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Sign language (SL) recognition for individuals with hearing disabilities involves leveraging machine learning (ML) and computer vision (CV) approaches for interpreting and understanding SL gestures. By employing cameras and deep learning (DL) approaches, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), these models analyze facial expressions, hand movements, and body gestures connected with SL. The major challenges in SL recognition comprise the diversity of signs, differences in signing styles, and the need to recognize the context in which signs are utilized. Therefore, this manuscript develops an SL detection by Improved Coyote Optimization Algorithm with DL (SLR-ICOADL) technique for hearing disabled persons. The goal of the SLR-ICOADL technique is to accomplish an accurate detection model that enables communication for persons using SL as a primary case of expression. At the initial stage, the SLR-ICOADL technique applies a bilateral filtering (BF) approach for noise elimination. Following this, the SLR-ICOADL technique uses the Inception-ResNetv2 for feature extraction. Meanwhile, the ICOA is utilized to select the optimal hyperparameter values of the DL model. At last, the extreme learning machine (ELM) classification model can be utilized for the recognition of various kinds of signs. To exhibit the better performance of the SLR-ICOADL approach, a detailed set of experiments are performed. The experimental outcome emphasizes that the SLR-ICOADL technique gains promising performance in the SL detection process.

Fall detection (FD) for disabled persons in the Internet of Things (IoT) platform contains a combination of sensor technologies and data analytics for automatically identifying and responding to samples of falls. In this regard, IoT devices like wearable sensors or ambient sensors from the personal space role a vital play in always monitoring the user's movements. FD employs deep learning (DL) in an IoT platform using sensors, namely accelerometers or depth cameras, to capture data connected to human movements. DL approaches are frequently recurrent neural networks (RNNs) or convolutional neural networks (CNNs) that have been trained on various databases for recognizing patterns connected with falls. The trained methods are then executed on edge devices or cloud environments for real-time investigation of incoming sensor data. This method differentiates normal activities and potential falls, triggering alerts and reports to caregivers or emergency numbers once a fall is identified. We designed an Artificial Rabbit Optimizer with a DL-based FD and classification (ARODL-FDC) system from the IoT environment. The ARODL-FDC approach proposes to detect and categorize fall events to assist elderly people and disabled people. The ARODL-FDC technique comprises a four-stage process. Initially, the preprocessing of input data is performed by Gaussian filtering (GF). The ARODL-FDC technique applies the residual network (ResNet) model for feature extraction purposes. Besides, the ARO algorithm has been utilized for better hyperparameter choice of the ResNet algorithm. At the final stage, the full Elman Neural Network (FENN) model has been utilized for the classification and recognition of fall events. The experimental results of the ARODL-FDC technique can be tested on the fall dataset. The simulation results inferred that the ARODL-FDC technique reaches promising performance over compared models concerning various measures.

Gastric Cancer (GC) has been identified as the world's fifth most general tumor. So, it is important to diagnose the GC at initial stages itself to save the lives. Histopathological analysis remains the gold standard for accurate diagnosis of the disease. Though Computer-Aided Diagnostic approaches are prevalently applied in recent years for the diagnosis of diseases, it is challenging to apply in this case, due to the lack of accessible gastric histopathological image databases. With a rapid progression in the Computer Vision (CV) technologies, particularly, the emergence of medicinal image classifiers, it has become feasible to examine all the types of electron micrographs in a rapid and an effective manner. Therefore, the current research article presents an Anas Platyrhynchos Optimizer with Deep Learning-based Gastric Cancer Classification (APODL-GCC) method for the classification of GC using the endoscopic images. The aim of the proposed APODL-GCC method is to identify the presence of GC with the help of CV and Deep Learning concepts. Primarily, the APODL-GCC technique employs a contrast enhancement technique. Next, the feature extraction process is performed using a neural architectural search network model to generate a collection of feature vectors. For hyperparameter optimization, the Anas Platyrhynchos Optimizer (APO) algorithm is used which enhances the classification performance. Finally, the GC classification process is performed using the Deep Belief Network method. The proposed APODL-GCC technique was simulated using medical images and the experimental results established that the APODL-GCC technique accomplishes enhanced performance over other models.

