Hybrid Recommender System for Mental Illness Detection in Social Media Using Deep Learning Techniques.

Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person's mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database's frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure's efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques.

Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm.

The digestive system is one of the essential systems in human physiology where the stomach has a significant part to play with its accessories like the esophagus, duodenum, small intestines, and large intestinal tract. Many individuals across the globe suffer from gastric dysrhythmia in combination with dyspepsia (improper digestion), unexplained nausea (feeling), vomiting, abdominal discomfort, ulcer of the stomach, and gastroesophageal reflux illnesses. Some of the techniques used to identify anomalies include clinical analysis, endoscopy, electrogastrogram, and imaging. Electrogastrogram is the registration of electrical impulses that pass through the stomach muscles and regulate the contraction of the muscle. The electrode senses the electrical impulses from the stomach muscles, and the electrogastrogram is recorded. A computer analyzes the captured electrogastrogram (EGG) signals. The usual electric rhythm produces an enhanced current in the typical stomach muscle after a meal. Postmeal electrical rhythm is abnormal in those with stomach muscles or nerve anomalies. This study considers EGG of ordinary individuals, bradycardia, dyspepsia, nausea, tachycardia, ulcer, and vomiting for analysis. Data are collected in collaboration with the doctor for preprandial and postprandial conditions for people with diseases and everyday individuals. In CWT with a genetic algorithm, db4 is utilized to obtain an EGG signal wave pattern in a 3D plot using MATLAB. The figure shows that the existence of the peak reflects the EGG signal cycle. The number of present peaks categorizes EGG. Adaptive Resonance Classifier Network (ARCN) is utilized to identify EGG signals as normal or abnormal subjects, depending on the parameter of alertness (µ). This study may be used as a medical tool to diagnose digestive system problems before proposing invasive treatments. Accuracy of the proposed work comes up with 95.45%, and sensitivity and specificity range is added as 92.45% and 87.12%.