Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation services.However,it also provides a sequence of novel cyber-security issues.Although IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers.Cyberattacks result in financial loss and data breaches for organizations and individuals.So,it becomes crucial to secure the IoT environment from such cyberattacks.With this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT environment.The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT envi-ronment.To attain this,the ECSADL-CAD model initially pre-processes the data.In the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack detection.At last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model.The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

Deep Consensus Network for Recycling Waste Detection in Smart Cities

Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management.Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials.This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart Cities.The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones.The proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input image.For improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is exploited.Finally,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones.The performance validation of theDCNWO-RWOD technique takes place using the open access dataset.The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

Multi-Versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis on Arabic Corpus

Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.

Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male users.Efforts had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for Arabic.Most of the research works are done mainly for English and least amount of effort for non-English language.The study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for Twitter.Therefore,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)model.The presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter data.To attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word embedding.Next,the MSDAE model is exploited for the identification of gender into two classes namely male and female.In the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our work.The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics.The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model

Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent years.Both Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective identification and classification of cyberattacks.In addition,the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification models.In this background,the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning(ICC-CGODL)Model.The goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon data.Besides,ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform format.In addition,Bidirectional Gated Recurrent Unit(BiGRU)model is utilized for detection and classification of cyberattacks.Moreover,CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work.A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.

Convolutional Deep Belief Network Based Short Text Classification on Arabic Corpus

With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day.In order to develop a classification system for the Arabic lan-guage there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective.In this view,this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification(DSOCDBN-STC)model on Arabic Corpus.The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media.The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage.Besides,the DSOCDBN-STC model involves CDBN based classification model for Arabic short text.At last,the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method.To establish the enhanced performance of the DSOCDBN-STC model,a wide range of simulations have been performed.The simulation results con-firmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,etc.Since UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs.In this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV networks.The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification.At the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize clusters.Besides,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV degree.For classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)classifier.For ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be resolved.Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks.Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs improvement.In this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G environment.The proposed PPBDLIIoT technique aims at identifying the existence of intrusions in network.Further,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the network.Besides,CGO technique is applied to fine tune the hyperparameters in BiGRNN model.CGO algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model.Moreover,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system.The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various measures.The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.