Cannabis-Induced Gastrointestinal Tract Symptoms in the Adult Population: A Systematic Review

Cannabinoid usage is widespread in the self-management of various medical ailments. However, adverse effects have been reported with use, especially pertaining to the gastrointestinal system in adults and aged patients. These range from nausea, vomiting, bloating, or abdominal pain. This systematic review of previously reported cannabis-induced gastrointestinal symptoms in the adult population from the literature provides an analysis of relevant data to enhance knowledge and awareness of this topic

Sentiment analysis using fuzzy logic: A comprehensive literature review

Understanding and comprehending humans' views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e-commerce platforms has led to an abundance of the real-time and free forms of opinions floating on social media platforms. This real-world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text-based SA, audio-based SA, and fusion of text-audio features-based SA. This article discusses the various novel ways of classifying fuzzy logic-based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance. Finally, the article outlines a taxonomy for sentiment classification based on the technique-supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy-based SA approaches into five different classes vis-a-vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy-rule based SA, (d) Neuro-fuzzy network-based SA, and (e) Fuzzy Emotion Recognition

A fuzzy rule-based system with decision tree for breast cancer detection

Breast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human-understandable terms-linguistic variables. The Mamdani Fuzzy Rule-Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels- benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state-of-the-art dataset