With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care reducing healthcare costs. The Internet of Things (IoT) recently drawn much interest as a potential remedy. IoT-based systems can gather analyze wide range physiological data, including blood oxygen levels, heart rates, body temperatures, ECG signals, then provide real-time feedback medical professionals so they may take appropriate action. This paper proposes...

Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and Internet, sentiment has become increasingly important various fields, such as marketing, politics, customer service. However, becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose ensemble model...

Smart farming is a hot research area for experts globally to fulfill the soaring demand food. Automated approaches, based on convolutional neural networks (CNN), crop disease identification, weed classification, and monitoring have substantially helped increase yields. Plant diseases pests are posing significant danger health of plants, thus causing reduction in production. The cotton crop, major cash Asian African countries affected by different types weeds leading reduced yield. Weeds...

ABSTRACT Due to the complex structure of brain, variations in tumor shapes and sizes, resemblance between healthy tissues, reliable efficient identification brain tumors through magnetic resonance imaging (MRI) presents a persistent challenge. Given that manual is often time‐consuming prone errors, there clear need for advanced automated procedures enhance detection accuracy efficiency. Our study addresses difficulty by creating an improved convolutional neural network (CNN) framework...

Abstract Thyroid disease has been on the rise during past few years. Owing to its importance in metabolism, early detection of thyroid is a task critical importance. Despite several existing works detection, problem class imbalance not investigated very well. In addition, studies predominantly focus binary-class problem. This study aims solve these issues by proposed approach where ten types diseases are considered. The uses differential evolution (DE)-based optimization algorithm fine-tune...

Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing depend on sensors to monitor occupancy spaces, which entail high installation maintenance costs limited functionality tracking vehicle movement within car park. To address these challenges, we propose multistage learning-based approach that leverages surveillance cameras park self-collected dataset Saudi license plates. The combines YOLOv5 for plate detection, YOLOv8...

Cyber attacks are growing with the rapid development and wide use of internet technology. Botnet attack emerged as one most harmful attacks. identification is becoming challenging due to numerous vectors ongoing evolution viruses. As Internet Things (IoT) technology developing rapidly, many network devices have been subject botnet leading substantial losses in different sectors. Botnets pose serious risks security deep learning models shown potential for efficiently identifying activity from...

In the rapidly evolving realm of cloud computing security, this paper introduces an innovative solution to address persistent challenges. The proliferation technology has brought forth heightened concerns regarding data necessitating novel approaches safeguarding sensitive information. issue centers on vulnerability cloud-stored data, often enhanced encryption and key management strategies. Traditional methods fall short in mitigating risks associated with compromised keys centralized...

Deep Learning and computer vision have become potent agricultural technologies in recent years. These are essential for identifying hazardous plant leaf diseases, which significantly impact crop quality productivity. The precise distinction between healthy damaged palm leaves is at the core of this research. Our study marks a significant improvement area by introducing novel method disease using hybrid model. model's central component combines Efficient Channel Attention Network (ECA-Net)...

Cauliflower cultivation plays a pivotal role in the Indian Subcontinent’s winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, susceptibility of cauliflower crops various diseases poses threat productivity quality. This paper presents novel machine vision approach employing modified YOLOv8 model called Cauli-Det for automatic classification localization diseases. The proposed system utilizes images captured through smartphones...

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there still room improvement to develop better more accurate systems using behavioral features such as mouth eye movement. This study proposes a neural network architecture employing...

Breast Cancer (BC) is among women's most lethal health concerns. Early diagnosis can alleviate the mortality rate by helping patients make efficient treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become one subtype of BC. According to College American Pathologists/American Society Clinical Oncology (CAP/ASCO), severity level HER2 expression be classified between 0 and 3+ range. detected effectively from immunohistochemical (IHC) and, hematoxylin & eosin (HE) images...

Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques identify and monitor contrails address challenges associated with climate change. We propose SegX-Net architecture, a highly efficient lightweight model combines DeepLabV3+, upgraded, ResNet-101 architectures achieve superior accuracy. evaluated performance our on comprehensive dataset...

Abstract Glaucoma is an eye disease that damages the optic nerve as a result of vision loss, it leading cause blindness worldwide. Due to time‐consuming, inaccurate, and manual nature traditional methods, automation in glaucoma detection important. This paper proposes explainable artificial intelligence (XAI) based model for automatic using pre‐trained convolutional neural networks (PCNNs) machine learning classifiers (MLCs). PCNNs are used feature extractors obtain deep features can capture...

The guava plant is widely cultivated in various regions of the Sub-Continent and Asian countries, including Bangladesh, due to its adaptability different soil conditions climate environments. fruit plays a crucial role providing food security nutrition for human body. However, plants are susceptible infectious leaf diseases, leading significant crop losses. To address this issue, several heavyweight deep learning models have been developed precision agriculture. This research proposes...

Date palm species classification is important for various agricultural and economic purposes, but it challenging to perform based on images of date palms alone. Existing methods rely fruit characteristics, which may not be always visible or present. In this study, we introduce a new dataset model image-based classification.

In the wind turbine remote fault diagnosis, sensor data is susceptible to low-quality phenomena such as missing and damaged due communication delays, environmental noise, faults. These issues decrease accuracy of diagnostic models, necessitating a solution enhance model robustness under non-ideal conditions. Hence, robust scheme based on adaptive noise filtering useful feature-domain enhancement (UFDE) proposed in this paper improve stability performance. An interference identification...

In the healthcare domain, essential task is to understand and classify diseases affecting vocal folds (VFs). The accurate identification of VF disease key issue in this domain. Integrating segmentation classification into a single system challenging but important for precise diagnostics. Our study addresses challenge by combining illness categorization integrated system. We utilized two effective ensemble machine learning methods: EfficientNetV2L-LGBM UNet-BiGRU. model classification,...

Leaf diseases are a global threat to crop production and food preservation. Detecting these is crucial for effective management. We introduce LeafDoc-Net, robust, lightweight transfer-learning architecture accurately detecting leaf across multiple plant species, even with limited image data. Our approach concatenates two pre-trained classification deep learning-based models, DenseNet121 MobileNetV2. enhance an attention-based transition mechanism average pooling layers, while MobileNetV2...

Network intrusion detection in the Internet of Things (IoT) framework has presented considerable challenges recent decades. A wide variety machine-learning approaches are introduced network detection. The existing methodologies commonly lack consistency achieving optimal performance across various multi-class categorization tasks. present study elucidates implementing a unique system with primary objective enriching efficacy In initial phase, it is imperative to employ data-denoising...

Understanding the nuanced emotions and points of view included in user-generated content remains challenging, even though text data analysis for mental health is a crucial instrument assessing emotional well-being. Most current models neglect significance integrating viewpoints comprehending favor single-task learning. To offer more thorough knowledge health, this study, we present an Opinion-Enhanced Hybrid BERT Model (Opinion-BERT), built to handle multi-task learning simultaneous...