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Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback–Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research. [ABSTRACT FROM AUTHOR]

• A Hybrid Deep Learning with Improved Salp Swarm Optimization-Based Multi-Class Grape Disease Classification (HDLISSA-MGDC) model is proposed. • The Median Filtering (MF) technique is applied for image pre-processing of the HDLISSA-MGDC. • grape disease classification, the ISSA with Convolutional Neural Network-Gated Recurrent Unit (CNN-GRUs) model is employed. • The proposed HDLISSA-MGDC model was experimentally validated using the plant leaf disease dataset. The recent revolutions in Computer Vision (CV) and Artificial Intelligence (AI) techniques have found many applications in grapevine and smart agriculture processes. Recently, Deep Learning (DL) techniques like Convolutional Neural Networks (CNN) have been broadly applied in smart agriculture, leaf disease recognition, and scene perception. In this background, the current study develops a Hybrid Deep Learning with Improved Salp Swarm Optimization-based Multi-class Grape Disease Classification (HDLISSA-MGDC) model. The proposed HDLISSA-MGDC model focuses on the classification of grape leaf images into four distinct classes such as black measles, black rot, Isariopsis leaf spot and healthy. Initially, the Median Filtering (MF) technique is applied for image pre-processing, which eliminates the noise present in the images. In addition, the HDLISSA-MGDC model designs a feature extractor with the help of Dilated Residual Network (DRN) and Adam optimizer. For grape disease classification, the Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model is employed in this study. Finally, the ISSA is exploited to adjust the hyperparameter values of the CNN-GRU method. The proposed HDLISSA-MGDC method was simulated using the plant leaf disease dataset. The simulation results show the significant performance of the proposed HDLISSA-MGDC model. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new A new Internet of Nano Health Things (IoNHT) was proposed and designed for Nano-body Sensor Network (NBSN) considering signal journey from body to cloud. • A Best Path Selection in IoNHT (BPS-IoNHT) is proposed using single or two nano-master nodes. • A new relay selection criteria and path selection are proposed based on number of nano-master node and nano-sensors node and the it is mathematically formulated. • Outage probability has been analysed and formulated of the A Best Path Selection in IoNHT (BPS-IoNHT). • A new metric has been analysed and formulated, bandwidth efficiency of the proposed A Best Path Selection in IoNHT (BPS-IoNHT) • The outage probability has been reduced by 29%, and bandwidth efficiency improved by 35% compared to recent work. Terahertz communication inside the human body uses minimal power; therefore, the destination may not receive the signal properly due to losses caused by molecular absorption and path loss. In this paper, we developed a new cooperative communication for the Internet of Nano Health Things (IoNHT), named the Best Path Selection in IoNHT (BPS-IoNHT). In addition, an innovative communication scenario for Nano Body Sensors Networks (NBSN) has been proposed. The proposed protocol has been thoroughly analysed and mathematically formulated regarding path loss, outage probability and bandwidth efficiency. The objective of the work is to reduce the outage probability and increase bandwidth efficiency. The results show that the proposed protocol performs better than recently published work. The outage probability has been reduced by 29%, and bandwidth efficiency improved by 35% compared to recent work. [Display omitted] [ABSTRACT FROM AUTHOR]

The Internet of Things is a well-known and emerging technology that allows authorised users to monitor and access sensors deployed in various industrial units. A robust key management protocol for industrial sensor network system is used to provide for such access and monitoring. The difficulty emerges when it comes to establishing a secure mutual authentication framework that allows users to safely communicate with sensors installed in Industrial Internet of Things (IIoT) systems. In the literature, only a few fundamental agreement protocols have been shown to be effective. Such protocols, however, have higher processing and communication costs. So, the objective would be to develop an effective mutual authentication approach that outperforms existing protocols. Therefore, we propose a secure key management framework that is both computationally and communicatively efficient. Additionally, the proposed mutual authentication framework allows users to securely communicate with sensors. The objective of this research is to provide a robust, secure, and anonymity preserving three-factor mutual authentication framework for the IIoT in order to eliminate any security vulnerabilities. We used widely known random oracle models and Scyther tool stimulation's to perform both informal and formal security analysis on the proposed scheme, ensuring that it is secure against all known security risks. The performance study demonstrates that the suggested framework is more efficient and lightweight than other existing frameworks. In addition, in a resource-constrained IIoT context, the suggested framework is relatively easy to implement. [ABSTRACT FROM AUTHOR]

