Domain adaptive learning for multi realm sentiment classification on big data.

Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data.

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors.

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible.

Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine.

Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquiring iris cellular images that suffer from the off-angle iris, noise, and specular reflection.

Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT.

Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management.

Classification of Highly Divergent Viruses from DNA/RNA Sequence Using Transformer-Based Models.

Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences.

An Intelligent Multi-Floor Navigational System Based on Speech, Facial Recognition and Voice Broadcasting Using Internet of Things.

Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system.

Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System.

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast.

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance.

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing.

Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world.

Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers.

Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results.

Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning.

The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses.

Optimizing the transfer-learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images.

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images.

Internet of medical things embedding deep learning with data augmentation for mammogram density classification.

Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices.

Brain tumor detection and multi-classification using advanced deep learning techniques.

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors.

A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques.

Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area.