This study presents a wind speed prediction model using monthly average wind speed data, employing the Extreme Gradient Boosting (XGBoost) algorithm to enhance forecasting accuracy for wind farm operations. Accurate wind speed forecasting is crucial for optimizing energy production, ensuring grid stability, and improving operational planning. Existing studies on enhancing wind speed prediction using ML algorithms have some drawbacks based on accuracy, efficient prediction, and stuck-in-local-optima parameters. The dataset comprises monthly average wind speed measurements, and extensive preprocessing is conducted to prepare the data for machine learning. Various hyperparameter tuning techniques, including Randomized Search, Grid Search, and Bayesian Optimization, are applied to improve prediction accuracy. The performance of the model is evaluated utilizing key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Continuous Ranked Probability Score (CRPS), and Maximum Error. The results indicate that hyperparameter tuning significantly improves model accuracy. Specifically, Grid Search demonstrates superior performance for short-term (one-month) forecasting, while Randomized Search is more effective for long-term (six-month) forecasting. The findings emphasize the critical importance of hyperparameter tuning strategies in the development of reliable wind speed forecasting models, which have significant implications for the efficient management of wind energy resources.

Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

Some people manipulate sales in marketplaces and other retail settings by combining beef and pork since the prices are so high. In addition to educating the public about these distinctions, this research aims to develop a technological solution for recognizing and differentiating between pork and beef. The proposed hybrid model, a combination of a Resnet-50 and a Support Vector Machine (SVM), is introduced for the classification of Beef and Pork Meat. In this hybrid model, the Resnet-50 functions as a powerful feature extractor, then utilizing its inherent ability to automatically capture distinctive features from diverse and highly specific meat image datasets. The SVM, serving as the binary classifier, effectively utilizes the extracted features for precise classification. The hybrid model achieves an outstanding accuracy of 100%, surpassing the performance of individual classifiers, with Resnet-50 achieving 97% accuracy and Resnet-50 achieving 97% obtained from the Hybrid model by gaining the best parameter C is 0,1 and the Kernel is linear. This remarkable outcome signifies the synergistic effectiveness of combining Resnet-50 and SVM.

A home automation system is essential for promoting a safe and comfortable living environment and notable energy conservation for the user. However, the system's favour had been obstructed by cost, power usage, inadequate security, complexity, and no emergency backup power. Current home automation systems with controllers were limited by their number of ports, fixed architecture, non-durable and non-parallel executions. Keeping this in view, integration of home comfort system, security system, and the automatic load transfer switch features are proposed using the base of Cyclone IV E: EP4CE115F29C7 FPGA Board (DE2-115). The top-level module is developed via Verilog Hardware Descriptive Language (HDL) with the bottom-up technique and used test bench for functional verification via ModelSim-Altera. The PWM method was applied to the lighting system to control the dimming of light through its digital signals via a maximum 500000 counter to improve energy efficiency for the proposed design. In this project, 200Hz pulses are successfully simulated to prevent visible flickering of lights in duty cycle generation. The light intensity of 40% and 100% are verified and successfully generated according to the inputs provided by the status of the LDR sensor and IR sensor. The proposed controller gives correct corresponding outputs to the 13 actuators based on the detected input stimuli. The proposed design utilized a total of 162 (<1%) logic elements, 32 registers, and total pins of 74 (14%). The proposed design successfully integrated the three-sub module and provided control on comfort and security system operations to prevent service failure during power blackout conditions at the top-level and utilized a low ratio of the FPGA.

The global transition toward electric vehicles (EVs) necessitates precise forecasting of charging demand to ensure grid stability and optimize infrastructure. Although gradient boosting (GB) Machines demonstrate significant potential for this task, performance limitations often arise from inadequate hyperparameter tuning. However, existing studies lack a systematic comparison of advanced metaheuristic hyperparameter optimization techniques and stacked ensemble learning under a unified forecasting framework for EV charging demand prediction. This study introduces a meta‐optimized forecasting framework that integrates advanced hyperparameter optimization techniques with a novel stacked ensemble architecture. A comprehensive comparison of five optimization methods, covariance matrix adaptation evolution strategy (CMA‐ES), Bayesian optimization (Hyperopt), particle swarm optimization (PSO), Optuna, and Randomized Search, is conducted to tune a GB model. Analysis indicates that CMA‐ES yields superior performance among single models, achieving a reduction in mean absolute error (MAE) by 23% compared to default parameters. Furthermore, the proposed stacked ensemble model amalgamates predictions from diversely tuned base learners through a meta‐learner, resulting in a breakthrough MAE of 0.94 kWh (a 53.6% reduction) and an accuracy measure of R ² value of 0.95. This ensemble effectively eliminates extreme errors and exhibits exceptional temporal fidelity, demonstrating responsiveness to demand spikes with minimal lag. The findings offer clear guidelines for selecting optimization strategies based on accuracy–computational cost trade‐offs, establishing that systematic hyperparameter optimization in conjunction with ensemble learning represents a transformative approach for EV charging demand forecasting.