One of the main challenges facing current approaches speech emotion recognition is lack a dataset large enough to train currently available deep learning models properly. Therefore, this paper proposes new data augmentation algorithm enrich emotions with more samples through careful addition noise fractions. In addition, hyperparameters are either handcrafted or adjusted during training process. However, approach does not guarantee finding best settings for these parameters. we propose an...

The world is still trying to recover from the devastation caused by wide spread of COVID-19, and now monkeypox virus threatens becoming a worldwide pandemic. Although not as lethal or infectious numerous countries report new cases daily. Thus, it surprising that necessary precautions have been taken, will be if another pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, COVID-19 patient...

This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA solve problems continuous binary decision variables. SCMWOA first tested on nineteen datasets from UCI Machine Learning Repository different numbers attributes, instances, classes for feature selection. It then employed several benchmark functions classical engineering case studies. applied solving constrained problems. two...

Recent technologies such as artificial intelligence, machine learning, and big data are essential for supporting healthcare monitoring systems, particularly Monkeypox confirmed cases. Infected uninfected cases around the world have contributed to a growing dataset, which is publicly available can be used by intelligence learning predict of at an early stage. Motivated this, we propose in this paper new approach accurate prediction based on optimized Long Short-Term Memory (LSTM) deep...

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...

Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility continuity. Transformer diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers’ ratio, Dornenburg, IEC code 60599, suffer poor diagnosis. Therefore, recent research has been developed diagnose fault diagnostic accuracy using combined methods with artificial intelligence optimization methods....

Personal Identification Numbers (PIN) and unlock patterns are two of the most often used smartphone authentication mechanisms. Because PINs have just four or six characters, they subject to shoulder-surfing attacks not as secure other techniques. Biometric methods, such fingerprint, face, iris, now being studied in a variety ways. The security biometric is based on PIN-based backup when maximum defined number failures surpassed during process. Keystroke-dynamics-based has been circumvent...

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...

As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures control spread of pandemic. Because excessive number infected patients resulting deficiency testing kits in hospitals, a rapid, reliable, automatic detection COVID-19 extreme need curb infections. By analyzing chest X-ray images, novel metaheuristic approach proposed based on hybrid dipper throated particle swarm optimizers. The lung region was...

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...

Background and aim: Machine learning methods are examined by many researchers to identify weeds in crop images captured drones. However, metaheuristic optimization is rarely used optimizing the machine models weed classification. Therefore, this research targets developing a new algorithm that can be optimize ensemble boost classification accuracy of images. Methodology: This work proposes approach for classifying wheat sprayer drone. The proposed based on voting classifier consists three...

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...

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals developed countries experiencing monkeypox. Such conditions often carry less obvious but no devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due the visual resolution monkeypox disease images, medical specialists high-level tools are typically required for a proper diagnosis. The manual diagnosis is subjective, time-consuming, labor-intensive....

Oral cancer is a deadly form of cancerous tumor that widely spread in low and middle-income countries. An early affordable oral diagnosis might be achieved by automating the detection precancerous malignant lesions mouth. There are many research attempts to develop robust machine-learning model can detect from images. However, these still lacking high precision detection. Therefore, this work aims propose new approach capable detecting medical images with higher accuracy. In work, novel...

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...

Over the past few decades, healthcare sector has developed along parallel paths with trendy technology. Modern practices of artificial intelligence strategies have led to improved diagnosis, development effective treatments, telemedicine, and patient monitoring devices. Many applications emerged that help doctors workers, most notably Chatbot. It is a program utilises techniques conduct conversations in real-time. This able simulate you are natural person writing or talking another Internet...

In healthcare, medical image processing is considered one of the most significant procedures used in diagnosing pathological conditions. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and X-ray visualization have been used. Health institutions are seeking to use artificial intelligence techniques develop reduce burden on physicians healthcare workers. Deep learning has occupied an important place field, supporting specialists analysing images. This article will...

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...

Technology for anticipating wind speed can improve the safety and stability of power networks with heavy penetration. Due to unpredictability instability wind, it is challenging accurately forecast speed. Several approaches have been developed this accuracy based on processing time series data. This work proposes a method predicting high novel weighted ensemble model. The weight values in proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO)...

Metamaterials have unique physical properties. They are made of several elements and structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials' exact structure, geometry, size, orientation, arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave...

The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation computers, especially predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance K-Nearest Neighbors (KNN) algorithm. proposed methodology initiates with establishment a particle...

Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated other systems better than ASR without diacritics. In this work, application of state-of-the-art end-to-end deep learning approaches is investigated build a robust diacritised ASR. These are based on Mel-Frequency Cepstral Coefficients and log Mel-Scale Filter Bank energies as acoustic features. To best our knowledge, approach has not been used in task recognition. fill gap, work presents new...