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.

Learning Discriminatory Information for Object Detection on Urine Sediment Image

In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.

Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

Recognition of Film Type Using HSV Features on Deep-Learning Neural Networks

The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition system of film types. Initially, a film is firstly sampled as frame sequences. The color space, including hue, saturation,and brightness value(HSV), is analyzed for each sampled frame by computing the deviation and mean of HSV for each film. These features are utilized as inputs to a deep-learning neural network(DNN) for the recognition of film types. One hundred films are utilized to train and validate the model parameters of DNN. In the testing phase, a film is recognized as one of the five categories, including action, comedy, horror thriller, romance, and science fiction, by the trained DNN. The experimental results reveal that the film types can be effectively recognized by the proposed approach, enabling the viewer to select an interesting film accurately and quickly.

Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst drones.While drones are naturally mobile,it undergoes frequent topological changes.Such alterations in the topology cause route election,stability,and scalability problems in IoD.Encryption is considered as an effective method to transmit the images in IoD environment.The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)environment.The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones.The presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction technique.In addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment.This process enables the secure transmission of images to the ground station.In order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different aspects.The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

Preliminary Exploration on the Application of Saussure Sign Concept in Bio-Inspired Design: A Case of Tiantoushui Doll-Design

Simply, shape bionics is to extract an image feature of natural objects, and most of the techniques for transforming and presenting are focused on metaphorical effects. In particular, the extraction process of metaphor design seems to be related to the concept of Saussure signs. Therefore, this research took the doll design of the Tiantoushui community in Changhua County, Taiwan as an example and attempted to use the Saussure concept to extract and evaluate the signs of bio-inspired design. The results showed that a total of three doll works symbolized respectively rice, guava, and pomelo implied the signification of local region by integrating the Saussure code image with the sign-image of the regional nature and transforming its content into “commodity design” and further transferring into “the product value of cultural and creative”. In addition, the specific extraction process included 1) Extraction of image—adjectives in creative products;2) Meaning space of image—adjec- tives in creative products;3) Extraction and determination of design elements of creative products, and 4) Combined the sensibility of creative products and design elements to derive design rules. After preliminary exploration of the extraction steps above, a concrete and efficient bio-inspired design process was proposed in this work. Besides, it was inferred that if further combined with the post-modern design style, it may not only simplify the design elements of creative products but also enhance the design connotations in the future.

From Usability to Pleasure: A Case Study of Difference in Users’ Preference

For past decades, research of designing “pleasure” into products in the academic community has produced a multitude of evaluation models and frameworks. These models address the critical issues of pleasurable product design leading to emotional design. This study is intended to explore the change from the need of “usability” for the product design to the need of “pleasure” for the user experience. The questionnaires were used to obtain data from 343 subjects. The four keyboard designs were adopted in the experiment to study the difference and the change from “usability” to “pleasure” of users” preference. The results show that the need for pleasure is higher than usability, as well as attractive things also transmit the feel of work better. Besides, preference is related to gender, age, major, and educational background. Results presented herein provide designers with a valuable reference for examining the way how to design “pleasure” into product and the interactive experience of users in the design process.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial ones.Since nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes.The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate.With this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for WSN.The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes.In addition,the IAOAB-NLS model is stimulated by the behaviour of Aquila.The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network.For guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS model.The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective manner.When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection methods.Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical systems.Malicious software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the information.The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)model.The presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud platform.To attain this,Zscore data normalization is utilized to scale the data into a uniform format.In addition,BDDBN model is also exploited for recognition and categorization of malware.To effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is applied.This scenario enhances the classification results and also shows the novelty of current study.The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent years.The most commonly-utilized NLP task is the‘Text Classification’process.Its main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined categories.In ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the method.One of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best outcomes.The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this background.The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process.For Arabic text classification,the DHBM technique is utilized.This technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification process.To adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this study.The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model.The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

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.

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%.

Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and industry.Image encryption is important for securing remote sensing images in the IoT environment.Recently,researchers have introduced plenty of algorithms for encrypting images.This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT Environment.The proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key generation.The remote sensing images were preprocessed at the initial stage to enhance the image quality.Next,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the images.The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)approach.The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images.Then,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence level.For an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were conducted.The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.