Because of increased applications of Internet of Things (IoT) environment in real‐time environment, confidential data gathered by the IoT devices are being communicated to the cloud environment to train the machine learning (ML) models in understanding the patterns that exist in the data. At the same time, the sensitive nature of the IoT data attracts malicious users into hacking efforts. An intrusion detection system (IDS) can be applied to ensure security in the IoT environment. In order to improve security, the ML models can be executed at the data source instead of centralized cloud server. Federated learning (FL) is a recent progression of ML model which enables the ML models to move into the data source rather than moving the data to centralized cloud and thereby resolves cybersecurity problems in the IoT environment. In this view, this study introduces an FL based IDS using bird swarm algorithm based feature selection with classification (FLIDS‐BSAFSC) model in IoT environment. The presented FLIDS‐BSAFSC model undergoes training on multiple aspects of IoT dataset in a decentralized format to classify, detect, and defend against attacks. The proposed FLIDS‐BSAFSC model initially applies min–max normalization technique to pre‐process the IoT data. Besides, BSA based feature selection (BSA‐FS) technique is designed to elect feature subsets. Finally, social group optimization algorithm with kernel extreme learning machine model is employed for identifying various kinds of classes. In the view of FL where the IoT dataset is not distributed to the server carries out profile aggregation competently with the advantage of peer learning. The experimental validation of the FLIDS‐BSAFSC model is tested using benchmark datasets and the results are inspected under several aspects. The experimental values highlighted the better performance of the FLIDS‐BSAFSC model over recent approaches. [ABSTRACT FROM AUTHOR]

Electronic health records are essential and sensitive since they include vital information and are routinely exchanged across several parties, such as hospitals and private clinics. These data must remain accurate, current, secret, and available only to authorized parties. Integrating these data improves the accuracy and cost-effectiveness of the present health data administration framework. Electronic Medical Records (EMRs) are now kept utilizing the structure of the client/server via whom patient data information is maintained in the hospital. Multiple hospitals use the same database to track a single patient. These limitations prevent a custom health system from providing various associated experts and patients with a cohesive, integrated, secure, and confidential medical history. Modern healthcare systems are distinguished by their complexity and expense. However, this may be mitigated by enhanced health record management and Blockchain technology. The Blockchain's data availability, confidence, and security characteristics have a bright future in healthcare services, giving solutions to the issues of the traditional customer/server architecture EMR management platform: intricacy, confidence, dependability, compatibility, and anonymity. An e-health record management based on Internet of Medical Things (EHRM-IoMT) is proposed in this paper. This paper explores and analyzes Blockchain efficiency and customer/server paradigms. The findings show that a patient-centred strategy may achieve remarkable success utilizing Blockchain. Moreover, the immutable and accurate data of persons in Blockchain may enable healthcare practitioners to better forecast and aid with diagnosis utilizing the IoMT via machine learning and artificial intelligence. [ABSTRACT FROM AUTHOR]

Smart grid integrates the physical power system infrastructure with internet-of-things-based digital communication networks that work together for grid stability, sustainability, and reliability. A significant number of smart devices converge in cyber-physical systems to make the smart grid more competitive and efficient in addressing the energy challenges and vulnerabilities in power system confidentiality, integrity, and availability in smart grid cyber-physical security systems. False data injection attacks are the most malicious threats in the smart grid paradigm and have been widely applied recently. Last few years, several detection algorithms for identifying the false data injection attack have been developed. Addressing these issues, this paper reports a false data injection attack and threat mathematical model, impacting the on-grid system, economy, and society. The classification of false data injection attack detection algorithms and mathematical models are mainly presented. Finally, issues and challenges are identified from existing research and recommended for future research direction. [ABSTRACT FROM AUTHOR]

• Device identification using several machine learning algorithms such as LR, KNN, SVC, DT, RF, GB, AdaBoost, LGBM, XGB, CNN, and LSTM. • Performance evaluation of models using multiple statistical parameters such as accuracy, precision, recall, F1 scores, etc. • Blockchain-based architecture for the smart home environment incorporating blockchain-based structure and smart contracts. • Blockchain-based architecture for two case studies, i.e., data marketplace and access control mechanism. Due to data collection, there is a potential risk concerning security and privacy, so IoT reliability and survivability are of utmost concern. In this paper, we address the concern using two methods. The first method is device identification, which uses an extensive set of machine learning algorithms for identifying IoT devices. The algorithms include Logistic Regression, K- Nearest Neighbour, Support Vector Classifier, Random Forest, Gradient Boosting, AdaBoost, Light Gradient Boosting Machine, Extreme Gradient Boosting Convolution Neural Networks, and Long Short Term Memory are used for device identification. The performance of these models has been evaluated using multiple statistical measures, and weobserve that LSTM outperforms all other baseline models. The second method proposed for ensuring the survivability of theIoT environment is a blockchain-based architecture for smart homes to ensuretransparency and data protection. [Display omitted] [ABSTRACT FROM AUTHOR]

The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs. [ABSTRACT FROM AUTHOR]

The advancement of healthcare technology is impossible without machine learning (ML). There have been numerous advances in ML to analyze, predict, and diagnose medical data. Integrating a centralized scheme and therapy for classifying and diagnosing illnesses and disorders is a major obstacle in modern healthcare. To standardize all medical data into a single repository, researchers have proposed using ML using the centralized artificial neural network model (ML-CANNM). Random tree, support vector machine, and gradient booster are just a few proposed ML classifiers. Artificial neural networks (ANNs) have been trained using a variety of medical datasets to predict and analyze outcomes. ML-CANNM collects patient data from various studies and uses ML and ANNs to determine the results. Three layers make up an ANN. ML is used to classify the given patients' data in the input layer. In the hidden layer, classification data are compared to a training dataset. The output layer's job is to identify, classify, and diagnose diseases. As a result, disease diagnosis and detection are integrated into a single healthcare database. The proposed framework has proven that ML-CANNM works with more accuracy and lesser execution time. Thus, the numerical outcome suggested ML-CANNM increased accuracy ratio of 99.2% and a prediction ratio of 97.5%. The findings further show that the execution time is enhanced by less than 2 h, decision table using ML and results in an efficiency ratio of 97.5%. [ABSTRACT FROM AUTHOR]

Simple Summary: The reported global incidences of skin cancer led to the development of automated clinical aids for making proper clinical decision models. Correctly classifying the skin lesions during the early stage may increase the chances of being cured before cancer. However, the skin lesion dataset images pose many critical challenges related to available features to develop classification models with cross-domain adaptability and robustness. This paper made an attempt to select important features from skin lesion datasets for proper skin cancer classification by proposing some feature fusion strategies. Three pre-trained models were utilized to select the important features and then an adaptive weighted mechanism of choosing important features was explored to propose model-based and feature-based optimized feature fusion strategies by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish algorithm. The empirical evidence shows that choosing the weights of the pre-trained networks adaptively in an optimized way gives a good starting point for initialization to mitigate the chances of exploding or vanishing gradients. This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS. [ABSTRACT FROM AUTHOR]

The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET's effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems. [ABSTRACT FROM AUTHOR]

Brain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments • Develop an accurate MI classification model using EEG data. • Propose a hybrid optimization strategy combining WSO and ChOA. • Implement a two-tier deep learning strategy for classifying motor imagery. [ABSTRACT FROM AUTHOR]

Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Since the outbreak of the COVID-19 epidemic, several control strategies have been proposed. The rapid spread of COVID-19 globally, allied with the fact that COVID-19 is a serious threat to people's health and life, motivated many researchers around the world to investigate new methods and techniques to control its spread and offer treatment. Currently, the most effective approach to containing SARS-CoV-2 (COVID-19) and minimizing its impact on education and the economy remains a vaccination control strategy, however. In this paper, a modified version of the susceptible, exposed, infectious, and recovered (SEIR) model using vaccination control with a novel construct of active disturbance rejection control (ADRC) is thus used to generate a proper vaccination control scheme by rejecting those disturbances that might possibly affect the system. For the COVID-19 system, which has a unit relative degree, a new structure for the ADRC has been introduced by embedding the tracking differentiator (TD) in the control unit to obtain an error signal and its derivative. Two further novel nonlinear controllers, the nonlinear PID and a super twisting sliding mode (STC-SM) were also used with the TD to develop a new version of the nonlinear state error feedback (NLSEF), while a new nonlinear extended state observer (NLESO) was introduced to estimate the system state and total disturbance. The final simulation results show that the proposed methods achieve excellent performance compared to conventional active disturbance rejection controls. [ABSTRACT FROM AUTHOR]

