Deep learning is a branch of artificial intelligence that simulates how the human brain solves complex problems.

Neural networks with multiple operators are used in deep learning to break the problem into smaller parts, and each is solved individually.

The marginal variables are those variables that will be retained in the subset of variables. To find these concepts, sum The values in a table arranged in rows and columns, and write the sum in the table's margins.

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease that most commonly affects people over 65. Neuronal loss is the primary cause of the primary motor symptoms of Parkinson's disease.

The presence of motor and non-motor symptoms significantly …

Hessian matrices are square matrices of second-order partial derivatives of functions or fields with scalar values. With many variables, it describes the local curvature of the function.

Ludwig Otto Hesse developed the Hessian matrix in the 19th century and …

Parkinson's disease (PD) is a complex neurodegenerative disorder that causes motor and non-motor symptoms.

Five to ten percent of cases are genetic, with mutations identified in several genes, including leucine-rich repeat kinase 2 (LRRK2), glucocerebrosidase (GBA), etc. The penetrance …

The term "Big Data" has recently gained popularity to describe large volumes of data that cannot be stored or processed by conventional means.

The vast amounts of human and machine-generated data are so complex and expansive that they cannot …

This platform section is suitable for ranking and identifying the skills of different users. Everyone who works on the platform can get exclusive badges related to their skills or abilities. These badges help users understand experts better and have the right mindset …

Parkinson's disease (PD) can be further subdivided into subtypes since homogeneous groups are more likely to share genetic and pathological features, enabling earlier disease detection and more targeted treatment.

Our goal is to identify PD subtypes using advanced hybrid …

In image classification, pixels or vectors are categorized and labeled according to specific rules. A categorization law can be devised based on spectral characteristics or textural characteristics.

In general, there are two types of classification: supervised and unsupervised.

Organizations and industries can outsource many projects or engage in team building to do it. TECVICO provides a platform where companies can define, plan and carry out their projects. On this platform, companies can choose the experts they want and communicate with …

Virtual events are held in the web space and can have different forms. Virtual events include workshops, classes, webinars, and conferences. TECVICO has provided a suitable platform for presenting and holding virtual events, which professionals can use as training workshops or other …

Recurrent neural networks are also known as RNNs. This artificial neural network can process sequential data, recognize patterns, and predict outcomes.

Recurrent neural networks can perform the same task or operation repeatedly based on a sequence of inputs. RNNs …

Previous studies have shown a correlation between diffusion tensor imaging (DTI) features and motor outcomes in Parkinson’s disease (PD) patients. Our study investigates the effect of DTI features on outcome prediction using hybrid machine learning systems (HMLSs).

Methods

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TECVICO has provided a platform where all students can improve their skills, work in a real team and prepare themselves for industrial and big projects.

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A machine learning hyperparameter is a parameter whose values are selected before the training begins. Hyperparameters should not be confused with parameters.

Machine learning uses the label parameter to …

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The stochastic model describes a series of possible events in which each event's probability depends only on the previous event's state.

A Markov chain, named after the Russian mathematician Andrey Markov, is a mathematical system in which …

A human approach to solving a new problem involves:

• Making inferences beyond the information available.
• Reusing previous experiences.
• Weighing alternatives in the face of uncertainty.

It is a machine learning method for regression and classification problems. The gradient boosting model is a linear combination of weak models periodically built to create a robust final model.

The convolutional neural network (CNN, or ConvNet) is an artificial neural network (ANN) class commonly used to analyze visual imagery in deep learning. CNNs, which provide translation-equivariant responses, are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN). …

Machine learning allows machines to learn automatically from data, improve performance from past experiences, and predict the future as a subset of AI.

Algorithms process large amounts of data in machine learning. The algorithms are …

Rather than being a futuristic technology, artificial intelligence is increasingly becoming part of our daily lives. AI displaces people by automating menial tasks and shifting employment to more productive activities.

Companies that realized AI's potential early on had achieved remarkable progress. With AI, businesses become more efficient within a short period.

Comparing the trend of AI in businesses with …

An artificial intelligence subset known as deep learning utilizes machines instead of humans. First, we must say that the human brain comprises nerve fibers that …

Machine learning is a relatively old artificial intelligence field undergoing growth and development. Machine learning is a very active research field in computer science. Machine learning is related to a machine model designed …

Often, head and neck cancer mean cancers that also affect the mouth, throat, and nose. Cancers in this area usually begin their growth and development from the cells covering the outer and moist part of the head and …

Not long ago, artificial intelligence (AI) seemed like a science fiction prediction of the future of technology. Today, artificial intelligence is like the driving engine of technological advancements that people use daily. Image recognition is one of …

Head and neck cancer is one of the most common cancers worldwide (fifth leading incidence). Radiotherapy and cetuximab are considered the standard treatment.

