Strain engineering of Zeeman and Rashba effects in transition metal dichalcogenide nanotubes and their Janus variants: anstudy.

We study the influence of mechanical deformations on the Zeeman and Rashba effects in transition metal dichalcogenide nanotubes and their Janus variants from first principles. In particular, we perform symmetry-adapted density functional theory simulations with spin-orbit coupling to determine the variation in the electronic band structure splittings with axial and torsional deformations. We find significant effects in molybdenum and tungsten nanotubes, for which the Zeeman splitting decreases with increase in strain, going to zero for large enough tensile/shear strains, while the Rashba splitting coefficient increases linearly with shear strain, while being zero for all tensile strains, a consequence of the inversion symmetry remaining unbroken.

Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning.

Pesticides are chemicals used to eradicate pests. Not only are they used for plant protection and livestock in agriculture, but they are also used in public areas to kill mosquitoes, cockroaches, and other pests. Approximately 95% of the pesticides produced are only used in agriculture for crop protection.

A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits.

Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers.

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification.

Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast.

Personality Prediction with Hybrid Genetic Programming using Portable EEG Device.

This work suggests a method to identify personality traits regarding the targeted film clips in real-time. Such film clips elicit feelings in people while capturing their brain impulses using the electroencephalogram (EEG) devices and examining personality traits. The Myers-Briggs Type Indicator (MBTI) paradigm for determining personality is employed in this study.

Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition is becoming one of the essential research areas among various world researchers. Treatment that is efficacious and diagnosed early for negative emotions is the only way to save people from mental health problems.

Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building.

Buildings are considered to be one of the world's largest consumers of energy. The productive utilization of energy will spare the accessible energy assets for the following ages. In this paper, we analyze and predict the domestic electric power consumption of a single residential building, implementing deep learning approach (LSTM and CNN).

Clinical risk assessment of chronic kidney disease patients using genetic programming.

Chronic kidney disease (CKD) is one of the serious health concerns in the twenty-first century. CKD impacts over 37 million Americans. By applying machine learning (ML) techniques to clinical data, CKD can be diagnosed early.

Torsional strain engineering of transition metal dichalcogenide nanotubes: anstudy.

We study the effect of torsional deformations on the electronic properties of single-walled transition metal dichalcogenide (TMD) nanotubes. In particular, considering forty-five select armchair and zigzag TMD nanotubes, we perform symmetry-adapted Kohn-Sham density functional theory calculations to determine the variation in bandgap and effective mass of charge carriers with twist. We find that metallic nanotubes remain so even after deformation, whereas semiconducting nanotubes experience a decrease in bandgap with twist-originally direct bandgaps become indirect-resulting in semiconductor to metal transitions.

Torsional moduli of transition metal dichalcogenide nanotubes from first principles.

We calculate the torsional moduli of single-walled transition metal dichalcogenide (TMD) nanotubes usingdensity functional theory (DFT). Specifically, considering forty-five select TMD nanotubes, we perform symmetry-adapted DFT calculations to calculate the torsional moduli for the armchair and zigzag variants of these materials in the low-twist regime and at practically relevant diameters. We find that the torsional moduli follow the trend: MS> MSe> MTe.

A novel fitness function in genetic programming for medical data classification.

In the last decade, machine learning (ML) techniques have been widely applied to identify different diseases. This facilitates an early diagnosis and increases the chance of survival. The majority of medical data-sets are unbalanced.

A novel genetic programming approach for epileptic seizure detection.

The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy.