Journal of physics 

About: Journal of physics is an academic journal published by IOP Publishing. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 1742-6588. It is also open access. Over the lifetime, 9821 publications have been published receiving 2668 citations.

Machine Learning Approaches in Stock Price Prediction: A Systematic Review

Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. With the emergence of Artificial Intelligence, various algorithms have been employed in order to predict the equity market movement. The combined application of statistics and machine learning algorithms have been designed either for predicting the opening price of the stock the very next day or understanding the long term market in the future. This paper explores the different techniques that are used in the prediction of share prices from traditional machine learning and deep learning methods to neural networks and graph-based approaches. It draws a detailed analysis of the techniques employed in predicting the stock prices as well as explores the challenges entailed along with the future scope of work in the domain.

Medical diagnosis of COVID-19 using blood tests and machine learning

Abstract: Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2), colloquially known as Coronavirus surfaced in late 2019 and is an extremely dangerous disease. RT-PCR (Reverse transcription Polymerase Chain Reaction) tests are extensively used in COVID-19 diagnosis. However, they are prone to a lot of false negatives and erroneous results. Hence, alternate methods are being researched and discovered for the detection of this infectious disease. We diagnose and forecast COVID-19 with the help of routine blood tests and Artificial Intelligence in this paper. The COVID-19 patient dataset was obtained from Israelita Albert Einstein Hospital, Brazil. Logistic regression, random forest, k nearest neighbours and Xgboost were the classifiers used for prediction. Since the dataset was extremely unbalanced, a technique called SMOTE was used to perform oversampling. Random forest obtained optimal results with an accuracy of 92%. The most important parameters according to the study were leukocytes, eosinophils, platelets and monocytes. This preliminary COVID-19 detection can be utilised in conjunction with RT-PCR testing to improve sensitivity, as well as in further pandemic outbreaks.

Building and training a fuzzy neural model of data mining tasks

Abstract: In this paper discussed the problem of constructing and training a fuzzy neural network based on fuzzy logical rules. Based on the constructed model, the developed algorithm, the objects classified with indistinct initial information. Under these conditions, traditional methods of mathematical statistics or simulation modeling do not allow building adequate models for solving data mining problems. We consider the solution of problems based on the use of intelligent modeling technologies using methods of neural networks and fuzzy logic in order to automate the construction of fuzzy rules based on methods and algorithms of machine learning.

milliAmpere: An Autonomous Ferry Prototype

Abstract: In this paper, we summarize the experiences with the autonomous passenger ferry development prototype milliAmpere, which has been used as a test platform in several research projects at the Norwegian University of Science and Technology (NTNU) since 2017. New algorithms for motion planning, motion control, collision avoidance, docking, multi-target tracking and localization have been developed and verified in full-scale experiments with milliAmpere. The infrastructure surrounding milliAmpere includes several sensor rigs supporting research on multi-sensor fusion and situational awareness, and a shore control lab which can be used to study the interaction between human operators and the autonomous ferry. Building upon the experiences with milliAmpere, the full-scale autonomous ferry milliAmpere2 was recently launched.

Marine ship detection and classification based on YOLOv5 model

Abstract: An improved deep learning neural model YOLOv5-DN based on YOLOv5 is proposed for marine ship detection and classification in the area of harbours and heavy traffic waterways. The CSP-DarkNet module in YOLOv5 is replaced by CSP-DenseNet to promote the accuracy of target detection and classification in the proposed model. Sample marine ships in the data set are divided into six classes: ore carriers, general cargo ships, bulk cargo ships, container ships, passenger ships, and fishing ships to meet the detection needs in the areas of ports and waterways. The data set are grouped into a training set, testing set, and validating set by the proportion of 6:2:2. Experiments show that the improved model has better average accuracy, from 62.2% to 71.6%.