scholarly journals The Automation of Critical Path Method using Machine Learning: A Conceptual Study

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Othman Aljumaili
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Critical Path ◽  
Theoretical Research ◽  
Critical Path Method ◽  
Theoretical Part ◽  
Conceptual Study ◽  
Shed Light

This research aims to shed light on the use of machine learning in improving, developing and automating the critical path method, solving its problems, studying this effect and its dimensions, and discussing that from many aspects. The research is divided into two theoretical and practical parts. The theoretical part is concerned with studying the critical path method and its advantages, problems and challenges, as well as studying machine learning and artificial intelligence and its dimensions, reviewing materials and sources related to this, and then presenting suggestions and future solutions based on this study. As for the practical section, it is a questionnaire that targeted a segment of engineers, in particular, and others who have sufficient experience in both the critical path method and machine learning, and seeking their opinions on both topics. The result of the theoretical research was 14 theories or proposals that were presented based on the foregoing study. As for the practical questionnaire, a sample of 127 was taken. Through statistical analysis, the results were analyzed and discussed separately, and then a conclusion was drawn regarding them.

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

scholarly journals Complex Scene Understanding and Recognition Based on the Explainable Machine Learning in AI Models

2021 ◽  
Author(s):  
Bin Wu ◽  
Yuhong Fan ◽  
Yeh-Cheng Chen ◽  
Tao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Network Model ◽  
Scene Understanding ◽  
Learning Model ◽  
Small Sample ◽  
Theoretical Research ◽  
Optimization Efficiency

Abstract Information fusion is an important part of numerous neural network systems and other machine learning models. However, there exist some problems about fusion in scene understanding and recognition of complex environment, such as difficulty in feature extraction, small sample size and interpretability of the model. Deep reinforcement learning can combine the perception ability of deep learning with the decision-making ability of reinforcement learning to learn control strategies directly from high-dimensional original data. However, It faces these challenges, such as low optimization efficiency, poor generality of network model, small labeled samples, explainable decisions for users without a strong background on Artificial Intelligence (AI). Therefore, at the level of application and theoretical research, this paper aims to solve the above problems,the main contributions include: (1)optimize the feature representation methods based on spatial-temporal feature of the behavior characteristics in the scene, deep metric learning between adjacent layers and cross-layer learning theory, and then propose a lightweight reinforcement learning network model to solve these problems of the complexity of the model to be explained, the difficulty of extracting feature and the difficulty of tuning parameter; (2)construct the self-paced learning strategy of the deep reinforcement learning model, introduce transfer learning mechanism in the optimization process, and solve the problem of low optimization efficiency and small labeled samples; (3)design the behavior recognition framework of the multi-perspective deep knowledge transfer learning model, construct a explainable behavior descriptor, and solve the problems of poor network generality and weak 1explanation of network. Our research is of great theoretical and practical significance in the fields of artificial intelligence and public security.

Statistical Analysis of Human Emotions to Suggest Suitable Music as per Individual's Mood

2021 ◽  
Vol 11 (3) ◽  
pp. 34-67
Author(s):  
Rohit Rastogi ◽  
Prabhat Yadav ◽  
Jayash Raj Singh Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Leisure Time ◽  
Recommendation System ◽  
Music Industry ◽  
Music Recommendation ◽  
Emotion Detection ◽  
Current Mood ◽  
Music Recommendation System

There is music recommendation software and music providers that are well explored and commonly used, but those are generally based on simple similarity calculations and manually tagged parameters. This project proposes a music recommendation system based on emotion detection of users, automatic computing, and classification. Music is recommended based on the emotion expressed and temper of the user. Like artists and genre, emotion of the user can also be a crucial recommendation point for music listeners. The different mооds in whiсh the system will сlаssify the imаges аre hаррy, neutrаl, аnd sаd. The system will рre-sоrt the songs according to their genre in the above-mentioned categories. This research project gives us advancement in the music industry with the help of machine learning and artificial intelligence and will reduce the hassle of selecting songs in our leisure time and will automatically play songs by detecting the emotion of the user. This data can be used to play the songs that match the current mood detected from the provided input by the user.

scholarly journals A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities

2021 ◽  
Vol 12 (1) ◽  
pp. 1-20
Author(s):  
Gao Niu ◽  
Richard S. Segall ◽  
Zichen Zhao ◽  
Zhijian Wu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Statistical Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Free Software ◽  
Statistical Software ◽  
Artificial Intelligence Application ◽  
Intelligence Application

This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.

scholarly journals Performance analysis of machine learning algorithms trained on biased data

2021 ◽  
Author(s):  
Renata Sendreti Broder ◽  
Lilian Berton
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Logistic Regression ◽  
Ethical Issues ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Applications ◽  
Processing Algorithms ