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Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models. [ABSTRACT FROM AUTHOR]

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 nations written 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 optimization with the deep learning-based AHCR (FWODL-AHCR) technique. The major 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. [ABSTRACT FROM AUTHOR]

In recent days, supply chain and logistic industries have been going through a transformational wave of automation and digitization. Supply chain management (SCM) can involve machine learning (ML) abilities and prediction models to ensure that the demands are satisfied at a minimum cost. Intelligent models can be developed to determine whether adequate inventory is accessible to encounter the predictable rise in demands, and if not, the system spontaneously begins to adjust the orders with suppliers to source the raw materials for resolving the predicted future high demand. The conventional ways of SCM can be replaced by the design of recent artificial intelligence (AI) and deep learning (DL) techniques. By this motivation, this research presents a tree seed algorithm-based feature selection with optimum DL technique for supply chain management (TSA-ODLSCM). The proposed TSA-ODLSCM model involves the design of a feature subset selection approach using a tree search algorithm (TSA) algorithm. Besides, a new convolutional neural network with fuzzy cognitive maps (CNN-FCM) technique is designed for the classification process. Moreover, optimal parameter tuning of the CNN-FCM model was performed using the Henry gas solubility optimization (HGSO) technique. To exhibit the improved performance of the TSA-ODLSCM approach, a huge range of simulations were executed and outcomes were examined below several aspects. The experimental validation reported an enhanced 96.52% outcome of the TSA-ODLSCM approach over other methods. [ABSTRACT FROM AUTHOR]

In recent years, the usage of social networking sites has considerably increased in the Arab world. It has empowered individuals to express their opinions, especially in politics. Furthermore, various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales. This is attributed to business owners' understanding of social media's importance for business development. However, the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns. Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies. In this background, the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection (CEHOML-HSD) model in the context of the Arabic language. The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal. To attain this, the CEHOML-HSD model follows different sub-processes as discussed herewith. At the initial stage, the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer. Secondly, the Support Vector Machine (SVM) model is utilized to detect and classify the hate speech texts made in the Arabic language. Lastly, the CEHO approach is employed for fine-tuning the parameters involved in SVM. This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm. The design of the CEHO algorithm for parameter tuning shows the novelty of the work. A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach. The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches. [ABSTRACT FROM AUTHOR]

Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites' finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a DeerHuntingOptimizationwithDeep BeliefNetwork Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented DHODBN-ECmodel aims to examine the existence of distinct emotion classes in tweets. At the introductory level, the DHODBN-EC technique pre-processes the tweets at different levels. Besides, the word2vec feature extraction process is applied to generate the word embedding process. For emotion classification, the DHODBN-EC model utilizes the DBN model, which helps to determine distinct emotion class labels. Lastly, the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique. An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach. A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%. [ABSTRACT FROM AUTHOR]

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 presentedMVODRL-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, theMVO 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%. [ABSTRACT FROM AUTHOR]

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful IntrusionDetection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODLID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches. [ABSTRACT FROM AUTHOR]

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes. When the number of labels is limited to one, the task becomes single-label text categorization. The Arabic texts include unstructured information also like English texts, and that is understandable for machine learning (ML) techniques, the text is changed and demonstrated by numerical value. In recent times, the dominant method for natural language processing (NLP) tasks is recurrent neural network (RNN), in general, long short term memory (LSTM) and convolutional neural network (CNN). Deep learning (DL) models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection, medical image analysis, and so on. This paper introduces a Modified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition (MDFO-EMTRR) model on Arabic Corpus. The presented MDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text. To achieve this, the MDFO-EMTRR technique encompasses data pre-processing to transform the input data into compatible format. Next, the ELM model is utilized for the representation and recognition of the Arabic text. At last, the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%. [ABSTRACT FROM AUTHOR]

Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days. ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text. Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons. In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools. These sources were used for processing and analyzing the Arabic language content like lexicons. However, an important challenge in this domain is the unavailability of sufficient and reliable resources. In this background, the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification (BROFDL-AASC) technique. The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language. In the presented BROFDL-AASC model, data pre-processing is performed at first to convert the input data into a useful format. Besides, the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine (DFRBM) model for the identification and categorization of sentiments. Furthermore, the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model. This scenario establishes the novelty of current study. The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimizationbased machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 language 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 (DSOCDBNSTC) model on Arabic Corpus. The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media. The presented DSOCDBNSTC 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 confirmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%. [ABSTRACT FROM AUTHOR]

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL- CTD model with increased accuracy of 99.47%. [ABSTRACT FROM AUTHOR]

Combined Economic and Emission Dispatch (CEED) task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs. The disadvantage of the conventional method is its incapability to avoid falling in local optimal, particularly when handling nonlinear and complex systems. Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box. Therefore, this paper focuses on the design of an improved sand cat optimization algorithm based CEED (ISCOA-CEED) technique. The ISCOA-CEED technique majorly concentrates on reducing fuel costs and the emission of generation units. Moreover, the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints. Besides, the improved sand cat optimization algorithm (ISCOA) is derived from the integration of traditional SCOA with the Levy Flight (LF) concept. At last, the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue. The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, ehealthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively. [ABSTRACT FROM AUTHOR]

Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts' knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%. [ABSTRACT FROM AUTHOR]

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDCCMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures. [ABSTRACT FROM AUTHOR]

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches. [ABSTRACT FROM AUTHOR]