Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

Structural insights on anti-biofilm mechanism of heated slightly acidic electrolyzed water technology against multi-resistant Staphylococcus aureus biofilm on food contact surface

Slightly acidic electrolyzed water(SAEW)has proven to be an efficient and novel sanitizer in food and agriculture field.This study assessed the efficacy of SAEW(30 mg/L)at 40℃on the inactivation of foodbome pathogens and detachment of multi-resistant Staphylococcus aureus(MRSA)biofilm.Furthermore.the underlying mechanism of MRS A biofilm under heated SAEW at 40℃treatment on metabolic profiles was investigated.The results showed that the heated SAEW at 40℃significantly effectively against foodbome pathogens of 1.96-7.56(lg(CFU/g))reduction in pork,chicken,spinach,and lettuce.The heated SAEW at 40℃treatment significantly reduced MRS A biofilm cells by 2.41(lg(CFU/cm^(2))).The synergistic effect of SAEW treatment showed intense anti-biofilm activity in decreasing cell density and impairing biofilm cell membranes.Global metabolic response of MRSA biofilms,treated by SAEW at 40℃,revealed the alterations of intracellular metabolites,including amino acids,organic acid,fatty acid,and lipid.Moreover,signaling pathways involved in amino acid metabolism,energy metabolism,nucleotide synthesis,carbohydrate metabolites,and lipid biosynthesis were functionally disrupted by the SAEW at 40℃treatment.As per our knowledge,this is the first research to uncover the potential mechanism of heated SAEW treatment against MRSA biofilm on food contact surface.

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.

Performance and Emission Characteristics of a CRDI Diesel Engine Fuelled by SiO_(2) Nanoparticle-Waste Fat Biodiesel Blends

This study investigates the use of waste fat biodiesel(WFB)from the leather industry as a substitute for diesel fuel.Specifically,it examines the diesel engine performance of WFB,a blend of WFB and diesel(B50),and different blends of WFB and silicon dioxide(SiO_(2))nanoparticles(B50SiO_(2)40,B50SiO_(2)80,and B50SiO_(2)120μg/g).The results indicate that the B50SiO_(2)120 blend increases brake thermal efficiency by 10.03%compared to pure biodiesel but falls 1.93%short of neat diesel.Furthermore,the B50SiO_(2)120 mixture reduces smoke,hydrocarbon,and carbon monoxide emissions by 31.87%,34.14%,and 43.97%respectively,compared to diesel.However,the B50SiO_(2)120 blend shows a 4.91%increase in nitrogen oxide emissions compared to diesel.

Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality nature.The overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved accuracy.The research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved accuracy.The methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance evaluation.This study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the investigation.This framework segments detected the glioma(low/high grade)and glioblastoma class BT.This work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 scheme.This study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

Enhancing MXene-based supercapacitors:Role of synthesis and 3D architectures

MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite its various desirable properties including intrinsic flexibility,high specific surface area,excellent metallic conductivity and unique abundance of surface functionalities,its full potential for electrochemical performance is hindered by the notorious restacking phenomenon of MXene nanosheets.Ascribed to its two-dimensional(2D)nature and surface functional groups,inevitable Van der Waals interactions drive the agglomeration of nanosheets,ultimately reducing the exposure of electrochemically active sites to the electrolyte,as well as severely lengthening electrolyte ion transport pathways.As a result,energy and power density deteriorate,limiting the application versatility of MXene-based supercapacitors.Constructing 3D architectures using 2D nanosheets presents as a straightforward yet ingenious approach to mitigate the fatal flaws of MXene.However,the sheer number of distinct methodologies reported,thus far,calls for a systematic review that unravels the rationale behind such 3D MXene structural designs.Herein,this review aims to serve this purpose while also scrutinizing the structure–property relationship to correlate such structural modifications to their ensuing electrochemical performance enhancements.Besides,the physicochemical properties of MXene play fundamental roles in determining the effective charge storage capabilities of 3D MXene-based electrodes.This largely depends on different MXene synthesis techniques and synthesis condition variations,hence,elucidated in this review as well.Lastly,the challenges and perspectives for achieving viable commercialization of MXene-based supercapacitor electrodes are highlighted.