• Thorough examination of the dehazing methods used to assess image quality. • Several standard evaluation metrics are implemented. • A list of standard haze image datasets is also included. Images captured in hazy environments need to be processed to increase their contrast and colour integrity. Dehazing, sometimes referred to as haze removal is an important pre-processing step for image processing and computer vision applications. Numerous methods for dehazing images have been proposed in the literature. This study provides a complete evaluation of numerous image dehazing techniques and their notable standards. This study extracted and presented an important direction of the many algorithms to handle the challenges of dehazing such as model-based methods, transform domain methods, variational-based algorithms, learning-based algorithm and transformer-based algorithms. This study attempts to compile and evaluate the most important studies in the domain of image dehazing. A variety of factors have been considered necessary to provide a detailed information in this study. These factors include datasets that utilised in the literature, challenges faced by the prior researchers, motivations, and recommendations for reducing the drawbacks in the available literature. The systematic rules are utilized for searching all relevant papers on image dehazing using several keyword diversities along with a glance for assessment and the benchmark studies. Image dehazing, which generally eliminates undesirable pictographic effects is often considered as an image enhancement method. A completely automated process, a valid assessment strategy, and databases based on diverse settings are needed for it to operate under real-time applications. Many relevant studies are conducted in order to achieve these substantial goals. We examined numerous image dehazing methods and assessed the objectivity of the results. The results of our study precisely reflect numerous observations on image dehazing regions in contrast to other review articles. We believe that the findings of the study can be a helpful set of recommendations for professionals looking for a full understanding of image dehazing. In addition, we present a thorough examination of the methods used to evaluate image quality using the full-reference category and the no-reference category. Several standard evaluation metrics are utilised to compare the results of the well-known dehazing techniques. A list of standard haze image datasets is also included in this study so that different dehazing methods can be compared. In our opinion, the findings of this study could act as a valuable guide for experts in quest of a detailed understanding of image dehazing. [ABSTRACT FROM AUTHOR]

The research paper mainly deals with waste heat recovery from internal combustion engines (ICE) using the organic Rankine cycle (ORC) and Thermoelectric generator (TEG). Simultaneously recovering the wasted heat of both exhaust gases and coolant, a novel configuration named two-stage is proposed. Then a comprehensive thermo-economic analysis and optimization are conducted. Produced power and total cost rate are selected as the objective function of the optimization. Also, the first and second stage pressures of the ORC system are considered as decision variables. Finally, a sensitivity analysis is performed to study the effect of expander inlet temperature, pumps isentropic efficiency, and expander isentropic efficiency on the objective function. [Display omitted] • Hybrid power generation system is proposed based on internal combustion engine, organic Rankine cycle, and thermoelectric generator. • Thermoelectric generator is used instead the condenser. • Energy, exergy, and economic analysis are performed. • Multiobjective optimization is conducted and the optimum operating condition is specified. [ABSTRACT FROM AUTHOR]

This research shows unethical user prediction on the peer-to-peer content delivery network against the Over-The-Top media service (OTT). The Web User Behaviour Analysis and unethical user prediction are performed based on the OTT multimedia service platform. The Analysis and unethical user prediction contain the four stages of processing. Firstly "Peer to Peer Content Delivery Network" is used to provide services and ideal resources (User). It uses ResourceCache: an optimized algorithm for giving efficient CDN-based over-the-top video streaming services. Secondly, “Selective destination Bitrate-adaptivity” is employed to provide the OTT Service (Devices). It utilizes bitrate adaptivity to improve the adaptivity of Over-The-Top Television systems. Thirdly, the “Network Functions Virtualization (NFV)” enabled multi-access edge data centers to embed to maintain sufficient data about users and their respective devices. It uses the QoE-based Load Balancing of OTT video content in CDN networks to improve the QoE of OTT multimedia services in wireless scenarios. Finally, the efficient “Review of Recommender System” are taken to collect feedback about services and subscription. It is most useful to check unethical user access. The performance result of the proposed system conceives the analysis based on web users' behavior prediction accuracy of 76%, Average precision of 67.8%, Average recall of 74.2%, and Average f1-measures of 85.9% against the performance of unethical user prediction. [ABSTRACT FROM AUTHOR]

Given that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants which can occasionally cause crop losses ranging from 30 to 60%. This manuscript proposed a Convolutional Neural Network (CNN) and Visual Geometry Group (VGG)19 i.e. CNN-VGG19 model with a transfer learning-based method for the precise identification and classification of rice leaf diseases. This scheme employs a transfer learning technique based on the VGG19 which can identify the brown spot class. The accuracy is 93.0% in the deployment of the dataset of rice leaf disease. The other parameters are sensitivity, specificity, precision and F1-score with 89.9%, 94.7%, 92.4% and 90.5% respectively. The developed technique obtained better results as compared to the existing baseline models. Proposed CNN-VGG19 model for detection of rice leaf disease. [Display omitted] • The CNN-VGG19 model is developed for the recognition of brown spot rice leaf diseases. • The CNN-VGG19 is based on transfer learning for the precise identification of the brown spot leaf disease. • The highest accuracy, precision, and sensitivity in deploying the developed model are 93.0%, 92.4%, 89.9% respectively. [ABSTRACT FROM AUTHOR]