Recently, deep learning models have become more prominent due to their tremendous performance for real-time tasks like face recognition, object detection, natural language processing (NLP), instance segmentation, image classification, gesture recognition, and video classification. Image captioning is one of the critical tasks in NLP and computer vision (CV). It completes conversion from image to text; specifically, the model produces description text automatically based on the input images. In this aspect, this article develops a Lighting Search Algorithm (LSA) with a Hybrid Convolutional Neural Network Image Captioning System (LSAHCNN-ICS) for NLP. This introduced LSAHCNN-ICS system develops an end-to-end model which employs convolutional neural network (CNN) based ShuffleNet as an encoder and HCNN as a decoder. At the encoding part, the ShuffleNet model derives feature descriptors of the image. Besides, in the decoding part, the description of text can be generated using the proposed hybrid convolutional neural network (HCNN) model. To achieve improved captioning results, the LSA is applied as a hyperparameter tuning strategy, representing the innovation of the study. The simulation analysis of the presented LSAHCNN-ICS technique is performed on a benchmark database, and the obtained results demonstrated the enhanced outcomes of the LSAHCNN-ICS algorithm over other recent methods with maximum Consensus-based Image Description Evaluation (CIDEr Code) of 43.60, 59.54, and 135.14 on Flickr8k, Flickr30k, and MSCOCO datasets correspondingly.

5G networks offer high-speed, low-latency communication for various applications. As 5G networks introduce new capabilities and support a wide range of services, they also become more vulnerable to different kinds of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Effective DDoS attack classification in 5G networks is a critical aspect of ensuring the security, availability, and performance of these advanced communication infrastructures. In recent days, machine learning (ML) and deep learning (DL) models can be employed for an accurate DDoS attack detection process. In this aspect, this study designs a Modified Equilibrium Optimization Algorithm with Deep Learning based DDoS Attack Classification (MEOADL-ADC) method in 5G networks. The goal of the MEOADL-ADC technique is the automated classification of DDoS attacks in the 5G network. The MEOADL-ADC technique follows a three-stage process such as feature selection, classification, and hyperparameter tuning. Primarily, the MEOADL-ADC technique employs MEOA based feature selection approach. Next, the MEOADL-ADC technique utilizes the long short-term memory (LSTM) model for the classification of DDoS attacks. Finally, the tunicate swarm algorithm (TSA) is exploited to adjust the hyperparameter of the LSTM model. The design of MEOA-based feature selection and TSA-based hyperparameter tuning shows the novelty of the work. The experimental outcome of the MEOADL-ADC method is tested on a benchmark dataset, and the outcomes indicate the betterment of the MEOADL-ADC algorithm over the current methods with maximum accuracy of 97.60%.

Image captioning is a deep learning technique that intends to create and generate textual descriptions or captions for images. It integrates computer vision and natural language processing (NLP) to comprehend the visual content of an image and generate human-like descriptions. Deep learning (DL) based image captioning models can be trained on large-scale datasets, allowing them to generalize various types of images and generate captions that apply to a wide range of visual scenarios. By combining computer vision and natural language processing, DL-enabled image captioning models can understand both visual and textual information, which enables them to generate captions that not only describe the visual content but also incorporate contextual and semantic information. This study develops an Automated Image Captioning using Sparrow Search Algorithm with Improved Deep Learning (AIC-SSAIDL) technique. The major intention of the AIC-SSAIDL technique lies in the automated generation of textual captions for the input images. To accomplish this, the AIC-SSAIDL technique utilizes the MobileNetv2 model to generate feature descriptors of the input images and its hyperparameter tuning process takes place using SSA. For the image captioning process, the AIC-SSAIDL technique utilizes an attention mechanism with long short-term memory (AM-LSTM) network. Finally, the hyperparameter selection of the AM-LSTM model is performed by the fruit fly optimization (FFO) algorithm. A wide range of experiments has been conducted on benchmark data to depict the better performance of the AIC-SSAIDL method. The comprehensive result analysis highlighted the enhanced captioning results of the AIC-SSAIDL method with maximum CIDEr of 46.12, 61.89, and 137.45 on Flickr8k, Flickr30k, and MSCOCO datasets, respectively.