Enhanced Dye Adsorption and Bacterial Removal of Magnetic Nanoparticle-Functionalized Bacterial Cellulose Acetate Membranes

Utilizing biomass waste as a potential resource for cellulose production holds promise in mitigating environmental consequences.The current study aims to utilize pineapple biowaste extract in producing bacterial cellulose acetate-based membranes with magnetic nanoparticles(Fe_(3)O_(4)nanoparticles)through the fermentation and esterification process and explore its characteristics.The bacterial cellulose fibrillation used a high-pressure homogenization procedure,and membranes were developed incorporating 0.25,0.50,0.75,and 1.0 wt.%of Fe3O4 nanoparticles as magnetic nanoparticle for functionalization.The membrane characteristics were measured in terms of Scanning Electron Microscope,X-ray diffraction,Fourier Transform Infrared,Vibrating Sample Magnetometer,antibacterial activity,bacterial adhesion and dye adsorption studies.The results indicated that the surface morphology of membrane changes where the bacterial cellulose acetate surface looks rougher.The crystallinity index of membrane increased from 54.34%to 68.33%,and the functional groups analysis revealed that multiple peak shifts indicated alterations in membrane functional groups.Moreover,adding Fe_(3)O_(4)-NPs into membrane exhibits paramagnetic behavior,increases tensile strength to 73%,enhances activity against E.coli and S.aureus,and is successful in removing bacteria from wastewater of the river to 67.4%and increases adsorption for anionic dyes like Congo Red and Acid Orange.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

Biosynthesis of Silver Nanoparticles with Clerodendron phlomoides Leave Extract:Particle Morphology,Antimicrobial Potential and Application

Silver nanoparticles are versatile nanomaterials that have found numerous applications in various fields.The use of plant extract for the synthesis of silver is a green and sustainable approach.Clerodendron phlomoides leaves extract has been found to contain various phytochemicals,such as phenols,flavonoids,tannins,and alkaloids,which possess reducing and stabilizing properties that can aid the production of silver particles.In this paper,morphological and topographical analyses were performed on silver nanoparticles.The biosynthesized silver nanoparticles showed antimicrobial potential against wound pathogens.SEM and TEM micrographs revealed that the particles were sphere and nanosized,which makes them suitable for various biomedical applications.

Biosynthesis of Copper Nanoparticles using an Extract of the root of Asparagus racemosus:Basic Synthesis,Antimicrobial,and Anti-oxidant Potentials

Copper nanoparticles(CuNPs)have emerged as a promising alternative due to their unique antimicrobial properties.The synthesis of CuNPs using Asparagus racemosus,commonly known as Shatavari,offers a sustainable and environmentally friendly approach to producing nanomaterials.Moreover,the resulting CuNPs have been found to possess excellent antibacterial,and antioxidant properties,which further expands their potential applications in medicine and environmental remediation.In this article,we discussed the in vitro characterization of the CuNPs.In vitro studies revealed that CuNPs have the potential for biomedical applications and as a base nanomaterial for the construction of drug delivery and targeting vehicles.

In Vivo Studies and Flow Cytometric Investigation on Anticancer Potential of Selenium Nanoparticles Synthesized via Aqueous Extract of Clerodendron phlomidis

