Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), novel stochastic motivated by natural systems. The proposed WWPA’s basic concept based on modeling plant’s behavior while hunting expedition. To find prey, WWPA uses plants as search agents. We present mathematical model for use addressing problems. Twenty-three objective functions of...

The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in domain ML, making energy key player fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by Supervisory Control and Data Acquisition (SCADA) data turbines, wherein conventional fault pattern identification time-consuming, guesswork process. This work proposed an intelligent automated approach early...

Self-driving car plays a crucial role in implementing traffic intelligence. Road smoothness front of self-driving cars has significant impact on the car's driving safety and comfort. Having potholes road may lead to several problems, including damage occurrence collisions. Therefore, should be able change their behavior based real-time detection potholes. Various methods are followed address this problem, reporting authorities, employing vibration-based sensors, 3D laser imaging. However,...

This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types antennas. The antennas are metamaterial and double T-shape monopoles. ADSCFGWO algorithm is based on technique recently developed powerful optimization techniques: a modified (GWO) value (SCA). suggested approach utilizes capabilities both algorithms to balance better exploration exploitation responsibilities process while achieving rapid convergence. First, new...

The design of an antenna requires a careful selection its parameters to retain the desired performance. However, this task is time-consuming when traditional approaches are employed, which represents significant challenge. On other hand, machine learning presents effective solution challenge through set regression models that can robustly assist designers find out best achieve intended In paper, we propose novel approach for accurately predicting bandwidth metamaterial antenna. proposed...

Biodiesel is considered to be a promising alternative option diesel fuel. The main contribution of the current work improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used evaluate output performance engine, as follows: brake torque (BT), specific fuel consumption (BSFC), and thermal efficiency (BTE), varying two input parameters (engine speed type). speeds in 1200–2400 rpm range. blends, containing 20 vol.% vegetable oil 80 pure fuel,...

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment ALL strongly associated with early diagnosis disease. Current practice for initial performed through manual evaluation stained smear microscopy images, which time-consuming and error-prone process. Deep learning-based human-centric biomedical has recently emerged as powerful tool assisting physicians making medical decisions....

One of the most promising research areas in healthcare industry and scientific community is focusing on AI-based applications for real medical challenges such as building computer-aided diagnosis (CAD) systems breast cancer. Transfer learning one recent emerging techniques that allow rapid progress improve imaging performance. Although deep classification cancer has been widely covered, certain obstacles still remain to investigate independency among extracted high-level features. This work...

Metaheuristic optimization algorithms present an effective method for solving several problems from various types of applications and fields. Several metaheuristics evolutionary have been emerged recently in the literature gained widespread attention, such as particle swarm (PSO), whale algorithm (WOA), grey wolf (GWO), genetic (GA), gravitational search (GSA). According to literature, no one metaheuristic can handle all problems. Hence novel methodologies are still needed. The Al-Biruni...

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase efficacy classification algorithms, it necessary to identify relevant subset features in a given domain. This means that challenge can be seen as an optimization problem, thus meta-heuristic techniques utilized find solution. Methodology: this work, we propose novel hybrid binary algorithm solve problem by combining two algorithms: Dipper Throated Optimization (DTO) Sine...

The vast majority of today's data is collected and stored in enormous databases with a wide range characteristics that have little to do the overarching goal concept. Feature selection process choosing best features for classification problem, which improves classification's accuracy. considered multi-objective optimization problem two objectives: boosting accuracy while decreasing feature count. To efficiently handle process, we propose this paper novel algorithm inspired by behavior...

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with meant, latter usually rather or extremely offensive, meant to offend humiliate someone. In routine conversations on social media websites, frequently utilized. Sentiment analysis procedures are prone errors because can change a statement’s meaning. Analytic accuracy apprehension has increased automatic networking tools have grown. According preliminary studies, computerized sentiment...

In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due its widespread occurrence around the world. Diabetes is just one of several diseases for techniques can be in diagnosis, prognosis, and assessment procedures.In this paper, we propose a new approach boosting classification based on metaheuristic optimization algorithm. The proposed proposes feature...

Abstract Potatoes are an important crop in the world; they main source of food for a large number people globally and also provide income many people. The true forecasting potato yields is determining factor rational use maximization agricultural practices, responsible management resources, wider regions’ security. latest discoveries machine learning deep new directions to yield prediction models more accurately sparingly. From study, we evaluated different types predictive models, including...

<abstract> <p>In this study, we present a comprehensive framework for enhancing the temperature control of electric furnaces, integrating three novel components: proportional-integral-derivative controller with filter (PID-F), customized objective function, and modified eel foraging optimization (mEEFO) algorithm. The PID-F controller, introduced first time in literature leverages coefficient to effectively mitigate kick effect, improving transient frequency responses. To further...

Abstract Potato blight, sometimes referred to as late is a deadly disease that affects Solanaceae plants, including potato. The oomycete Phytophthora infestans causal agent, and it may seriously damage potato crops, lowering yields causing financial losses. To ensure food security reduce economic losses in agriculture, diseases must be identified. approach we have proposed our study provide reliable efficient solution improve blight classification accuracy. For this purpose, used the...

Abstract Potato consumption forecasting is crucial for several stakeholders in the food market. Due to market flexibility, farmers can manipulate volumes planted a given type of produce reduce costs and improve revenue. Consequently, it means that establishing optimal inventories or inventory levels possible critical sense sellers avoid either inadequate excessive may lead wastage. In addition, governments predict future deficits put measures place guarantee they have steady supply some...

Shell and tube heat exchangers are pivotal for efficient transfer in various industrial processes. Effective control of these structures is essential optimizing energy usage ensuring system reliability. In this regard, study focuses on adopting a fractional-order proportional-integral-derivative (FOPID) controller shell exchanger. The novelty work lies the utilization an enhanced version cooperation search algorithm (CSA) FOPID tuning, offering novel approach to optimization. optimizer...

Heart disease is a category of various conditions that affect the heart, which includes multiple diseases influence its structure and operation. Such may consist coronary artery disease, characterized by narrowing or clotting arteries supply blood to heart muscle, with resulting threat attacks. rhythm disorders (arrhythmias), valve problems, congenital defects present at birth, muscle (cardiomyopathies) are other types disease. The objective this work introduce Greylag Goose Optimization...

One of the most common kinds cancer is breast cancer. The early detection it may help lower its overall rates mortality. In this paper, we robustly propose a novel approach for detecting and classifying regions in thermal images. proposed starts with data preprocessing input images segmenting significant interest. addition, to properly train machine learning models, augmentation applied increase number segmented using various scaling ratios. On other hand, extract relevant features from...

Secure and economic operation of the power system is one prime concerns for engineers 21st century. Unit Commitment (UC) represents an enhancement problem controlling operating schedule units in each hour interval with different loads at various technical environmental constraints. UC complex optimization tasks performed by plant regular planning system. Researchers have used a number metaheuristics (MH) solving this demanding problem. This work aims to test Gradient Based Optimizer (GBO)...

Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast accurately. However, the accuracy these still needs more improvements achieve accurate results. In this paper, an optimized model proposed boosting prediction speed. The optimization performed in terms a new algorithm based on dipper-throated (DTO) and genetic (GA), which referred as (GADTO). used optimize bidrectional long short-term memory (BiLSTM)...