Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and one of the top 10 leading causes death. Accurate early detection TB very important, otherwise, it could be life-threatening. In this work, we have detected reliably from chest X-ray images using image pre-processing, data augmentation, segmentation, deep-learning classification techniques. Several public databases were used create database 3500 infected normal for study. Nine different deep CNNs (ResNet18,...

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection using computer vision artificial intelligence (AI) help reduce adverse effects also overcome shortcomings human In this work, we propose use deep learning architecture based on recent convolutional neural network called EfficientNet 18,161 plain segmented...

Providing clean water to a rapidly growing population is an issue that currently getting lots of attention offer sustainable solution for scarcity. Membrane distillation (MD) one the latest technologies provides great potential in treatment. Even though there tremendous amount research done during past two decades on membrane distillation, long-term use this process still restricted by fouling. Fouling can be defined as accumulation various materials pores or surface affect permeate's...

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of patients. In this study, a cascaded system proposed segment lung, detect, localize, and quantify infections from computed tomography images. An extensive set experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, Feature Pyramid Network (FPN), with different backbone (encoder) structures variants DenseNet ResNet. The conducted for lung region segmentation showed Dice...

Cardiovascular diseases are one of the most severe causes mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring blood pressure seems to be viable option, but this demands an invasive process, introducing several layers complexities and reliability concerns due non-invasive techniques not being accurate. This motivates us develop method estimate continuous arterial (ABP) waveform through approach using Photoplethysmogram (PPG) signals. We explore advantage deep...

Cardiovascular diseases are the most common causes of death around world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters required. Several invasive non-invasive methods have been developed for this purpose. Most existing used in hospitals BP invasive. On contrary, cuff-based methods, which can predict systolic (SBP) diastolic (DBP), cannot be monitoring. studies attempted to from non-invasively collectible signals such...

Computerized brain tumor classification from the reconstructed microwave (RMB) images is important for examination and observation of development disease. In this paper, an eight-layered lightweight classifier model called image network (MBINet) using a self-organized operational neural (Self-ONN) proposed to classify into six classes. Initially, experimental antenna sensor-based imaging (SMBI) system was implemented, RMB were collected create dataset. It consists total 1320 images: 300...

The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, surgical conditions. RR SpO2 are used to assess the effectiveness lung medications ventilator support. In recent studies, use a photoplethysmogram (PPG) has been recommended evaluating SpO2. This research presents novel method estimating using machine learning models that incorporate PPG signal features. A number established methods extract meaningful features from...

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry have reduced crop and yields in sericulture, which produces 90% of the world’s raw silk. Manual leaf disease identification is tedious error-prone. Computer vision can categorize diseases early overcome challenges manual identification. No deep learning (DL) models been reported. Therefore, this study, two types diseases: rust spot, with disease-free leaves, were collected from regions...

The rising prevalence of gastrointestinal (GI) tract disorders worldwide highlights the urgent need for precise diagnosis, as these diseases greatly affect human life and contribute to high mortality rates. Fast identification, accurate classification, efficient treatment approaches are essential addressing this critical health issue. Common side effects include abdominal pain, bloating, discomfort, which can be chronic debilitating. Nausea vomiting also frequent, leading difficulties in...

Brain tumors present a significant global health challenge, and their early detection accurate classification are crucial for effective treatment strategies. This study presents novel approach combining lightweight parallel depthwise separable convolutional neural network (PDSCNN) hybrid ridge regression extreme learning machine (RRELM) accurately classifying four types of brain (glioma, meningioma, no tumor, pituitary) based on MRI images. The proposed enhances the visibility clarity tumor...

Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) been suggested to enhance the crystallization rate relative crystallinity of pure PLLA polymers. Despite large amount experimental research that has taken place date, theoretical aspects not comprehensively investigated. Therefore, this uses machine learning methods estimate biodegradable PLLA/PGA (polyglycolide)...

Diabetes mellitus (DM) is one of the most prevalent diseases in world, and correlated to a high index mortality. One its major complications diabetic foot, leading plantar ulcers, amputation, death. Several studies report that thermogram helps detect changes temperature which may lead higher risk ulceration. However, patients, distribution does not follow standard pattern, thereby making it difficult quantify changes. The abnormal infrared (IR) foot images can be used for early detection...

An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from comfort of their home, which can help capture problems earliest stages. Constant monitoring for complications is essential to avoid potentially devastating outcomes common diseases such as diabetes mellitus. Inspired by those goals, authors this work propose a full design wearable that detect both plantar using off-the-shelf sensors. The provides details specific sensors, circuit...

The fouling factor (Rf) is an operating index for measuring undesirable effect of solids' deposition on the heat transfer ability exchangers. Accurate prediction helps appropriate scheduling cleaning cycles. Since diverse factors affect this feature, it sometimes hard to estimate accurately using simple empirical or traditional intelligent methods. Therefore, study employs four up-to-date machine-learning algorithms (Gaussian Process Regression, Decision Trees, Bagged Support Vector...

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown detect changes in temperature distribution associated with a higher risk of ulceration. Machine learning approaches applied such may utility the early diagnosis diabetic complications. In this work, publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on parameter-the thermal...

Every one of us has a unique manner communicating to explore the world, and such communication helps interpret life. Sign language is popular for hearing speech-disabled people. When sign user interacts with non-sign user, it becomes difficult signer express themselves another person. A recognition system can help user. This study presents that capable recognizing Arabic Language from recorded RGB videos. To achieve this, two datasets were considered, as (1) raw dataset (2) face–hand...

Detecting colorectal polyps promptly and accurately is crucial in preventing the progression of cancer. These cause severe conditions colon or rectum, presenting a significant diagnostic challenge. Traditional manual detection through medical imaging not only bulky prone to errors but also incurs substantial costs, requiring expert endoscopist. Inefficient treatment can lead critical health complications. Addressing these issues, we extensively employed various configurations...

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying categorizing these is often time-consuming prone errors. This research addresses this issue by employing convolutional neural network support vector machine (CNN-SVM) hybrid model classify in four economically important crops: strawberries, peaches, cherries, soybeans. objective categorize 10 classes diseases, with six diseased healthy classes, for...

This work presents a multi-course project-based learning (MPL) approach implemented using two electrical engineering (EE) interdisciplinary undergraduate courses at Qatar University. Implementing an MPL helps in the development of critical thinking and collaborative decision-making skills. The attainment these skills is also outcome education for sustainable (ESD); help students acquire knowledge, attitudes, values necessary to shape future. participating students’ worked on design project,...