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Information on the inheritance of adaptive traits to high plant density (HD) and low N (LN) in maize is a prerequisite for breeders to develop tolerant varieties to these stresses. The objective of the presented study was to estimate additive and dominance variances, heritability, and genetic advancement of the adaptive traits of tolerance to HD and LN stresses. Eight diverse maize inbred lines tolerant to HD and LN were options for diallel crosses. Parents and F1 crosses sustained evaluation in two seasons under nine environments, viz., three plant densities (high = HD, medium = MD, and low = LD) × three N levels (high = HN, medium = MN, and low = LN). A randomized complete block design with three replications functioned in each environment. Both additive and dominance variances were significant; dominance was appreciably higher than the additive variance in most cases. The degree of dominance was more in the over-dominance range. The highest heritability, along with the highest genetic advance, succeeded from the environment stressed for both HD and LN for leaf angle, penetrated light at the bottom of the plant, and leaf area produced 1g grain, and from the environment stressed for HD for plant height and 100-kernel weight. Moreover, the results suggested that heterosis breeding is the method of choice for the genetic improvement of most studied traits. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD), classified as a developmental disability, is now more common in children than ever. A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children. Parents can seek professional help for a better prognosis of the child's therapy when ASD is diagnosed under five years. This research study aims to develop an automated tool for diagnosing autism in children. The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition, feature selection, and classification phases. The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification. The Imperialistic competitive algorithm (ICA) based on empires conquering colonies performs feature selection in this study. The performance of Logistic Regression (LR), Decision tree, K-Nearest Neighbor (KNN), and Random Forest (RF) classifiers are experimentally studied in this research work. The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset. The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection method and different classifiers. The Exploratory Data Analysis (EDA) phase has uncovered crucial facts about the data, like the correlation of the features in the dataset with the class variable. [ABSTRACT FROM AUTHOR]

Promising technologies such as sensors, networking, and edge have led to many smart healthcare solutions to monitor and track patient health status. The health sector is now experiencing a significant transformation from conventional patient care to a smart healthcare environment. Smart health care allows medical professionals to monitor patients remotely and visualize the disease prognosis effectively. The Internet of medical things connect patients, doctors, and medical equipment via wireless networking technologies to process the data with Artificial Intelligence models. One of the domains of automated health care systems is to alert the caregivers and hospital on emergency conditions. This research study is a novel work that aims to help the caregivers of somnambulism patients attend to them in case of emergency. Sleep quality improves the health and work efficiency of any person. The caregivers of sleepwalking patients suffer from lack of sleep as the patient gets active during the night hours. The model is based on fall detection and sleep detection from wearable sensor data. The fall detection model includes feature selection by LASSO and classification by ensemble classifier. The proposed methodology shows improved performance for the fall detection model for all ensemble machine learning classifiers. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disease that drastically affects brain cells. Early detection of this disease can reduce the brain cell damage rate and improve the prognosis of the patient to a great extent. The patients affected with AD tend to depend on their children and relatives for their daily chores. Objective: This research study utilizes the latest technologies of artificial intelligence and computation power to aid the medical industry. The study aims at early detection of AD to enable doctors to treat patients with the appropriate medication in the early stages of the disease condition. Methods: In this research study, convolutional neural networks, an advanced deep learning technique, are adopted to classify AD patients with their MRI images. Deep learning models with customized architecture are precise in the early detection of diseases with images retrieved by neuroimaging techniques. Results: The convolution neural network model classifies the patients as diagnosed with AD or cognitively normal. Standard metrics evaluate the model performance to compare with the state-of-the-art methodologies. The experimental study of the proposed model shows promising results with an accuracy of 97%, precision of 94%, recall rate of 94%, and f1-score of 94%. Conclusion: This study leverages powerful technologies like deep learning to aid medical practitioners in diagnosing AD. It is crucial to detect AD early to control and slow down the rate at which the disease progresses. [ABSTRACT FROM AUTHOR]

Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool. [ABSTRACT FROM AUTHOR]