Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient’s severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient’s current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy.

Since the deregulation of the power markets, accurate short term Electricity Price Forecasting (EPF) has become crucial in maximizing economic benefits and mitigating power market risks. Due to the challenging characteristics of electricity price, which comprise high volatility, rapid spike, and seasonality, developing robust machine learning prediction tools becomes cumbersome. This work proposes a new hybrid machine learning method for a day-ahead EPF, which involves linear regression Automatic Relevance Determination (ARD) and ensemble bagging Extra Tree Regression (ETR) models. Considering that each model of EPF has its own strengths and weaknesses, combining several models gives more accurate predictions and overcomes the limitations of an individual model. Therefore, the linear ARD model is applied because it can efficiently deal with trend and seasonality variations; on the other hand, the ensemble ETR model is employed to learn from interactions, and thus combining ARD with ETR produces robust forecasting outcomes. The effectiveness of the proposed method was validated using a data set from the Nord Pool electricity market. The proposed model is compared with other models to demonstrate its superiority using performance matrices, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiment results show that the proposed method achieves lower forecasting errors than other individual and hybrid models. Additionally, a comparative study has been performed against previous works, where forecasting measurement of the proposed method outperforms previous works’ accuracy in forecasting electricity price.

The global shift toward renewable energy, particularly wind power, underscores the critical need for advanced fault diagnosis systems to optimize wind turbine reliability and efficiency. While traditional diagnostic methods remain foundational, their limitations in addressing the nonlinear dynamics and operational complexity of modern turbines have accelerated the adoption of Artificial Intelligence (AI)-driven approaches. This review systematically examines advancements in AI-based fault diagnosis techniques, including machine learning (ML) and deep learning (DL), from 2019 to 2024, analyzing their evolution, efficacy, and practical challenges. Drawing on a curated selection of 55 studies (identified via structured searches across IEEE Xplore, ScienceDirect, and Web of Science), the paper prioritizes research employing data-driven or model-based methodologies with explicit experimental validation and clearly documented data sources. The excluded works lacked English accessibility, validation, or data transparency. Focusing on high-impact faults in gearboxes, blades, and generators, these components are responsible for over 70% of turbine failures, the review maps prevalent ML and DL algorithms, such as CNNs, LSTMs, and SVMs, to specific fault types, revealing hybrid AI models and real-world data integration as key drivers of diagnostic accuracy. Critical gaps are identified, including overreliance on simulated datasets and inconsistent signal preprocessing, which hinder real-world applicability. This study concludes with actionable recommendations for future research, advocating adaptive noise-filtering techniques, scalable hybrid architectures, and standardized benchmarking using operational turbine data. By bridging theoretical AI advancements with practical deployment challenges, this work aims to inform next-generation fault diagnosis systems, enhancing turbine longevity and supporting global renewable energy goals.

A low-power, low-phase noise, high tuning range, and fully integrated inductorless RC-VCO (voltage-controlled oscillator) for OC-48 application is designed and simulated in standard 0.18 μm CMOS technology. The proposed inductorless RC-VCO has a simulated phase noise of -141 dBc/Hz at 1 MHz offset from the carrier frequency of 2.4 GHz, with a bias current of 605 μA and voltage headroom of 1.8 V. It has 50% tuning range at 2.4 GHz of operating frequency and consumes 1.09 mW of power. This RC-VCO shows a Figure of merit performance of -208.2 dBc/Hz at the desired frequency.