Text information is a natural language dependent, which enhances reliability and security of text data exchanged through Internet networks and is becoming main concern for researchers. Content authentication, Tampering detection, and integrity verification of digital content are considered challenges in the information and communication exchange area through the Internet. Watermarking algorithms emerge as a potential method for achieving this with text. In simple words, text watermarking is embedding imperceptible text messages within the content that serve as unique signatures or identifiers. The text message has data regarding the content creator, copyright data, or other metadata utilized for authentication purposes. Therefore, this study presents a new Coyote Optimization Algorithm with Watermarking-based Content Authentication and Tampering Detection (COAW-CATD) technique for English text. The presented COAW-CATD technique aims to secure the English text via content authentication and tampering recognition. To accomplish this, the presented COAW-CATD technique designs a zero-watermarking (ZWM) approach to produce watermarks depending on the textual content. The generated watermark can be extracted to assure the authentication of the text document. Moreover, the COA can be utilized to optimize the placement of the watermarks in the content to ensure that it is imperceptible and robust to tampering. The experimental outcomes of the COAW-CATD algorithm are tested utilizing a series of simulations and the comparative study reported its superior performance with improved tampering detection accuracy of 95.93%.

The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices. FL clients frequently use Wi-Fi and have to interact in unstable network surroundings. However, as the present FL aggregation approaches receive and send a large number of weights, accuracy can be decreased considerably in unstable network surroundings. Therefore, this study presents a Quantum with Metaheuristics Algorithm Based Minimization of Communication Costs in Federated Learning (QMAMCC-FL) technique. The presented QMAMCC-FL technique is designed a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model with a quantum Aquila optimization (QAO) algorithm. The QMAMCC-FL technique upgrades the global model via weight collection of the learned model, which is commonly used in FL. The proposed model can be employed to increase the performance of network communication and reduce the size of data transmitted from clients to servers such as smartphones and tablets. The experimental analysis of the QMAMCC-FL approach is tested, and the outcomes show better performance over other existing models.

Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs a novel UAV assisted DL based image classification model (UAVDL-ICM) for Industry 4.0 environment. The proposed UAVDL-ICM technique involves an ensemble of voting based three DL models, namely Residual network (ResNet), Inception with ResNetv2, and Densely Connected Networks (DenseNet). Also, the hyperparameter tuning of these DL models takes place using a genetic programming (GP) approach. Finally, Oppositional Water Wave Optimization (OWWO) with Fully Connected Deep Neural networks (FCDNN) is employed for the classification of aerial images. A wide range of simulations takes place and the results are examined in terms of different parameters. A detailed comparative study highlighted the betterment of the UAVDL-ICM technique compared to other recent approaches.

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

Although numerous case studies have determined the critical success factors (CSFs) for enterprise resource planning (ERP) during the adoption and implementation stages, empirical investigations of CSFs for ERP in post-implementation stages (after going live) are in scarcity. As such, this study examined the influence of top management support and vendor support as CSFs on the post-implementation stage of ERP systems in small and medium enterprises (SMEs) established in the Kingdom of Saudi Arabia (KSA). A total of 177 end-users of ERP systems from two manufacturing organizations in KSA that had implemented on-premises ERP systems were involved in this study. Data gathered from structured questionnaires were analyzed using SmartPLS3 and SPSS software programs. The regression analysis was performed to assess the correlations among the variables. Out of seven CSFs identified from the literature, the impact of top management support was significant on user training, competency of internal Information Technology (IT) department, and effective communication between departments, but insignificant on continuous vendor support. Meanwhile, continuous vendor support had a significant influence on continuous integration of the system, but was insignificant on user interfaces and custom code. The study outcomes may serve as practical guidance for effective post-implementation in ERP systems. Referring to the proposed research model, ERP post-implementation success in KSA was significantly influenced by top management support, whereas continuous vendor support displayed a substantial impact on the continuous integration of ERP systems.

