With the rapid increase in population, the rate of diseases like cancer is also increasing. Lung cancer is a leading cause of cancer-related deaths with a minimum survival rate; there is a need to find better, faster, and more accurate methods for early diagnosis of this disease. Although previous research in lung cancer has presented numerous prediction schemes, the feature selection utilized in the schemes and learning process has failed to enhance the accurate performance of lung cancer diagn...

Machine Learning 

This paper represents a groundbreaking advancement in Parkinson’s disease (PD) research by employing a novel machine learning framework to categorize PD into distinct subtypes and predict its progression. Utilizing a comprehensive dataset encompassing both clinical and neurological parameters, the research applies advanced supervised and unsupervised learning techniques. This innovative approach enables the identification of subtle, yet critical, patterns in PD manifestation, which traditional m...

Artificial Intelligence  Neurology  Technology  Machine Learning 

Human trafficking is the world’s most prevalent and growing crime. Law enforcement agencies have been faced with a lot of challenges worldwide in this area. Over the years different works of literature have tried to sort out methods and techniques for detecting and controlling human trafficking by making use of different types of data available. In this research, an EfficientNet model was developed to help in the detection of human trafficking cases, by classifying an image into a class of chain...

Artificial Intelligence  Computer Vision  Image Processing  Machine Learning 

The question relating to the Detection and Prediction of human behavior using Artificial Intelligence tools is the main focus of our research. More specifically, we have studied the link between emotion and human behavior in order to model an artificial intelligence that is not only capable of detecting but also, and above all, predicting human behavior. Predicting what someone is about to do next based on their body language is natural for humans, but not for computers. When we meet another per...

Artificial Intelligence  Sociology  Psychology  Machine Learning 

The main goal of this article is to give optimization methods of the algorithms of the Recommender System in calculation acceleration and accuracy based on mathematical theory. We first introduce the Collaborative Filtering Algorithm and the similarity function used in this algorithm. Both the weakness and the strength of two different mathematical distance used to describe the similarity will be illustrated detailedly in this article. And both nonparametric and parametric methods will be applie...

Machine Learning 

Globally, the advent of new cases of cancer has been steadily increasing, with rising mortality and a significant impact on the economy. Most malignancy outcomes are linked to early detection, prompt diagnosis, and treatment. The need for early detection is crucial to cancer management. With these increasing numbers, there is a need for the adoption of emerging technologies such as machine learning to help improve the outcome of cancer management. For these reasons, in this paper, we reviewed th...

Artificial Intelligence  Biological Engineering  Computational Biology  Oncology  Image Processing  Bioengineering  Machine Learning 

In recent years, there are many surrogates for tensor tubal rank. In this paper, we propose a hybrid norm consisting of the weighted nuclear norm and the weighted Frobenius norm (WTNFN) of a tensor. The WTNFN is a surrogate for tensor tubal rank, and studies the weighted tensor nuclear and Frobenius norm minimization (WTNFNM) problem. The aim is to enhance the stability of the solution and improve the shortcomings of the traditional method of minimizing approximate rank functions based on the te...

Machine Learning 

This paper mainly studies the problem of tensor robust principal component analysis (TRPCA), in order to accurately recover the low rank and sparse components from the observed signals. Most of the existing robust principal component analysis (RPCA) methods are based on nuclear norm minimizationss. These methods minimize all singular values at the same time, so they can not approach the rank well in practice. In this paper, the idea of truncated nuclear norm regularization is extended to RPCA. A...

Machine Learning 

The decision tree and neural network models are considered as one of the fastest and easy-to-use techniques having the ability to learn from classified data patterns. These models can be employed in detecting result anomalies measura- ble under normal circumstances on the bases that the student is healthy, had no problem and sat for exams. The existing techniques lack merit and integrity to efficiently detect irregularities found between student continuous assessments and exam scores. The additi...

Machine Learning