Cloud service providers are aggressively expanding their data centers to meet end-user demand, resulting in increased power and energy consumption. As a result, it becomes extremely important to implement server consolidation solutions in the context of cloud computing. One of the difficult problems in cloud data centers is efficient server consolidation, which is related to both environmental and financial aspects. Using resources efficiently and reducing energy consumption are the main goals of consolidating servers for cloud computing. Regrettably, the majority of methods now in use have been geared towards either lowering system performance or reducing energy consumption. Using the Markov chain principle for each server transition, we suggested a strategy in this study to optimize the server consolidation process. An early review of the study's findings is encouraging and comes to the conclusion that a successful server consolidation strategy may be implemented with the least amount of resources and the best possible energy use. The suggested strategy takes into account the following three elements: consolidating the resources, cutting down energy consumption, and trading off of both (composite case). [Display omitted] • Presented an optimized server consolidation approach in Cloud environment. • Shows how resource usage and energy consumption can be optimised together. • Employs Markov chain-based scenarios for cloud server consolidation. • Lowers the cost of VM migration by reducing the number of migration counts. • Shows how QoS has improved while categorising servers based on resource demand. [ABSTRACT FROM AUTHOR]

The Internet of Drones (IoD) allows for coordinated control of airspace for Unmanned Aerial Vehicles (UAVs), also known as drones. The decreasing costs of processors, sensors, and wireless connectivity have made it possible to use UAVs in many variety of military to civilian applications. While most applications utilizing the drones in the IoD have been real-time related, users are now interested in obtaining real-time services from drones that are tailored to a specific fly zone. This study develops a Sea Turtle Foraging Algorithm with Hybrid Deep Learning-based Intrusion Detection (STFA-HDLID) as a algorithm that recognizes and categorizes intrusions in the IoD environment. For this purpose, it is necessary to implement data pre-processing to standardize the input data via min-max normalization. Additionally, the feature selection process is also based on the STFA. Finally, a Deep Belief Network (DBN) with a Sparrow Search Optimization (SSO) algorithm is used for classification. A comprehensive experimental analysis is performed on a benchmark dataset to demonstrate the performance of the STFA-HDLID, which achieves maximum accuracy of 99.51% and 98.85% on the TON_IoT and UNSW-NB15 datasets, respectively, outperforming other techniques. [ABSTRACT FROM AUTHOR]

The massive population growth and rising environmental issues raise several challenges in the agriculture sector, such as agricultural land scarcity, overuse of pesticides, and global food demand. To meet global food demand, farmer uses large quantities of pesticides to enhance crop quality and quantity. However, consuming food from pesticide crops reduces human life expectancy. To overcome the aforementioned issue and improve human life expectancy, we proposed a blockchain and artificial intelligence (AI)-empowered smart agriculture framework to predict pesticide crop's beyond the threshold. The blockchain is integrated to confront the data manipulation attack, where the crop that uses minimum pesticides is securely stored inside the blockchain's immutable ledger. Finally, the proposed framework is evaluated with performance metrics, such as accuracy, blockchain scalability, and latency. The result shows that the proposed framework outperforms in terms of accuracy, scalability, and latency compared to the baseline approaches. [Display omitted] • This article proposed a reliable framework that predicts the human life expectancy. • Public blockchain alleviates data manipulation attacks to enhance security. • Proposed framework enhances the latency and packet drop ratio using a 6G network. [ABSTRACT FROM AUTHOR]

Aggressive, unexpected, and catastrophic changes in the environment-induced or impacted by the cultivation of land, crops, and cattle are known as agricultural disasters. In agriculture, the volume of data unpredictability, processing, and data management standards for interoperability are significant concerns. While natural catastrophes are still a considerable problem, the enormous amount of data available has opened up new avenues for coping. Accordingly, big data analytics has profoundly changed the way people respond to disasters in the agriculture sector. In this paper, the Data handling model using big data analytics (DHM-BDA) explores the role of big data in managing agricultural disasters and highlights the technical status of delivering practical and efficient disaster management solutions. DHM-BDA is used to address the essential sources of big data that include climatic causes and associated successes and developing technological problems in different disaster management phases. In addition, it aids in the monitoring, mitigation, alleviation, and acceptance of agricultural catastrophes and the process of recovery and rebuilding. The simulation findings have been executed, and the suggested model enhances the prediction ratio of 98.9%, decision-making level of 97.8%, data management of 96.5%, production ratio of 95.6%, and risk reduction ratio of 97.1% compared to other existing approaches. • Aggressive, unexpected, and catastrophic changes in the environment-induced. • Data unpredictability, processing, and data management standards for interoperability. • Data handling model using big data analytics (DHM-BDA)explores. • The simulation findings have been executed, and the suggested model enhances. [ABSTRACT FROM AUTHOR]

Coal is a key fuel source and an important resource for a wide range of businesses. The hazardous and potentially poisonous nature of the work also has to be taken into consideration. High temperatures, humidity, and the discharge of hazardous gases are just a few of the challenges coal miners confront on a daily basis. This produces a risky work environment that places employees at risk of serious injury or death. In this paper, IoT based Dynamic Sensor Information Control System (IoT-DSICS) has been proposed to solve warm humidity, precipitation, and unhealthy carbon emissions of the coal mine. Using sensor networks and control systems that have been deployed in many sectors, the Industrial Internet of Things (IIoT) is combined in this article. The present security examination of information management has been evaluated since the national coal mining output remains serious and significant accidents are successfully limited. Wi-Fi microcontroller system IIoT is used to monitor and operate the prototypes, activate fans in the Pittsburgh Investigation of Mine, and trigger a surface alert to track the low cost of opening alternative coal. The findings of this feasibility study existing communication and tracking infrastructure are leveraged to examine the potential of IIoT in underground coal mines. • This produces a risky work environment that places employees at risk. • IoT based Dynamic Sensor Information Control System (IoT-DSICS). • The present security examination of information management. • Wi-Fi microcontroller system IIoT is used to monitor. [ABSTRACT FROM AUTHOR]

This paper proffers a secure and anonymous authenticated key exchange (AKE) scheme for SGs, called SAAS-SG, for establishing a secure communication channel between smart meter (SM) and service provider (SPR). SAAS-SG utilizes the hash algorithm, Esch256, and authenticated encryption algorithm AEGIS, to perform the AKE process. Besides, SAAS-SG ensures the integrity and confidentiality of AKE messages while preserving the anonymity of SMs and SPR. Besides, for encrypted transmission in the future, SAAS-SG empowers SMs and SPR to set up an analogous secret session key (SK) after performing the mutual authentication. SK encrypts the sensitive information exchanged between SMs and service providers (SPR) over the public Internet. Moreover, we illustrate that SAAS-SG is capable of resisting different security vulnerabilities by conducting informal and Scyther-based security analyses. In addition, the random oracle model is operated to validate the security of the established secret SK. Furthermore, comparing SAAS-SG with other related AKE schemes explicates that SAAS-SG requires low communication, storage, and computational overheads, respectively, while accomplishing the AKE phase and renders enhanced security features. [ABSTRACT FROM AUTHOR]

• Develop an IoT assisted optimal deep learning-driven weed detection model. • Employ hybrid leader optimizer with YOLO-v5 model for detection process. • Present KELM model for classification of weeds and crops. • Validate the performance on benchmark dataset and achieves 98.87% accuracy. Recent technological advancements of Cloud Computing (CC), Internet of Things (IoT), Artificial Intelligence (AI), computer vision, etc. enable the transformation of traditional agricultural practices into smart agricultural practices. In this background, the current article introduces a novel Hybrid Leader-based Optimization with DL-driven Weed Detection in IoT-enabled Smart Agriculture (HLBODL-WDSA) model. The prime aim of the proposed HLBODL-WDSA model is to collect the images using IoT devices and recognize the weeds automatically. Initially, the HLBODL-WDSA model enables the IoT devices to capture the farm images and transmits the images to the cloud server for examination. Next, the HLBODL-WDSA model applies YOLO-v5-based weed detection process in which HLBO algorithm is exploited as a hyperparameter optimizer. Finally, the Kernel Extreme Learning Machine (KELM) model is applied for effective classification of the weeds. The proposed HLBODL-WDSA model was experimentally validated and the outcomes established the better performance of the proposed HLBODL-WDSA model over recent approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

• Develop an AI enabled Coconut Tree Disease Detection model. • Present AIE-CTDDC model with Bayesian fuzzy clustering-based segmentation. • Apply Harris Hawks Optimizer with gated recurrent unit for classification. • Achieves higher accuracy of 97.75% on coconut tree disease classification. Real-time and accurate plant disease recognition systems help in the development of disease mitigation strategies and ensure food security on a large scale compounded with small-scale economic crop protection. The current research article presents an Artificial Intelligence Enabled Coconut Tree Disease Detection and Classification (AIE-CTDDC) model for smart agriculture. The aim of the presented AIE-CTDDC technique is to classify the coconut tree diseases in a smart farming environment so as to enhance the crop productivity. Firstly, the AIE-CTDDC model applies median filtering-based noise removal technique. Then, the Bayesian fuzzy clustering-based segmentation method is employed for the detection of the affected leaf regions. Besides, the capsule network (CapsNet) method is exploited as a feature extractor. In this study, the Harris Hawks Optimization (HHO) with Gated Recurrent Unit (GRU) model is exploited for the detection of diseases in coconut trees. The experimental analysis was conducted upon AIE-CTDDC model and the outcomes confirmed the better performance of the proposed AIE-CTDDC model over recent state-of-the-art techniques. [Display omitted] [ABSTRACT FROM AUTHOR]

• Develop a secure communication system for internet of drones environment. • Derive OPRFS-ODFNN model for intrusion detection in IoD environment. • Present an oppositional poor and rich optimization based feature selection. • Apply optimal deep feed-forward neural network for intrusion detection. As a result of technological advancements and the need for continuous reduction in manufacturing costs, the concept of Internet of Things (IoT), consisting of Unmanned Aerial Vehicles (UAVs), has entered the industrial production units. IoT devices have not only penetrated the day-to-day activities of human beings, but also in defence. In recent years, there is a widespread application of Internet of Drones (IoD) in areas such as television and film shooting, meteorological monitoring, forest fire detection, agricultural monitoring, emergency rescue, etc. In this background, Intrusion Detection System (IDS) plays an important role to effectually secure the IoD network. The current research work develops an Opposition Poor and Rich Optimization-based Feature Selection with Optimal Deep Feed-forward Neural Network (OPRFS-ODFNN) model for intrusion detection in IoD communication system. The aim of the presented OPRFS-ODFNN technique is to accomplish enhanced security in IoD communication system. In order to achieve the objective, OPRFS-ODFNN model initially executes feature scaling as a pre-processing step. Then, OPRFS technique is applied for effective selection of the features. Moreover, Improved Mayfly Optimization (IMFO) is applied with ODFNN model for intrusion detection and classification processes. In order to validate the enhanced performance of the proposed OPRFS-ODFNN method, extensive simulations were conducted. [Display omitted] [ABSTRACT FROM AUTHOR]

Facial expression is commonly utilized by humans to deliver their mood and emotional state to other people. Facial expression recognition (FER) becomes a hot research area in recent days, and it is a tedious process owing to the presence of high intra-class variation. The conventional methods for FEC are mainly based on handcrafted features with a classification model trained on image or video datasets. Since the facial datasets involve large variations in the images and comprise partial faces, it is needed to design automated FER models. The latest advancements in artificial intelligence (AI) and deep learning (DL) models find useful for better understanding of facial emotions related to face images. In this aspect, this paper presents an intelligent FER using optimal deep transfer learning (IFER-DTFL) model. The proposed IFER-DTFL technique aims to detect the face and identify the facial expressions automatically. The IFER-DTFL technique encompasses a three state process: face detection, feature extraction, and expression classification. In addition, a mask RCNN model is used for the detection of faces. Moreover, the Adam optimizer with Densely Connected Networks (DenseNet121) model is employed for feature extraction process. Furthermore, the weighted kernel extreme learning machine (WKELM) model is utilized to classify the facial expressions. A comprehensive set of simulations were carried out on benchmark dataset and the results are inspected under varying aspects. The experimental results pointed out the supremacy of the IFER-DTFL technique over the other recent techniques interms of several performance measures. • Propose a deep learning based facial expression recognition technique. • Apply Mask RCNN model for face detection process. • Employ Adam optimizer with DenseNet and WKELM model for recognition. [ABSTRACT FROM AUTHOR]

The utilization of 3D display systems offers significant advantages to end-users. Sequencing technologies are essential for accessing an autonomous, auto-balanced, non-glass 3D world with greatly improved visual acuity. To have both social and emotional depth cues on a 3D display is difficult. Because of the theoretically constant feature vectors provided, certain 3D overall volume display approaches cannot produce shadows or structures. Hence, in this paper, a Wireless Sensor Assisted Image Correlation framework (WSAICF) has been proposed to enhance the current competitiveness, performance experience, and better visual understanding of the 3d display systems. In 3D presentations, the Steep Learning algorithm is used to enhance social depth estimations rapidly. The WSAICF method is integrated with the Deep Learning algorithm to enhance the emotional depth indicators of 3D display systems. The simulation result shows that an image correlation system achieves 97 % reliability, 99 % performance, 96 % viability, 11 % resilience, and 95 % efficiency. [ABSTRACT FROM AUTHOR]

• Present an optimal SSDAE to classify cervical cancer on pap smear images. • Employ Kapur's entropy segmentation and efficientnet feature extraction. • Propose modified firefly optimization algorithm for hyperparameter tuning. • Validate the performance of proposed model on Herlev database. Cervical cancer is the most commonly diagnosed cancer among women globally, with high mortality rate. For early diagnosis, automated and accurate cervical cancer classification approaches can be developed through effective classification of Pap smear cell images. The current study introduces a novel Modified Firefly Optimization Algorithm with Deep Learning-enabled cervical cancer classification (MFFOA-DL3) model for the classification of Pap Smear Images (PSI). The proposed MFFOA-DL3 model examines the PSI for the existence of cervical cancer cells. To accomplish this, the proposed MFFOA-DL3 model primarily applies Bilateral Filtering (BF)-based noise removal approach to get rid of the noise. Then, Kapur's entropy-based image segmentation technique is applied to determine the affected regions. Moreover, EfficientNet technique is also applied to generate the feature vectors. Finally, MFFOA with Stacked Sparse Denoising Autoencoder (SSDA) model is exploited to classify the PSI. In current study, MFFOA is utilized to appropriately modify the parameters related to SSDA model. The proposed MFFOA-DL3 model was experimentally validated using benchmark dataset. The results attained from extensive comparative analysis highlighted the better performance of MFFOA-DL3 model over other recent approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

The adaptive Joint Photographic Experts Group 2000 (JPEG2000) image compression approach employing the wavelet image transform had proposed with the rise of the optimized Video Internet of Things (VIoT) using image transmission security using Elliptic Curve Cryptography (ECC) techniques. Compressive Sensing (CS) for periodic data transfers has shown to be an excellent option for Wireless Sensor Networks (WSNs) since CS-based sensor communications drastically reduce data transmissions and enhance energy efficiency. However, another issue that arises when utilizing the optimized VIoT with image transmission compression is data loss as a result of various security risks during transmission. The numerous cooperative communication strategies proved their feasibility in different ways. However, additional issues must be handled while handling image transmission in VIoT. For example, when preparing to transmit an image, you may expend a lot of energy. The main objective of this paper is to increase image quality while minimizing processing time and error rates. However, force aptitude is vital to the research problem for the Wireless Multimedia Sensor Network (WMSN), as high-dimensional digital images use greater processing capabilities of sensor nodes. The image in WMSN is transmitted through a large number of relays. The experimental findings demonstrate the effectiveness of the suggested paradigm. [ABSTRACT FROM AUTHOR]

The rainfall-runoff process is one of the most complex hydrological phenomena. Estimating runoff in the basin is one of the main conditions for planning and optimal use of rainfall. Using machine learning models in various sciences to investigate phenomena for which statistical information is available is a helpful tool. This study investigates and compares the abilities of HEC-HMS and TOPMODEL as white box models and adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) as black box models in rainfall-runoff simulation using 5-year statistical data. Using the inputs of rainfall and temperature of the previous day and discharge in the steps of the previous 2 days reduced the prediction error of both models. Examining the role of different parameters in improving the accuracy of simulations showed that the temperature as an effective parameter in cold months reduces the amount of prediction error. A comparison of R 2 , RMSE, and MBE showed that black box models are more effective forecasting tools. Among the black box models, the ANFIS model with R 2 = 0.82 has performed better than the GEP model with R 2 = 0.76. For white box models, the HEC-HMS and TOPMODEL had R 2 equal to 0.3 and 0.25, respectively.

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective. Healthcare 4.0 mainly consisted of periodic medical data sensing, aggregation, data transmission, data sharing, and data storage. The sensitive and personal data of patients lead to several challenges while protecting it from hackers. Therefore storing, accessing, and sharing the patient medical information on the cloud needs security attention that data should not be compromised by the authorized user's components of E-healthcare systems. To achieve secure medical data storage, sharing, and accessing in cloud service provider, several cryptography algorithms are designed so far. However, such conventional solutions failed to achieve the trade-off between the requirements of EHR security solutions such as computational efficiency, service side verification, user side verifications, without the trusted third party, and strong security. Blockchain-based security solutions gained significant attention in the recent past due to the ability to provide strong security for data storage and sharing with the minimum computation efforts. The blockchain made focused on bitcoin technology among the researchers. Utilizing the blockchain which secure healthcare records management has been of recent interest. This paper presents the systematic study of modern blockchain-based solutions for securing medical data with or without cloud computing. We implement and evaluate the different methods using blockchain in this paper. According to the research studies, the research gaps, challenges, and future roadmap are the outcomes of this paper that boost emerging Healthcare 4.0 technology., Competing Interests: Conflict of interestAll authors declares that they have no conflict of interest., (© King Abdulaziz City for Science and Technology 2022.)

The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs., Competing Interests: Conflict of interestThere are no conflicts of interest., (© King Abdulaziz City for Science and Technology 2021.)

It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as support vector machine (SVM), gene expression programming (GEP), wavelet-SVM (WSVM), and wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River Basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with wavelet improved simulation accuracy, which is now the highest for the WGEP method.

Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (© 2022 Alias et al.)

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%., Competing Interests: The authors declare there are no competing interests., (©2022 Abdalla et al.)