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Nowadays, vehicular ad hoc networks (VANET) turn out to be a core portion of intelligent transportation systems (ITSs), that mainly focus on achieving continual Internet connectivity amongst vehicles on the road. The VANET was utilized to enhance driving safety and build an ITS in modern cities. Driving safety is a main portion of VANET, the privacy and security of these messages should be protected. In this aspect, this article presents a blockchain with sunflower optimization enabled route planning scheme (BCSFO-RPS) for secure VANET. The presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is secure. In addition, the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of routes. Besides, the proposed BCSFO-RPS model derives an intrusion detection system (IDS) encompassing two processes namely feature selection and classification. To detect intrusions, correlation based feature selection (CFS) and kernel extreme machine learning (KELM) classifier is applied. The performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem inwireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises amassive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique forWSN. The goal of theQBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques. [ABSTRACT FROM AUTHOR]

Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms (EASCPSMA). The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection. The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm (IFFOA) based clustering and optimal deep stacked autoencoder (OSAE) based intrusion detection. Besides, the optimal selection of stacked autoencoder (SAE) parameters takes place using root mean square propagation (RMSProp) model. The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects. The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures. [ABSTRACT FROM AUTHOR]

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%. [ABSTRACT FROM AUTHOR]

The unstructured growth of abnormal cells in the lung tissue creates tumor. The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment. Various medical image modalities can help the physicians in the diagnosis of disease. Many research works have been proposed for the early detection of lung tumor. High computation time and misidentification of tumor are the prevailing issues. In order to overcome these issues, this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling (ASPP)-Unet architecture withWhale Optimization Algorithm (ASPP-Unet -WOA). To get a fine tuning detection of tumor in the Computed Tomography (CT) of lung image, this model needs pre-processing using Gabor filter. Secondly, feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization. Thirdly, feature selection is done using Binary Grasshopper Optimization Algorithm. This proposed (ASPPUnet -WOA) is implemented in the dataset of National Cancer Institute (NCI) Lung Cancer Database Consortium. Various performance metric measures are evaluated and compared to the existing classifiers. The accuracy of Deep Convolutional Neural Network (DCNN) is 93.45%, Convolutional Neural Network (CNN) is 91.67%, UNet obtains 95.75% and ASPP-UNet-WOA obtains 98.68%. compared to the other techniques. [ABSTRACT FROM AUTHOR]

Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type II fuzzy logic system (T2FLS) energy management scheme to accomplish the desired engine torque based on distinct parameters. In addition, the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer (BMO). For accurate TFP, the bidirectional gated recurrent neural network (Bi-GRNN) model is used in AVs. A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation metrics. The experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches. [ABSTRACT FROM AUTHOR]

Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems. The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals. In addition, the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension (SFD) approach. Moreover, the modulation signal classification process takes place using BiLSTM with fully convolutional network (BiLSTM-FCN). Furthermore, the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm. In order to ensure the enhanced classification performance of the COSBO-BiLSTM model, a wide range of simulations were carried out. The experimental results highlighted that the COSBO-BiLSTMtechnique has accomplished improved performance over the existing techniques. [ABSTRACT FROM AUTHOR]

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer (QOBMO) based feature selection technique. Moreover, the deep stacked autoencoder (DSAE) based classification model is designed for the detection and classification of atherosclerosis disease. Furthermore, the krill herd algorithm (KHA) based parameter tuning technique is applied to properly adjust the parameter values. In order to showcase the enhanced classification performance of theMDL-BADDCtechnique, a wide range of simulations take place on three benchmarks biomedical datasets. The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods. [ABSTRACT FROM AUTHOR]

Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked SparseAutoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features. Moreover, the swallow swarm optimization (SSO) with the SSAE model is derived to perform the classification process. To showcase the enhanced outcomes of the ISOFSOSSAE model, a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine (UCI) Machine Learning Repository. The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures. [ABSTRACT FROM AUTHOR]

