzzy reasoning grade and has less uncertainties in the output when compared with grey relational technique. The confirmatory test reveals an increase in grey-fuzzy reasoning grade from 0.619 to 0.891, which substantiates the improvement in multi-performance characteristics at the optimal level of process parameters setting.

Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.

Therapeutic effect of bone marrow mesenchymal stem cells on cold stress induced changes in the hippocampus of rats

The present study aims to evaluate the effect of bone marrow mesenchymal stem cells on cold stress induced neuronal changes in hippocampal CA1 region of Wistar rats. Bone marrow mes- enchymal stem cells were isolated from a 6-week-old Wistar rat. Bone marrow from adult femora and tibia was collected and mesenchymal stem cells were cultured in minimal essential medium containing 10% heat-inactivated fetal bovine serum and were sub-cultured. Passage 3 cells were analyzed by flow cytometry for positive expression of CD44 and CD90 and negative expression of CD45. Once CD44 and CD90 positive expression was achieved, the cells were cultured again to 90% confluence for later experiments. Twenty-four rats aged 8 weeks old were randomly and evenly divided into normal control, cold water swim stress （cold stress）, cold stress ＋ PBS （intra- venous infusion）, and cold stress ＋ bone marrow mesenchymal stem cells （1 x 106; intravenous infusion） groups. The total period of study was 60 days which included 1 month stress period followed by 1 month treatment. Behavioral functional test was performed during the entire study period. After treatment, rats were sacrificed for histological studies. Treatment with bone marrow mesenchymal stem cells significantly increased the number of neuronal cells in hippocampal CA 1 region. Adult bone marrow mesenchymal stem cells injected by intravenous administration show potential therapeutic effects in cognitive decline associated with stress-related lesions.

Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques

In India’s economy, agriculture has been the most significantcontributor. Despite the fact that agriculture’s contribution is decreasing asthe world’s population grows, it continues to be the most important sourceof employment with a little margin of difference. As a result, there is apressing need to pick up the pace in order to achieve competitive, productive,diverse, and long-term agriculture. Plant disease misinterpretations can resultin the incorrect application of pesticides, causing crop harm. As a result,early detection of infections is critical as well as cost-effective for farmers.To diagnose the disease at an earlier stage, appropriate segmentation of thediseased component from the leaf in an accurate manner is critical. However,due to the existence of noise in the digitally captured image, as well asvariations in backdrop, shape, and brightness in sick photographs, effectiverecognition has become a difficult task. Leaf smut, Bacterial blight andBrown spot diseases are segmented and classified using diseased Apple (20),Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in thesuggested work. In addition, a superior segmentation technique for the ROIfrom sick leaves with living backdrop is presented here. Textural features of thesegmented ROI, such as 1st and 2nd order WPCA Features, are discoveredafter segmentation. This comprises 1st order textural features like kurtosis,skewness, mean and variance as well as 2nd procedure textural features likesmoothness, energy, correlation, homogeneity, contrast, and entropy. Finally,the segmented region of interest’s textural features is fed into four differentclassifiers, with the Enhanced Deep Convolutional Neural Network provingto be the most precise, with a 96.1% accuracy.