The particulate matter (PM) is emitted from diverse sources and affects the human health very badly. In the past, researchers applied different automated computational tools in the predication of PM. Accurate prediction of PM requires more relevant features and feature importance. In this research, we first extracted the multimodal features from time domain standard deviation average (SDAPM), standard deviation of standard deviation (SDSD), standard deviation of particulate matter (SDPM), root-mean square of standard deviation (RMSSD), and nonlinear dynamical measure wavelet entropy (WE) – Shannon, norm, threshold, multiscale entropy based on KD tree (MSEKD), and multiscale approximate entropy (MAEnt). We then applied the intelligent-based Bayesian inference approach to compute the strength of relationship among multimodal features. We also computed total incoming and outgoing forces between the features (nodes). The results reveal that there was a very highly significant correlation (p-value

Antibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. For reducing the antibiotics from the industrial wastewater, the broadcast efforts of the experiments are a little complicated. In the proposed method, Gaussian process regression (GPR), active learning (AL), and ANN are used for predicting the antibiotic levels in the industrial wastewater. Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. To evaluate TC and SMX adsorption on CBMs, we used comparative significance investigation and partial trust plots based on ML models. The proposed GPR reduces the antibiotics in wastewater; minimal experimental screening and the comparative significance and partial trust plot help in the treatment of wastewater.

Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in waste water treatment and transfer data via internet of things (IoT). Prediction of wastewater quality content which is presence of total nitrogen (T-N) and total phosphorous (T-P) elements, chemical oxygen demand (COD), biochemical demand (BOD), and total suspended solids (TSS) is associated with eutrophication that should be prevented. This may leads to algal bloom and spoils aquatic life which is consumed by human. The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented. Adsorption of T-N and T-P activated carbon was predictable as one of the most promising methods for wastewater treatment. Many research works have been done. The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. The accuracy of the BKNN-ELM algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is K=9 and k=10 with rate of accuracy which is 93.56%, and the lowest accuracy is K=1 of 65.34%. Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%. The relative error rate of prediction is 12.03 which is lesser than existing models.

Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems. Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms. This research used machine learning (ML) techniques to generate general adsorption forecasting model for sulfamethoxazole (SMX) and tetracycline (TC) on CBM. Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations. In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the CO2 concentration and flows in units of rate of a reaction characteristics, an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods. Their relevance and complete dependency graph evaluation lead reasonable CBM uses for antimicrobial wastewater treatment. Also, machine learning forecasting model with good generalization capability is useful for building effective CBMs with few empirical screens. It evaluates the accuracy, precision, recall, false positive rate (FPR), and false negative rate (FNR) and also reduces the experimental screening.

Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97.

Cloud computing (CC) refers to an Internet-based computing technology in which shared resources, such as storage, software, information, and platform, are offered to users on demand. CC is a technology through which virtualized and dynamically scalable resources are presented to users on the Internet. Security is highly significant in this on-demand CC. Therefore, this paper presents improved metaheuristics with a fuzzy logic-based intrusion detection system for the cloud security (IMFL-IDSCS) technique. The IMFL-IDSCS technique can identify intrusions in the distributed CC platform and secure it from probable threats. An individual sample of IDS is deployed for every client, and it utilizes an individual controller for data management. In addition, the IMFL-IDSCS technique uses an enhanced chimp optimization algorithm-based feature selection (ECOA-FS) method for choosing optimal features, followed by an adaptive neuro-fuzzy inference system (ANFIS) model enforced to recognize intrusions. Finally, the hybrid jaya shark smell optimization (JSSO) algorithm is used to optimize the membership functions (MFs). A widespread simulation analysis is performed to examine the enhanced outcomes of the IMFL-IDSCS technique. The extensive comparison study reported the enhanced outcomes of the IMFL-IDSCS model with maximum detection efficiency with accuracy of 99.31%, precision of 92.03%, recall of 78.25%, and F-score of 81.80%.