Nowadays,doctors and nutritionists recommend individuals incorporate selenium-rich foods such as nuts,cereals,and mushrooms into their regular diet to maintain fitness and overall health.Selenium nanoparticles(SeNPs)exhibit strong chemopreventive capabilities.The anticipations for SeNPs with enhanced and tunable bioactive activities have led to a keen interest in phytofabrication.In this study,the aqueous extract of Clerodendron phlomidis plant leaves was utilized for the synthesis of SeNPs.In traditional Indian medicine,this plant extract is recognized as a significant anti-diabetic agent.The flavonoids tetrahydroxylflavone,7-hydroxyflavanone,and 6,4’-dimethyl-7-acetoxy-scutellarein present in this plant leaf extract demonstrate excellent anticancer activity.These secondary metabolites exhibit the ability to reduce sodium selenite into SeNPs.At a concentration of 13μg/mL,the synthesized SeNPs effectively inhibited the proliferation of the HepG2 cell line.The results suggest that the SeNPs possess promising anti-cancer potential against liver cancer and can be considered as a therapeutic agent for liver cancer treatment.Additionally,the cell cycle arrest induced by SeNPs was further confirmed by the fluorescence-activated cell sorting(FACS)method,indicating that SeNPs could efficiently differentiate cancer cells from normal cells.Notably,it showed a significant improvement in diethylnitrosamine(DEN)-induced Swiss Wistar rat groups.This scientific investigation highlights the high anti-cancer potential of SeNPs,positioning them as a promising therapeutic agent for liver cancer treatment.

Identification of Dynamic Active Sites Among Cu Species Derived from MOFs@CuPc for Electrocatalytic Nitrate Reduction Reaction to Ammonia

Direct electrochemical nitrate reduction reaction(NITRR)is a promising strategy to alleviate the unbalanced nitrogen cycle while achieving the electrosynthesis of ammonia.However,the restructuration of the high-activity Cu-based electrocatalysts in the NITRR process has hindered the identification of dynamical active sites and in-depth investigation of the catalytic mechanism.Herein,Cu species(single-atom,clusters,and nanoparticles)with tunable loading supported on N-doped TiO_(2)/C are successfully manufactured with MOFs@CuPc precursors via the pre-anchor and post-pyrolysis strategy.Restructuration behavior among Cu species is co-dependent on the Cu loading and reaction potential,as evidenced by the advanced operando X-ray absorption spectroscopy,and there exists an incompletely reversible transformation of the restructured structure to the initial state.Notably,restructured CuN_(4)&Cu_(4) deliver the high NH_(3) yield of 88.2 mmol h^(−1)g_(cata)^(−1) and FE(~94.3%)at−0.75 V,resulting from the optimal adsorption of NO_(3)^(−) as well as the rapid conversion of^(*)NH_(2)OH to^(*)NH_(2) intermediates originated from the modulation of charge distribution and d-band center for Cu site.This work not only uncovers CuN_(4)&Cu_(4) have the promising NITRR but also identifies the dynamic Cu species active sites that play a critical role in the efficient electrocatalytic reduction in nitrate to ammonia.

Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer(BCa)is a leading cause of death in the female population across the globe.Approximately 2.3 million new BCa cases are recorded globally in females,overtaking lung cancer as the most prevalent form of cancer to be diagnosed.However,the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries.Early diagnosis is the only option to minimize the risks of BCa.Deep learning(DL)-based models have performed well in image processing in recent years,particularly convolutional neural network(CNN).Hence,this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram images.The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection.This proposed model is a multi-view-based computer-aided diagnosis(CAD)model,which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique(MLO)and cranio-caudal(CC).The digital mammogram images are collected from the digital database for screening mammography(DDSM)dataset.In preprocessing,median filter and contrast limited adaptive histogram equalization(CLAHE)techniques are utilized for image enhancement.After preprocessing,the segmentation is performed using the region growing(RG)algorithm.The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients(PHOG)and the AlextNet model.Finally,the classification is performed using the weighted k-nearest neighbor(WkNN)optimized with sequential minimal optimization(SMO).The classified images are evaluated based on accuracy,recall,precision,specificity,f1-score,and mathews correlation coefficient(MCC).Additionally,the false positive and error rates are evaluated.The proposed model obtained 98.57%accuracy,98.61%recall,99.25%specificity,98.63%precision,97.93%f1-score,96.26%MCC,0.0143 error rate,and 0.0075 false positive rate(FPR).Compared to the existing models,the research model has obtained better performances and outperformed the other models.