We are aware that autonomous vehicle handles camera and LiDAR data pipelines and uses the sensor pictures to provide an autonomous object identification solution. While current research yields reasonable results, it falls short of offering practical solutions. For example, lane markings and traffic signs may become obscured by accumulation on roads, making it unsafe for a self-driving car to navigate. Moreover, the car’s sensors may be severely hindered by intense rain, snow, fog, or dust storms, which could endanger human safety. So, this research introduced Multi-Sensor Fusion and Segmentation for Deep Q-Network (DQN)-based Multi-Object Tracking in Autonomous Vehicles. Improved Adaptive Extended Kalman Filter (IAEKF) for noise reduction, Normalized Gamma Transformation-based CLAHE (NGT-CLAHE) for contrast enhancement, and Improved Adaptive Weighted Mean Filter (IAWMF) for adaptive thresholding have been used. A novel multi-segmentation using several segmentation methods and degrees dependent on the orientation of images has been used. DenseNet (D Net)-based multi-image fusion provides faster processing speeds and increased efficiency. The grid map-based pathways and lanes are chosen using the Energy Valley Optimizer (EVO) technique. This method easily achieves flexibility, robustness, and scalability by simplifying the complex activities. Furthermore, the YOLOv7 model is used for classification and detection. Metrics like velocity, accuracy rate, success rate, success ratio, and mean-squared error are used to assess the proposed method. [ABSTRACT FROM AUTHOR]

Young children can enhance their movements and develop motor abilities through play and education with guidance and practice. Understanding young children’s physical development and identifying potential growth areas require an analysis of their motor skills. In this study, we assess young children’s motor skills using a unique deep residual technique on the cloud-to-thing continuum. Seven kids were used to identify autistic movements. The dataset was gathered from the Tamimi Centre for Autism in Saudi Arabia. The data preprocessing to standardize the data’s scale using min–max normalization and removing noise-relevant features is extracted using principal component analysis (PCA). This step is crucial for ensuring the quality and reliability of the data used for subsequent analysis. The bumble bees mating optimization with deep residual algorithm (BBMO-DRN) is designed to handle the complexities of motor skill assessment. Finally, we explore the potential of cloud–fog–edge computing in data storing and processing young children’s motor skill development. The results showed that the proposed method performs better compared to the existing methods. This will make it possible to evaluate how well the suggested solution works in terms of accuracy, precision, recall, F1-score, and scalability. As a result, this proposed approach in evaluating young children’s motor skills is to improve data storage in the cloud to the thing continuum. [ABSTRACT FROM AUTHOR]

The surge in applications necessitates a more intelligent and automated Higher Education Admission Management System (HEAMS). This research proposes a novel Deep Learning (DL)-based HEAMS utilizing a Cloud-Edge Architecture. The first step collects applicant data like transcripts, test scores, essays, and recommendations. Edge devices perform initial cleaning and preprocessing on these data to ensure quality and privacy. These preprocessed data using normalization and feature extraction using the Latent Dirichlet Allocation (LDA) are then transferred to the cloud where DL models, such as Convolutional Neural Networks (CNNs) for essays or Recurrent Neural Networks (RNNs) for transcripts, are trained. These models learn complex patterns from historical labeled data (admitted/not admitted) to predict an applicant’s success probability. During application evaluation, new data are fed through the trained models on the edge, generating probabilities for predefined classifications — high-potential, moderate, or low-potential. The cloud receives these probabilities and combines them with predefined admission criteria like minimum GPA. This combined analysis leads to a final classification using Novel Lite Convolutional Neural Network with Hybrid Leader-based Optimization (Lite CNN-HLO) for each applicant — admitted, waitlisted, or rejected and admission management system by refining admission decisions for admitted, waitlisted, and rejected applicants based on institutional priorities and constraints. The system not only generates classifications but also provides detailed model score breakdowns for transparency. This Cloud-Edge HEAMS offers improved efficiency, reduced workload for admissions staff, and potentially fairer decisions by mitigating bias through data-driven analysis. [ABSTRACT FROM AUTHOR]

One of the most advanced waveforms available in the beyond-fifth-generation radio (B5GR) framework is nonorthogonal multiple access (NOMA). The power amplifier (PA) of the NOMA structure performs less efficiently when the signal is strong and the peak-to-average power ratio (PAPR) is high. This study applies a hybrid algorithm to the NOMA structure, combining fractal partial transmit sequence (PTS), A-law companding, and the bacterial foraging algorithm (BFA). Fractals are known for their self-repeating structures at different scales, allowing for efficient coverage and exploration of space. Fractals enhance the bacteria foraging algorithm (BFA) by improving search efficiency, enabling better exploration of complex, multi-dimensional optimization landscapes. Similarly, BFA balances exploring the search in promising areas. We utilized BFA to achieve optimal phase factors for the PTS algorithm and applied A-Law companding to the NOMA symbols to further enhance the structure’s performance. The intermediate computational complexity in the Rician and Rayleigh channels improves PAPR, bit error rate (BER), and power spectral density performance. Simulation results reveal that the performance of the proposed hybrid method is superior to that of existing PAPR reduction algorithms and substantially enhances the efficiency of NOMA. [ABSTRACT FROM AUTHOR]