Facial Expression Recognition for Healthcare Monitoring Systems Using Neural Random Forest.

Facial expressions vary with different health conditions, making a facial expression recognition (FER) system valuable within a healthcare framework. Achieving accurate recognition of facial expressions is a considerable challenge due to the difficulty in capturing subtle features. This research introduced an ensemble neural random forest method that utilizes convolutional neural network (CNN) architecture for feature extraction and optimized random forest for classification.

Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.

The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset.

Development of a Smart Signalization for Emergency Vehicles.

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos.

FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis.

The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes.

An Enhanced Machine Learning Approach for Brain MRI Classification.

Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset.

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today's era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset.

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image.

Image denoising methods are important in order to diminish various kinds of noises, which are presented either capturing the image or distorted during image transmission. Signal-to-noise ratio (SNR) is one of the main barriers which avoids the theoretical observations to be accomplished in practice. In this study, we have utilized various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain.

Edge Detection-Based Feature Extraction for the Systems of Activity Recognition.

Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators.

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare.

Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement.

Boosting Breast Cancer Detection Using Convolutional Neural Network.

Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs).

Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques.

Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of heart vessels and then classified.

Protein-Protein Interaction Analysis through Network Topology (Oral Cancer).

Oral cancer is a complex disorder. Its creation and spreading are due to the interaction of several proteins and genes in different biological thoroughfares. To study biological pathways, many high-yield methods have been used.

Traffic accident detection and condition analysis based on social networking data.

Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions.

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields.

In healthcare, the analysis of patients' activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method.

A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition.

Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to another.

Mining human behavior for health promotion.

The monitoring of human lifestyles has gained much attention in the recent years. This work presents a novel approach to combine multiple context-awareness technologies for the automatic analysis of people's conduct in a comprehensive and holistic manner. Activity recognition, emotion recognition, location detection, and social analysis techniques are integrated with ontological mechanisms as part of a framework to identify human behavior.

Multimodal hybrid reasoning methodology for personalized wellbeing services.

A wellness system provides wellbeing recommendations to support experts in promoting a healthier lifestyle and inducing individuals to adopt healthy habits. Adopting physical activity effectively promotes a healthier lifestyle. A physical activity recommendation system assists users to adopt daily routines to form a best practice of life by involving themselves in healthy physical activities.

MRPack: Multi-Algorithm Execution Using Compute-Intensive Approach in MapReduce.

Large quantities of data have been generated from multiple sources at exponential rates in the last few years. These data are generated at high velocity as real time and streaming data in variety of formats. These characteristics give rise to challenges in its modeling, computation, and processing.

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body's resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient's data.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset.

Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields.

This paper introduces an accurate and robust facial expression recognition (FER) system. For feature extraction, the proposed FER system employs stepwise linear discriminant analysis (SWLDA). SWLDA focuses on selecting the localized features from the expression frames using the partial F-test values, thereby reducing the within class variance and increasing the low between variance among different expression classes.

Video-based human activity recognition using multilevel wavelet decomposition and stepwise linear discriminant analysis.

Video-based human activity recognition (HAR) means the analysis of motions and behaviors of human from the low level sensors. Over the last decade, automatic HAR is an exigent research area and is considered a significant concern in the field of computer vision and pattern recognition. In this paper, we have presented a robust and an accurate activity recognition system called WS-HAR that consists of wavelet transform coupled with stepwise linear discriminant analysis (SWLDA) followed by hidden Markov model (HMM).

Hierarchical recognition scheme for human facial expression recognition systems.

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy.

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e.