Energy Efficient Hyperparameter Tuned Deep Neural Network to Improve Accuracy of Near-Threshold Processor

When it comes to decreasing margins and increasing energy effi-ciency in near-threshold and sub-threshold processors,timing error resilience may be viewed as a potentially lucrative alternative to examine.On the other hand,the currently employed approaches have certain restrictions,including high levels of design complexity,severe time constraints on error consolidation and propagation,and uncontaminated architectural registers(ARs).The design of near-threshold circuits,often known as NT circuits,is becoming the approach of choice for the construction of energy-efficient digital circuits.As a result of the exponentially decreased driving current,there was a reduction in performance,which was one of the downsides.Numerous studies have advised the use of NT techniques to chip multiprocessors as a means to preserve outstanding energy efficiency while minimising performance loss.Over the past several years,there has been a clear growth in interest in the development of artificial intelligence hardware with low energy consumption(AI).This has resulted in both large corporations and start-ups producing items that compete on the basis of varying degrees of performance and energy use.This technology’s ultimate goal was to provide levels of efficiency and performance that could not be achieved with graphics processing units or general-purpose CPUs.To achieve this objective,the technology was created to integrate several processing units into a single chip.To accomplish this purpose,the hardware was designed with a number of unique properties.In this study,an Energy Effi-cient Hyperparameter Tuned Deep Neural Network(EEHPT-DNN)model for Variation-Tolerant Near-Threshold Processor was developed.In order to improve the energy efficiency of artificial intelligence(AI),the EEHPT-DNN model employs several AI techniques.The notion focuses mostly on the repercussions of embedded technologies positioned at the network’s edge.The presented model employs a deep stacked sparse autoencoder(DSSAE)model with the objective of creating a variation-tolerant NT processor.The time-consuming method of modifying hyperparameters through trial and error is substituted with the marine predators optimization algorithm(MPO).This method is utilised to modify the hyperparameters associated with the DSSAE model.To validate that the proposed EEHPT-DNN model has a higher degree of functionality,a full simulation study is conducted,and the results are analysed from a variety of perspectives.This was completed so that the enhanced performance could be evaluated and analysed.According to the results of the study that compared numerous DL models,the EEHPT-DNN model performed significantly better than the other models.

Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

Increase in the use of internet of things owned devices is one of the reasonsforincreasednetworktraffic.Whileconnectingthesmartdeviceswith publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system.The Phishing is the slow and resilient attack stacking techniques probe the users.The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm(NWAT).Though Predictive mapping,Predictive Validation and Predictive analysis mechanism is developed.For the test purpose Canadian Institute of cyber security(CIC)dataset is used for creating a robust prediction model.This attack generates a resilience corruption works that slowly gathers the credential information from the mobiles.The proposed Predictive analysis model(PAM)enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT networks.The prediction system considers the peer-to-peer communication window open for the established communication,the suspicious process and its pattern is identified by the new approach.The proposed model is validated by finding thepredictionaccuracy,Precision,recallsF1score,errorrate,Mathew’sCorre-lationCoefficient(MCC)andBalancedDetectionRate(BDR).Thepresented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

Analysis of a Cashew Shell and Fly Ash Rich Brake Liner Composite Material

Hybrid materials collected from organic and inorganic sources,which are traditionally used as brake lining materials,generally include fly ash,cashew shell powder,phenolic resins,aluminium wool,barites,lime powder,carbon powder and copper powder.The present research focuses on the specific effects produced by fly ash and aims to provide useful indications for the replacement of asbestos due to the health hazards caused by the related fibers.Furthermore,the financial implications related to the use of large-volume use of fly ash,lime stone and cashew shell powder,readily available in most countries in the world,are also discussed.It is shown that many manufacturing and automotive industries,which are currently experiencing difficulties in meeting the increasing demand for brake lining material,may take advantage from the proposed solution.

Design of Clustering Techniques in Cognitive Radio Sensor Networks

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

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.

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.