However, locoregional defects are a significant challenge in improving and controlling the condition and …

The goal of this study is to summarize the seven most frequent types of categorization algorithms.

Data can be classified in two ways: structured and unstructured. Classification is a technique for categorizing data into a set of categories. The main goal of a classification …

Machine learning algorithms are widely used for various tasks such as classification and regression. For training a machine learning model, you need to feed it with accurate data to improve the model's performance.

Deep Learning is a subset of machine learning which has shown huge potential on various tasks such as computer vision, classification, regression etc.

Deep Learning models’ computation ability mostly comes from its architecture and large amount of data they …

Artificial intelligence (AI) in the aerospace industry can help companies streamline manufacturing while also addressing safety concerns. Further, AI systems can analyze inputs from various assets and process vast amounts of data faster than humans.

Something wonderful is happening. Nowadays finding the right and powerful algorithms for machine learning and deep learning tasks are easy. Open-source resources are increasing and universities, R&D labs, and other centers creating new machine learning and deep learning algorithms with clear and …

AI and machine learning permeate every aspect of modern life, from email spam filtering and e-commerce to financial security and medical diagnostics.

Deep learning has been one …

All deep learning applications and related Machine Learning (ML) models, clinical data, and image analysis may have the most significant potential for creating a positive, long-lasting impact on human lives in a reasonable …

Everybody is talking about machine learning. Because it promises vast benefits that could impact every aspect of human life. Efforts have been made to develop machine learning to a stage where there will be no human intervention necessary. This section …

Evolutionary Algorithms (EAs) are general-purpose search procedures based on the mechanisms of natural selection and population genetics. These algorithms are increasingly used in applications as diverse as architectural design, engineering, factory job scheduling, electronic circuit design, signal processing, network configuration, robotic control, …

If you are familiar with deep neural networks and how they work, and a little bit of history behind it, you have heard how deep neural networks are inspired by real brain neurons and the reason behind the name of these networks.

Before the rise of "deep learning" in 2013, feature-based ML was the dominating method in respective domains. Classic classifiers like LDA, QDA, and a k-nearest neighbor classifier (k-NN) were employed for classification before 1980, even when the phrase "machine learning" did not …

Today, when it comes to image data, ML algorithms can interpret images the same way our brains do. These are used almost everywhere, right from face recognition while capturing images on our smartphones, automating tedious manual work, self-driving cars, and everything in …

Image enhancement is essential in the field of image processing since this improves image quality by highlighting useful information and reducing redundant information in the image.

Image enhancement is among the most essential technologies in the world of image …

Computer Vision is a subfield of Deep Learning and Artificial Intelligence where humans teach computers to see and interpret the world around them. Machine vision is the use of a camera or multiple cameras to inspect and analyze objects automatically, usually in …

Agriculture benefits from AI, particularly, with the advancements in deep learning (DL) concepts, significant improvements have been observed in agricultural.

The ability of automatic feature extraction creates an adaptive nature in deep learning (DL).  Convolutional neural networks which …

Classification is a comprehensive perspective that can be adopted in contexts. You may encounter classification methods in machine learning: learn to assign labels of predefined categories to the datasets and incoming data. As an easy-to-understand example, you can consider a task including …

Nowadays, Artificial Intelligence overtakes other technologies in terms of the capacity of research and investment. But why? Simple but necessary, we need to know what AI methods are doing in problems. This will bring a state of perception and let people trust …

An autoencoder is a type of neural network that transfers input value to output value, as shown in figure (1). Since the autoencoder model does not need a target value (Y), we can call it unsupervised learning.

Natural language processing (NLP) is a branch of artificial intelligence that allows computers to understand text and spoken words in the same way humans can. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the …

Data mining is the process of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Data mining combines statistics and artificial intelligence to analyze large data sets to discover useful information. Data mining uses sophisticated mathematical algorithms to …

Artificial neural networks (ANNs) are a subset of machine learning at the core of deep learning algorithms.

An artificial neuron network (neural network) is a computational model that mimics how nerve cells work in the human brain …

Image processing (IP) is a way to convert an image to a digital aspect and perform certain functions to get an enhanced image or extract other helpful information from it. The image processing system usually treats all images as 2D signals …

Deep Learning can be considered as a subset of machine learning (ML). It is a field based on learning and improving on its own by examining computer algorithms. While machine learning uses more straightforward concepts, deep learning works with artificial neural networks …

Machine learning (ML) is an exciting branch of Artificial Intelligence (AI), and it’s all around us. ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. You train a model …

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. In the simplest terms, Artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks …