ABSTRACT Early detection of heart disease is essential for minimizing mortality, particularly in resource-constrained health-care environments. Recent models have shown accurate performance for medical diagnosis; however, they often struggles from limited interpretability and sensitivity to missing clinical data. To address these challenges, this paper proposes A Label-Enhanced Graph Neural Network Optimized by Elk Herd Optimization for Heart Disease Detection (HDD–LEGNN–EHOA). The input data is taken from Cleveland Heart disease data collection. Initially, data are pre-processed using Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter (GCSGQF) to impute missing values. Then, the processed data is given to Secretary Bird Optimization Algorithm (SBOA) to select optimal characteristics. Then, the selected attributes fed to Label-Enhanced Graph Neural Network (LEGNN) to detect heart disease as heart disease present and heart disease absent. Elk Herd Optimization Algorithm (EHOA) to fine-tune the weight parameter of LEGNN. The newly suggested HDD–LEGNN–EHOA framework is assessed based on some metrics like accuracy, sensitivity, and computational time. Finally, the performance of HDD–LEGNN–EHOA method provides attains 25.68%, 22.54%, and 31.24% higher accuracy when compared to the existing models, respectively.

ABSTRACT This study presents the design and development of a novel lightweight bicycle brake disc that offers significant improvements in weight reduction, thermal performance, and aesthetic customization compared to conventional models. Traditional bicycle brake discs, typically made entirely of chromium-molybdenum steel, are relatively heavy and susceptible to excessive heat buildup during prolonged braking. The newly designed brake disc features a composite structure, incorporating a chromium-molybdenum steel outer frame for mechanical strength and a lightweight aluminum alloy core to reduce overall weight while maintaining structural integrity significantly. The brake disc design enhances aesthetics through an anodized aluminum alloy center frame, allowing for vibrant color customization tailored to fleet and brand preferences. More importantly, the riveted joint structure between the steel outer frame and the aluminum core acts as a thermal insulation, increasing thermal resistance and preventing excessive heat transfer to the hub. This design effectively reduces thermal deformation risk, improving braking reliability and rider safety. This study investigates the thermal properties and fatigue crack growth behavior of a proposed composite brake disc through a combination of experimental testing and numerical simulations. A comparison between the traditional brake disc (Model A) and the novel composite brake discs (Models B, C, and D) reveals that Model D demonstrates the most effective thermal isolation. In addition, structural evaluations indicate that Model D significantly outperforms Model A in terms of deformation, stress, fatigue life, damage, and safety factors. Model D demonstrates a significantly higher maximum fatigue life of 108 cycles, which is 100 times greater than that of Model A. These findings provide valuable insights for optimizing performance, enhancing safety, and improving the user experience in modern bicycle braking systems.

ABSTRACT The Optimum Moisture Content (OMC) is the moisture level at which soil acquires peak density during compaction, making it crucial in geotechnical engineering and construction to determine the optimal conditions for maximum soil strength and stability in infrastructure. The relationship between OMC and Maximum Dry Density (MDD) is integral, as both parameters together determine the compaction characteristics of soil. This correlation varies depending on various features, highlighting the combined significance of OMC and MDD in geotechnical applications. Current methods for determining OMC are costly and time-consuming, highlighting the need for more efficient approaches. Machine learning-based methods offer a promising alternative, facilitating the development of innovative predictive models and algorithms that can enhance the accuracy and efficiency of OMC predictions compared to traditional empirical techniques. This study employs the Multi-Layer Perceptron model to address challenges in constructing a machine-learning model. Additionally, it integrates two distinct meta-heuristic optimization approaches, namely the Coot Optimization Algorithm and the Golden Jackal Optimizer, to achieve optimal results. As a result of this integration, two hybrid models, MLGJ and MLCO, were generated in three layers of the MLP models. The productivity of the frameworks was assessed by drawing on five metrics, including R2, RMSE, MSE, SI, and RAE. The outcomes revealed that the MLCO (2) approach stands out as the top-performing predictor among the three models of this study. It acquires an impressive maximum R2 value of 0.998 in the training stage, indicating exceptional explanatory capability and exhibiting notably low RMSE and MSE values of 0.2 and 0.04, respectively. This approach signifies minimal prediction discrepancies as opposed to different models utilized in this study. On the other hand, MLGJ (1) was identified as the weakest model in this study, registering the lowest value for R2 at 0.965 and the highest value of RMSE at 1.568.

ABSTRACT Detection and classification of power system instabilities due to the raised transient effect during switching operation of different kinds of load, capacitor bank, large sized induction motor is challenging in microgrid network because of inconstant direction of current and complexity of network. However, this classification study is a mandatory measure to maintain quality power in modern microgrid network. A novel decision tree (DT) classification based machine learning technique is proposed in this study to do this significant task. The test transient signals during several switching operation have been extracted from a real microgrid network in India. The statistical attributes are computed from the coefficients of discrete wavelet transform (DWT) of transient voltage signals. The significant features are selected according to the performance of the processed DWT. The fine, medium, and coarse DT classifiers considering different number of splits and ensembled tree based on bagging have been trained in the next stage of algorithm. The performances of each classifier are assessed in terms of confusion matrix and receiver operating characteristics curve. The accuracy and training time of each classifier are compared to recommend a specific method to detect and classify the similar type of transient events. A 99.4% accuracy has been achieved with a very lower detection time from fine DT.

ABSTRACT The variable DC link direct torque control (DTC) for induction motors was proposed in this paper utilizing an artificial neural network (ANN). Variable DC-link voltage is utilized as the inverter’s controlling input to improve the induction motor’s performance. By injecting the dynamically variable dc-link voltage, inverter switching losses are decreased and inverter efficiency is increased. Two significant limitations are the torque ripple in DTC and the speed regulation of induction motors at low speeds. The driveline of an electric vehicle is modeled, and the suggested control is put into practice to enhance low-speed speed regulation, high dynamic performance, and minimal torque ripple. With variable DC-link voltage based on ANN, an improvement in torque and speed is made possible. Additionally, the suggested strategy minimizes the current ripples. To verify the improved efficacy of the electric driveline under various operating scenarios, a proposed control of the electric driveline is implemented using MATLAB SIMULINK, and the results are compared with the conventional scheme. The recommended speeds for the three speed ranges are as follows: 300 rpm for low speed, 900 rpm for medium speed, and 1275 rpm for high speed. The torque ripples decreased from 0.39 to 0.24 in the low-speed region, 0.38 to 0.24 in the medium-speed region, and 0.36 to 0.24 in the high-speed region while the motor was operated at a constant load torque of 2 N-m. The torque ripples decreased from 0.4 to 0.274 in the low speed region, 0.4 to 0.274 in the medium speed zone, and 0.2 to 0.274 in the medium speed region when the motor operated at various speed regions with a constant load. The main effect of running the motor at different load torques, such as 1.5 N-m, 2.5 N-m, and 2.0 N-m, is shown in Table 9 and numerical data of torque ripples is presented in Table 9.

ABSTRACT Electrical power generation in Oman depends mainly on natural gas, while in rural areas it is based mainly on diesel fuel. Oman has committed to net-zero emissions by 2050, in line with the Paris Agreement’s goal of limiting global warming to 1.5°C. The objective of this paper is to propose an optimal energy mix model for electricity generation from various energy sources, such as gas, diesel, wind, and photovoltaic, that considers more renewable sources in the energy mix. The optimization model minimizes various costs such as construction cost, operation cost, fuel cost, and carbon emissions, while satisfying the load demand. In this paper, Masirah Island is selected to perform our analysis. Several scenarios are contemplated, including grid extension to the island. The results found that gas-powered generators with 20% PV and storage are the best option in terms of the levelized cost of electricity. The grid extension is an economically feasible option if the existing load is more than doubled.

ABSTRACT Home area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks.

ABSTRACT In this era of information security and communication, a major priority is the achievement of a robust and secure steganography system when thinking about information concealment. The development of such an information-hiding scheme demands that the scheme be able to hide a secret message within the cover media. The most vexing issues in existing steganography protocols are imperceptibility, security, and capacity, and researchers have frequently emphasized a trade-off between these issues. Scholars have consistently ignored the balance between security and payload because resolving one problem has been shown to have an impact on the other, and vice versa. To overcome these problems, an effective method known as the Conventional Neural Network based Edge Detection Method (CNN-EDM) has been presented for image steganography in this study. The CNN-EDM is used to improve the contributions of the proposed scheme. Four main stages were used to achieve the objectives in this research, beginning with the cover image and secret image preparation, followed by embedding, and culminating in extraction. The last stage is the evaluation stage, which employs several evaluations to benchmark the obtained results. A standard database from the Signal and Image Processing Institute (SIPI) containing color and grayscale images with 512 × 512 pixels was utilized in this study. Different parameters were used to test the performance of the suggested scheme based on security and imperceptibility (image quality). The image quality was evaluated using three important metrics: histogram analysis, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, two metrics were used to evaluate the security properties of the proposed system: the Human Visual System (HVS) and Chi-square (X2) attacks. The evaluations showed that the proposed scheme can enhance the capacity, invisibility, and security features and address the already existing problems in this domain.

ABSTRACT This paper discusses an overview of power allocation (PA) strategy for enabled sixth-generation (6 G) communication in visible light non-orthogonal multiple access (VL-NOMA) scenario. Light emitting diode (LED) deployed in the advancement of VL-NOMA 6 G limit-less efficiencies when complemented with radio frequency (RF)/millimeter-wave (), terahertz (THz), free-space optical (FSO) and VL. The encountered challenges in the currently deployed fifth generation (5 G) technology such as signal failures, low power, data loss, latency from (5), and loss of signal strength were solved with 6 G providing disruptive technologies, latency from (), massive connectivity, cell-less communications, modified machine learning algorithms, new security measures, more energy-efficient, infrastructure smart networking management, new spectrum, artificial intelligence (AI), disaggregation and virtualization supporting enough bandwidth, and increase data rate. The PA uses available power to distribute entire signals assigning power levels to multi-devices connected to VL-NOMA applications for envisioned virtual reality, unlocking all the possibilities of an indoor and outdoor transmission positioned to achieve superior accuracy, reliability and unlimited access. The 6 G 2030 roadmap positioning LED as a carrier assigning VL-NOMA PA techniques for a green solution improving high quality of services, higher data rate, reduced power consumption (using metasurface schemes), high capacity, energy efficiencies, low cost, illumination, communication and indication as detailed herein.

ABSTRACT This article emphasizes the design and analysis of an optimal intelligent controller for frequency regulation application. The load frequency control (LFC) mechanism is an eminent and essential mechanism to reinstate the system frequency and scheduled tie-line power to their nominal values. Employing an appropriate controller enhances the operation of the LFC mechanism. Hence, this article put forward an adoptive control policy-based fuzzy-fractional ordered PI controller parallel with fractional PIDN controller (i.e., Fuzzy (PIλf)+PIλDN) for the LFC mechanism. Besides that, a maiden attempt of using a new opposition-learning-based volleyball premier league (OVPL) algorithm is carried out to obtain optimal control parameters. The proposed optimal intelligent controller is explored for a nonlinear multi-area interconnected power system under deregulated environment. The proposed LFC scheme<apos;>s performance has been compared with several popular strategies for step and random perturb in load. Also, the robustness of the proposed scheme has been verified for a wide range of system parameter variations. On the other hand, the modified HVDC tie-line model and the impact of the inertia emulation technique (IET) using converter capacitors on transient behavior are illustrated in this article. Finally, the efficacy of the proposed LFC scheme has been verified over published literature on its platform.