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.

Wireless sensor networks (WSNs) have been developed recently to support several applications, including environmental monitoring, traffic control, smart battlefield, home automation, etc. WSNs include numerous sensors that can be dispersed around a specific node to achieve the computing process. In WSNs, routing becomes a very significant task that should be managed prudently. The main purpose of a routing algorithm is to send data between sensor nodes (SNs) and base stations (BS) to accomplish communication. A good routing protocol should be adaptive and scalable to the variations in network topologies. Therefore, a scalable protocol has to execute well when the workload increases or the network grows larger. Many complexities in routing involve security, energy consumption, scalability, connectivity, node deployment, and coverage. This article introduces a wavelet mutation with Aquila optimization-based routing (WMAO-EAR) protocol for wireless communication. The presented WMAO-EAR technique aims to accomplish an energy-aware routing process in WSNs. To do this, the WMAO-EAR technique initially derives the WMAO algorithm for the integration of wavelet mutation with the Aquila optimization (AO) algorithm. A fitness function is derived using distinct constraints, such as delay, energy, distance, and security. By setting a mutation probability P, every individual next to the exploitation and exploration phase process has the probability of mutation using the wavelet mutation process. For demonstrating the enhanced performance of the WMAO-EAR technique, a comprehensive simulation analysis is made. The experimental outcomes establish the betterment of the WMAO-EAR method over other recent approaches.

Gliomas are tumors of the central nervous system, which usually start within the glial cells of the brain or the spinal cord. These are extremely migratory and diffusive tumors, which quickly expand to the surrounding regions in the brain. There are different grades of gliomas, hinting about their growth patterns and aggressiveness and potential response to the treatment. As part of routine clinical procedure for gliomas, both radiology images (rad), such as multiparametric MR images, and digital pathology images (path) from tissue samples are acquired. Each of these data streams are used separately for prediction of the survival outcome of gliomas, however, these images provide complimentary information, which can be used in an integrated way for better prediction. There is a need to develop an image-based method that can utilise the information extracted from these imaging sequences in a synergistic way to predict patients’ outcome and to potentially assist in building comprehensive and patient-centric treatment plans. The objective of this study is to improve survival prediction outcomes of gliomas by integrating radiology and pathology imaging. Multiparametric magnetic resonance imaging (MRI), rad images, and path images of glioma patients were acquired from The Cancer Imaging Archive. Quantitative imaging features were extracted from tumor regions in rad and path images. The features were given as input to an ensemble regression machine learning pipeline, including support vector regression, AdaBoost, gradient boost, and random forest. The performance of the model was evaluated in several configurations, including leave-one-out, five-fold cross-validation, and split-train-test. Moreover, the quantitative performance evaluations were conducted separately in the complete cohort (n = 171), high-grade gliomas (HGGs), n = 75, and low-grade gliomas (LGGs), n = 96. The combined rad and path features outperformed individual feature types in all the configurations and datasets. In leave-one-out configuration, the model comprising both rad and path features was successfully validated on the complete dataset comprising HGFs and LGGs (R=0.84 p=2.2×10−16). The Kaplan–Meier curves generated on the predictions of the proposed model yielded a hazard ratio of 3.314 [95%CI:1.718−6.394], log−rank(P)=2×10−4 on combined rad and path features. Conclusion: The proposed approach emphasizes radiology experts and pathology experts’ clinical workflows by creating prognosticators upon ‘rad’ radiology images and digital pathology ‘path’ images independently, as well as combining the power of both, also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas.

Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures.

The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively.

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.

Cyber security is becoming a challenging issue, because of the growth of the Internet of Things (IoT), in which an immense quantity of tiny smart gadgets push trillions of bytes of data over the Internet. Such gadgets have several security flaws, due to a lack of hardware security support and defense mechanisms, thus, making them prone to cyber-attacks. Moreover, IoT gateways present limited security features for identifying such threats, particularly the absence of intrusion detection techniques powered by deep learning (DL). Certainly, DL methods need higher computational power that exceeds the capability of such gateways. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies a DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process. The experimental validation of the PODCNN-LWID model is carried out on a benchmark dataset, and the results are assessed using varying measures. The comparison study reports the enhancements of the PODCNN-LWID model over other approaches.

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques.

Recently, Telehealth connects patients to vital healthcare services via remote monitoring, wireless communications, videoconferencing, and electronic consults. By increasing access to specialists and physicians, telehealth assists in ensuring patients receive the proper care at the right time and right place. Teleophthalmology is a study of telemedicine that provides services for eye care using digital medical equipment and telecommunication technologies. Multimedia computing with Explainable Artificial Intelligence (XAI) for telehealth has the potential to revolutionize various aspects of our society, but several technical challenges should be resolved before this potential can be realized. Advances in artificial intelligence methods and tools reduce waste and wait times, provide service efficiency and better insights, and increase speed, the level of accuracy, and productivity in medicine and telehealth. Therefore, this study develops an XAI-enabled teleophthalmology for diabetic retinopathy grading and classification (XAITO-DRGC) model. The proposed XAITO-DRGC model utilizes OphthoAI IoMT headsets to enable remote monitoring of diabetic retinopathy (DR) disease. To accomplish this, the XAITO-DRGC model applies median filtering (MF) and contrast enhancement as a pre-processing step. In addition, the XAITO-DRGC model applies U-Net-based image segmentation and SqueezeNet-based feature extractor. Moreover, Archimedes optimization algorithm (AOA) with a bidirectional gated recurrent convolutional unit (BGRCU) is exploited for DR detection and classification. The experimental validation of the XAITO-DRGC method can be tested using a benchmark dataset and the outcomes are assessed under distinct prospects. Extensive comparison studies stated the enhancements of the XAITO-DRGC model over recent approaches.

As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency-inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques.

Nowadays, unmanned aerial vehicles (UAVs) have gradually attracted the attention of many academicians and researchers. The UAV has been found to be useful in variety of applications, such as disaster management, intelligent transportation system, wildlife monitoring, and surveillance. In UAV aerial images, learning effectual image representation was central to scene classifier method. The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. Due to the limited resource in UAVs, it can be difficult to fine-tune the hyperparameter and the trade-offs amongst computation complexity and classifier results. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model involves the proper identification and classification of images into distinct kinds. For accomplishing this, the presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy. To assure the better performance of the SIDTLD-AIC model, a wide range of simulations are implemented and the outcomes are investigated in many aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches.

In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

Recent developments of semiconductor and communication technologies have resulted in the interconnection of numerous devices in offering seamless communication and services, which is termed as Internet of Everything (IoE). It is a subset of Internet of Things (IoT) which finds helpful in several applications namely smart city, smart home, precise agriculture, healthcare, logistics, etc. Despite the benefits of IoE, it is limited to processing and storage abilities, resulting in the degradation of device safety, privacy, and efficiency. Security and privacy become major concerns in the transmission of multimedia data over the IoE network. Encryption and image steganography is considered effective solutions to accomplish secure data transmission in the IoE environment. For resolving the limitations of the existing works, this article proposes an optimal multikey homomorphic encryption with steganography approach for multimedia security (OMKHES-MS) technique in the IoE environment. Primarily, singular value decomposition (SVD) model is applied for the separation of cover images into RGB elements. Besides, optimum pixel selection process is carried out using coyote optimization algorithm (COA). At the same time, the encryption of secret images is performed using poor and rich optimization (PRO) with multikey homomorphic encryption (MKHE) technique. Finally, the cipher image is embedded into the chosen pixel values of the cover image to generate stego image. For assessing the better outcomes of the OMKHES-MS model, a wide range of experiments were carried out. The extensive comparative analysis reported the supremacy of the proposed model over the rennet approaches interms of different measures.

Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394.