<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides...

With the advancement in technology, machine learning can be applied to diagnose mass/tumor brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for early diagnosis of tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers isolated convolutional-neural-network (CNN) models are built from scratch check performances MRI images. Then, 22-layer, binary-classification (tumor or no tumor) isolated-CNN...

A compact tree shape planar quad element Multiple Input Output (MIMO) antenna bearing a wide bandwidth for 5G communication operating in the millimeter-wave spectrum is proposed. The radiating of proposed design contains four different arcs to achieve response. Each backed by 1.57 mm thicker Rogers-5880 substrate material, having loss tangent and relative dielectric constant 0.0009 2.2, respectively. measured impedance MIMO system based on 10 dB criterion from 23 GHz 40 with port isolation...

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides...

Significant yield challenges are posed by biotic stress on coffee leaves, which has a negative effect the revenue generation of this highly utilized commodity. Numerous studies have proposed techniques for early detection and classification in leaves. In study, we propose technique called extracted feature ensemble (EFE) classifying healthy infected classes. Transfer learning-based convolutional neural networks (CNNs) custom-designed features used to improve performance. Under concept EFE,...

Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to the effective classification under different conditions and experimental settings. In this study, we proposed a weighted voting ensemble (WVE) three low-computation custom-designed convolutional neural networks (CNNs) classify at 48KHz. Some recent have...

Bearing faults are critical in machinery; their early detection is vital to prevent costly breakdowns and ensure operational safety. This study presents a pioneering take on addressing the challenges of imbalanced datasets bearing fault diagnosis. By mitigating issues such as mode collapse vanishing gradients, our novel method employs Conditional Generative Adversarial Networks (CGANs) with spectral normalization adaptive adversarial noise injection generate high-quality samples. enhances...

Neurological and brain-related cancers are one of the main causes death worldwide. A commonly used tool in diagnosing these conditions is Magnetic Resonance Imaging (MRI), yet manual evaluation MRI images by medical experts presents difficulties due to time constraints variability. This research introduces a novel, two-module computerized method aimed at increasing speed accuracy brain tumor detection. The first module, termed Image Enhancement Technique, utilizes trio machine learning...

Abstract Nowadays detection of deterioration electrical motors is an important topic research. Vibration signals often carry diagnostic information a motor. The authors proposed setup for the analysis vibration three-phase induction motors. In this paper rotor fault techniques motor (TPIM) were presented. presented used and signal processing methods. analyzed recognition rate readings 3 states TPIM: healthy TPIM, TPIM with 1 broken bar, 2 bars. described method feature extraction Method...

Physical activity is essential for physical and mental health, its absence highly associated with severe health conditions disorders. Therefore, tracking activities of daily living can help promote quality life. Wearable sensors in this regard provide a reliable economical means such activities, are readily available smartphones watches. This study the first kind to develop wearable sensor-based classification system using special class supervised machine learning approaches called boosting...

Increasing waste generation has become a significant issue over the globe due to rapid increase in urbanization and industrialization. In literature, many issues that have direct impact on of improper disposal been investigated. Most existing work literature focused providing cost-efficient solution for monitoring garbage collection system using Internet Things (IoT). Though an IoT-based provides real-time system, it is limited control spreading overspill bad odor blowout gasses. The poor...

Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability free parking spaces major reasons for traffic jams. Congestion are interrelated because searching a spot creates additional delays increase local circulation. In center large cities, 10% circulation due to cruising, drivers nearly spend 20 min space. Therefore, necessary develop space availability prediction system that can inform in advance about...

COVID-19 syndrome has extensively escalated worldwide with the induction of year 2020 and resulted in illness millions people. patients bear an elevated risk once symptoms deteriorate. Hence, early recognition diseased can facilitate intervention avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) X-ray imaging, predict onset suffering from COVID-19. To be precise, subjects were classified into 3 categories namely...

Forecasting the electricity load provides its future trends, consumption patterns and usage. There is no proper strategy to monitor energy generation; high variation among them. Many strategies are used overcome this problem. The correct selection of parameter values a classifier still an issue. Therefore, optimization algorithm applied with deep learning machine techniques select optimized for classifier’s hyperparameters. In paper, novel learning-based method implemented forecasting. A...

The Coronavirus disease 2019 (COVID-19) is an infectious spreading rapidly and uncontrollably throughout the world. critical challenge rapid detection of infected people. available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking swabs lab testing complex, intrusive, require many resources. Furthermore, lack test kits to meet exceeding cases also a major limitation. current develop some technology non-intrusively detect...

The conventionally synthesized nano-ferrite materials do not possess bulk properties, generally required for their use in mainstream industry. To make ferrite nanoparticles clinically applicable materials, it is important to have good control over morphology and optical properties of these materials. In this study, low-pressure microwave plasma was used tailor the structural surface chemistry manganese nanoparticles. A facile sol-gel method prepare cubic spinal structures These were exposed...

Information security depends on Network Intrusion Detection (NID), which properly identifies network threats. This work explores simulating a NID system by stacking ensemble classifiers with various feature selection methods. We used the NSL-KDD dataset for investigation. The binary classification performance integrates all assaults into one class and treats another target variable as usual. Random Forest (RF), Bagging Classifier (BC), Extra Tree Ensemble (ETE) using Logistics Regression...

Nowadays, brain tumors have become a leading cause of mortality worldwide. The cells in the tumor grow abnormally and badly affect surrounding cells. These could be either cancerous or non-cancerous types, their symptoms can vary depending on location, size, type. Due to its complex varying structure, detecting classifying accurately at initial stages avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based CNN with ResNet50 U-Net solve this problem....

AISI 316L stainless steel (SS) is one of the extensively used biomaterials to produce implants and medical devices. It provides a low-cost solution with ample mechanical properties, corrosion resistance, biocompatibility compared its counterpart materials. However, made this material are subjected short life span in human physiological conditions leading leaching metal ions, thus limiting use as biomaterial. In research, addition boron, titanium, niobium varying concentrations SS matrix has...

This study investigated the production of Cu2+-doped CoFe2O4 nanoparticles (CFO NPs) using a facile sol-gel technique. The impact Cu2+ doping on lattice parameters, morphology, optical properties, and electrical properties CFO NPs was for applications in devices. XRD analysis revealed formation spinel-phased crystalline structures specimens with no impurity phases. average grain size, constant, cell volume, porosity were measured range 4.55-7.07 nm, 8.1770-8.1097 Å, 546.7414-533.3525 Å3,...