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...

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...

With the help of machine learning, many problems that have plagued mammography in past been solved. Effective prediction models need normal and tumor samples. For medical applications such as breast cancer diagnosis framework, it is difficult to gather labeled training data construct effective learning frameworks. Transfer an emerging strategy has recently used tackle scarcity by transferring pre-trained convolutional network knowledge into domain. Despite well reputation transfer based on...

This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. In light of increasing global challenges related to food security sustainable agriculture, this research focuses on developing highly efficient accurate automated system identifying diseases, thereby contributing enhanced crop protection yield optimization. The proposed...

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands die brain tumors. Brain-related diagnoses require caution, and even smallest error in diagnosis have negative repercussions. Medical errors tumor common frequently result higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for evaluation detection. However, MRI generates large amounts data, making manual segmentation difficult laborious work,...

It is an undeniable fact that people excessively rely on social media for effective communication. However, there no appropriate barrier as to who becomes a part of the Therefore, unknown ruin fundamental purpose communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, impact society also from primarily being positive negative. Cyber aggression negative impact; it defined willful use information technology harm, threaten, slander, defame, or harass...

The rapid population growth results in a crucial problem the early detection of diseases medical research. Among all cancers unveiled, breast cancer is considered second most severe cancer. Consequently, an exponential rising death cases incurred by expected due to and lack resources required for performing diagnoses. Utilizing recent advances machine learning could help staff diagnosing as they offer effective, reliable, responses, which decreasing risk. In this paper, we propose new...

Circular-transmit/linear-receive compact polarimetry synthetic aperture radar systems combine coherent dual polarization with wide-swath imaging. The information in these data may be represented as a Stokes vector, or one can reconstruct several quadpolarized covariance elements. Two reconstruction algorithms have been published the literature: by Souyris and refined algorithm Nord We investigated application of two for reconstructing ocean clutter purpose detecting targets. tested...

Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence clear pathological symptoms DM hinders timely diagnosis and treatment such a critical condition. Early feasible through eye screening technologies. However, manual inspection retinography images by specialists time-consuming routine. Therefore, many deep learning-based computer-aided systems have been recently developed for automatic prognosis retinal images. Manual...

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in variety remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep (DL) subfield ML uses models with complex structures to solve prediction problems higher performance than traditional ML. In this study, framework based on DL was developed for SMC retrieval. For purpose, sample dataset built, which included synthetic aperture radar...

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development computer-aided diagnosis (CAD) systems for classifying brain in magnetic resonance imaging (MRI) has been subject many research papers so far. However, this sector is still its early stage. The ultimate goal to develop a lightweight effective implementation U-Net deep network use performing exact real-time segmentation. Moreover, simplified convolutional neural (DCNN) architecture BT classification...

Plant diseases annually cause damage and loss of much the crop, if not its complete destruction, this constitutes a significant challenge for farm owners, governments, consumers alike. Therefore, identifying classifying at an early stage is very important in order to sustain local global food security. In research, we designed new method identify plant by combining transfer learning Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted extracting...

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis ALL indispensable for effectual remediation this disease. Initial screening conducted through manual examination stained smear microscopic images, process which time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established automatically diagnose ALL. This paper...

Text classification is a common task in natural language processing (NLP), where the objective to assign predefined categories or labels given text. Detecting sarcasm and classifying sentiment dialect NLP has practical applications, including spam detection, topic classification, analysis. However, sentimental expressions, such as irony, humor, criticism, can be difficult identify through traditional methods due their implicit nature. To address this, we propose Modified Switch Transformer...

Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis DME necessary for effective treatment disease. Visual detection retinal screening images by ophthalmologists a time-consuming process. Recently, many computer-aided systems have been developed to assist doctors detecting automatically. In this paper, new deep feature transfer-based stacked autoencoder neural network system proposed automatic fundus images. The integrates...

In recent times, there has been considerable focus on harnessing artificial intelligence (AI) for medical image analysis and healthcare purposes. this study, we introduce CADFU (Computer-Aided Diagnosis System Foot Ulcers), a pioneering diabetic foot ulcer diagnosis system. The primary objective of is to detect segment ulcers similar chronic wounds in images. To achieve this, employ two distinct algorithms. Firstly, DHuNeT, an innovative Dual-Phase Hyperactive UNet, utilized the segmentation...

Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful achieving high levels accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances data lack relevance certain features. study aims to tackle these challenges proposing an advanced machine learning (ML) based that minimizes...

In this paper, wide band transmitting and receiving antennas; each composed of a bowtie partially covered by an open conducting box; are proposed for ground-penetrating-radar (GPR) system.The inner walls the box lossy coating which is number layers with conductivity profile designed to achieve better characteristics antenna.The Finite-Difference Time-Domain (FDTD) method applied simulate radiating antennas, buried target wave propagation in ground soil over frequency operation.The...

The financial markets have been influenced by the emerging spread of Coronavirus disease, COVID-19. oil, and gold as well experienced a downward trend due to increased rate in number confirmed COVID-19 cases. Lately, published COVID data comprised new variables that may influence accuracy oil/gold prices forecasting models including Stringency index, Reproduction rate, Positive Rate, Vaccinations. In this study, Deep Autoencoders are introduced combined with well-known approach: Pearson...

A realistic model of ground soil is developed for the electromagnetic simulation Ground Penetrating Radar (GPR) systems. three dimensional Finite Difierence Time Domain (FDTD) algorithm formulated to dispersive media using N-term Debye permittivity function with static conductivity. The formulation based on concept Piecewise Linear Recursive Convolution (PLRC) in order simulate dispersion properties as a two-term medium. This approach modeling enhances accuracy and reliability results...

Achieving accurate energy consumption prediction can be challenging, particularly in residential buildings, which experience highly variable behavior due to changes occupants and the construction of new buildings. This variability, combined with potential for privacy breaches through conventional data collection methods, underscores need novel approaches forecasting. The proposed study suggests a approach predict consumption, utilizing Federated Learning (FL) train global model while...