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Machine learning and deep neural network developments have evolved image classification. This study offers a new neural network design with segmentation and localization, different crack types, categorization accuracy, and efficiency. In the proposed work, an advanced and innovative Spliced Multimodal Residual18 Neural Network (SMR18-NN) Model is presented that departs from previous models. For data authentication, augmentation is done by proposing the new and effective model known as an Augmented Minority Over-sampling Technique (AMOST). The SMR18 NN model combines the well-established ResNet18 framework with the Faster RCNN architecture, incorporating parts from the modified Fast RCNN and the Region Proposal Network. This work aims to revolutionize crack-type recognition and categorization in images. A Support Vector Machine strategically improves the network’s data classification. A modified ResNet18 model is ultimately implemented and compared with the proposed innovative SMR18-NN model. Both network’s parameters, such as epochs, number of iterations, etc., were kept the same for fair evaluation. Innovative frameworks and properly selected benchmark datasets supported this. The empirical results of this comparison study are convincing. ResNet18 training and testing accuracy was 90.60% and 85.20%, respectively. The SMR18-NN outperformed these results with 96.20 training and 92.00% testing accuracy. The experiment concluded with SMR18-NN accurately detecting image features, proving its superiority in image classification.

The integration of unmanned aerial vehicles (UAVs) in the fifth-generation (5G) network has emerged as a promising solution to deliver seamless services and provide connectivity because of their mobile nature. In vehicular networks, connectivity between vehicles and the network can be affected by ground-level obstacles and false messages created by some deceitful vehicles. UAVs are integrated into vehicular networks to provide line-of-sight communication. This integration also poses specific challenges, such as maintaining reliable communication links and handling operational complexities related to coordination and routing, which minimizes the network throughput and packet delivery ratio (PDR) and increases energy consumption, routing overhead, and communication delay. This paper proposes improved trust analysis and artificial gorilla troops optimization (ITA-AGTO) to address the challenges associated with the security and deployment of UAVs in vehicular networks. ITA improves network security and reduces congestion by discarding incoming messages from a deceitful vehicle. It filters false messages by calculating the trust values corresponding to every message and determining whether to accept or discard the incoming message. The artificial gorilla troops optimization (AGTO) approach positions the UAVs and finds the optimal solution to transmit the data sent by the RSU or vehicle to a destination directly or through the ground base station. AGTO enhances communication intelligence and reduces the complexity of the network, thereby contributing to an overall performance improvement. The proposed approach is evaluated in terms of packet delivery ratio (PDR), communication delay, network throughput, energy efficiency, energy consumption, and routing overhead. Simulation results validate the effectiveness of the proposed approach over state-of-the-art approaches by achieving a maximum PDR (93.28%), throughput (658.17 Kbps), energy efficiency (88.19%), minimum routing overhead (865 packets), energy consumption (1.13 KJ) and communication delay (125.46 ms). The findings indicate the superior performance of the ITA-AGTO approach.

The DC-DC converters are essential in power electronics as they maintain a stable output voltage even when there are changes in input voltage and load current. This study introduces an advanced Proportional-Derivative (PD) compensator for buck converters. The compensator enhances stability and transient responsiveness by employing a unique modulation technique that has not been previously applied in this context. The proposed method entails applying an input of 28 volts, which yields an output amplification of 15 volts, even in the presence of interference. The small signal transfer function of the buck converter is meticulously derived, considering the converter’s dynamic behavior to achieve exceptional results. The transfer function comprehensively explains the intricate relationship between the input and output voltages, providing the theoretical basis for our distinctive control approach. The MATLAB code accurately generates the Bode diagram of the buck converter by employing the small signal transfer function. The graph illustrates the frequency response of the converter, a crucial factor in enhancing the stability and quality of the output voltage. The proposed research is substantiated by mathematical data shown through several simulated figures, distinguishing it from conventional methodologies. The implemented research achieves the maximum efficiency exceeding 95% with a minimum ripple factor.

Violence detection holds immense significance in ensuring public safety, security, and law enforcement in various domains. With the increasing availability of video data from surveillance cameras and social media platforms, the need for accurate and efficient violence detection algorithms has become paramount. Automated violence detection systems can aid law enforcement agencies in identifying and responding to violent incidents promptly, thereby preventing potential threats and ensuring public protection. This research focuses on violence detection in large video databases, proposing two keyframe-based models named DeepkeyFrm and AreaDiffKey. The keyframes selection process is critical in violence detection systems, as it reduces computational complexity and enhances accuracy. EvoKeyNet and KFCRNet are the proposed classification models that leverage feature extraction from optimal keyframes. EvoKeyNet utilizes an evolutionary algorithm to select optimal feature attributes, while KFCRNet employs an ensemble of LSTM, Bi-LSTM, and GRU models with a voting scheme. Our key contributions include the development of efficient keyframes selection methods and classification models, addressing the challenge of violence detection in dynamic surveillance scenarios. The proposed models outperform existing methods in terms of accuracy and computational efficiency, with accuracy results as follows: 98.98% (Hockey Fight), 99.29% (Violent Flow), 99% (RLVS), 91% (UCF-Crime), and 91% (ShanghaiTech). The ANOVA and Tukey tests were performed to validate the statistical significance of the differences among all models. The proposed approaches, supported by the statistical tests, pave the way for more effective violence detection systems, holding immense promise for a safer and secure future. As violence detection technology continues to evolve, our research stands as a crucial stepping stone towards achieving improved public safety and security in the face of dynamic challenges.

The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.

Adults who have brain tumors face a serious and potentially fatal challenge since the tumors rapidly growing malignant cells can seriously impair their physiological ability. In clinical practice, imaging techniques such as MRI, PET, and CT scans are widely used to determine the size, kind, and location of tumors. The goal of this research is to create a Computer-Aided Diagnostic (CAD) system that can automatically segment and classify brain tumors utilizing T1W-CE Magnetic Resonance Imaging (MRI) of the brain. The CAD system will involve two primary tasks: tumor classification, which determines the type of tumor depicted in the image, and tumor segmentation, which involves accurately determine the tumor region from the surrounding healthy tissue. By automating these processes, the proposed system aims to enhance the accuracy and effectiveness of brain tumor diagnosis and treatment planning. The classification of brain tumors into multiple classes is recognized as a complex challenge within the field of medical imaging. This research article proposes a model named VS-BEAM that can be used efficiently for clinical decision-making. The proposed VS-BEAM (Voting Based Semi-Supervised Bayesian Ensemble Attention Mechanism) model has been examined for a brain tumor’s multi-class classification. The VS-BEAM model achieved the highest level of accuracy possible. The proposed work achieves maximum sensitivity, specificity, and diagnostic accuracy compared to existing models using T1W-CE MRI images. A convolutional autoencoder is utilized for extracting tumors from MRI images. The accuracy obtained from testing data of 264 brain tumors was 98.91%, indicating that the method is effective and can be used in the clinical context to assist in detecting larger or even smaller tumors.

Text classification is a common task in natural language processing (NLP), where the objective is to assign predefined categories or labels to a given text. Detecting sarcasm and classifying sentiment and dialect in NLP has practical applications, including spam detection, topic classification, and sentiment analysis. However, sarcasm and sentimental expressions, such as irony, humor, or criticism, can be difficult to identify through traditional NLP methods due to their implicit nature. To address this, we propose a Modified Switch Transformer (MST) for detecting sarcasm and classifying sentiment and dialect in Arabic text data. Our approach includes two key contributions: Variational Enmesh Expert’s Routing ( $VE_{e}R$ ) and Probabilistic Projections ( $P_{\phi} $ ). The switch transformer model incorporates probabilistic projections using a Variational Spatial Gated Unit-MLP to enhance the embedding generation mechanism. This updated mechanism introduces a variational aspect, providing dynamic control over the flow of information in the network, in contrast to the simpler embedding generation phase used in the original switch transformer. Moreover, we incorporate Variational Enmesh Expert’s Routing, which utilizes a hierarchical set of Variational experts, where each expert is a small and variational-directed acyclic graph network. The $VE_{e}R$ routing technique allows the network to dynamically choose which path to take at each layer based on the input, using a set of weights learned during training to determine the best route for a given input. Instead of optimizing route paths deterministically, we utilize Variational Inference and model each route as a random variable from a distribution. Our study evaluates the effectiveness of the Modified Switch Transformer (MST) model on the ArSarcasm Dataset, which includes Arabic language data related to sarcasm, dialect, and sentiments. We compare the performance of our proposed model with existing state-of-the-art models in the literature. The results show that the switch transformer outperforms other models in detecting sarcasm and also performs well in classifying sentiment and dialect.

Recent advances in graph-structured learning have demonstrated promising results on the graph classification task. However, making them scalable on huge graphs with millions of nodes and edges remains challenging due to their high temporal complexity. In this paper, by the decomposition theorem of Laplacian polynomial and characteristic polynomial we established an explicit closed-form formula of the global mean-first-passage time (GMFPT) for hexagonal model. Our method is based on the concept of GMFPT, which represents the expected values when the walk begins at the vertex. GMFPT is a crucial metric for estimating transport speed for random walks on complex networks. Through extensive matrix analysis, we show that, obtaining GMFPT via spectrums provides an easy calculation in terms of large networks.

The current analysis is carried out for the Al2O3 nanoparticles transportation through a permeable cubic geometry under the influence of the magnetic force through a hot cubic object. The physical phenomenon described in the basic equations together with the Maxwell’s equations. Then D3Q19 model is sketched for the discretization of the velocity vectors for using the $3D$ Lattice Boltzmann Method (LBM). The impact of Brownian motion in the Al2O3-H2O nanofluids is considered by taking the Koo–Kleinstreuer model into consideration. The nanoparticles transportation under the impacts of the buoyancy and Lorentz forces, and permeability is studied with LBM. Numerical simulations are performed for different values of magnetic parameter, Darcy’s and Rayleigh numbers. The validation of the applied technique is presented in Fig. 3 in the form of isotherms by comparing the obtained results with Calcagni et al.. For the enhancement of the heat transfer analysis a quantitative comparison of the current study is presented in Fig. 4. All the results for various parameters are presented in the form of isotherms. The obtained results show the efficient conduction for higher values of $Ha$ . The larger values of $Da$ show reduction in the boundary layer thickness. The average Nusselt number $Nu_{ave} $ enhances with higher values of the permeability parameter. The efficiency of the implemented technique is demonstrated in Table 4, where the Nusselt number is tabulated for distinct numbers of $Gr$ and $Ha$ , and are compared with the available literature.

360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

Current extended virtual reality (VR) applications use 360-degree video to boost viewers' sense of presence and immersion. The quality of experience (QoE) effectiveness of 360-degree video in VR has often been related to many aspects. The four significant aspects to take into account when evaluating QoE in the VR are a sense of presence and immersion, acceptability, reality judgment, and attention captivated. In this manuscript, we subjectively investigate the impact of 360-degree videos QoE-affecting factors, including quantization parameters (QP), resolutions, initial delay, and different interruptions (single interruption and two interruptions) on these QoE-aspects. We then design a Decision Tree-based (DT) prediction models that predict users' VR immersion, acceptability, reality judgment, and attention captivated based on subjective data. The accuracy performance of the DT-based model is then analyzed with respect to mean absolute error (MAE), precision, accuracy rate, recall, and f1-score. The DT-based prediction model performs well with a 91% to 93% prediction accuracy, which is in close agreement with the subjective experiment. Finally, we compare the performance accuracy of the proposed model against existing Machine learning methods. Our DT-based prediction model outperforms state-of-the-art QoE prediction methods.