Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system.

As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies done in this field, but very few discuss implementing smart technologies to present a combined sustainable farming system. In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture.

Demoralization in Parkinson disease.

Objective: To determine the prevalence and associated features of demoralization in Parkinson disease (PD).

Methods: Participants with PD and controls were prospectively recruited from outpatient movement disorder clinics and the community. Demoralization was defined as scoring positively on the Diagnostic Criteria for Psychosomatic Research, Demoralization questionnaire or Kissane Demoralization Scale score ≥24.

Iron, dopamine, genetics, and hormones in the pathophysiology of restless legs syndrome.

Restless legs syndrome (RLS) is a common, chronic neurologic condition, which causes a persistent urge to move the legs in the evening that interferes with sleep. Human and animal studies have been used to study the pathophysiologic state of RLS and much has been learned about the iron and dopamine systems in relation to RLS. Human neuropathologic and imaging studies have consistently shown decreased iron in different brain regions including substantia nigra and thalamus.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework.

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results.

BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines.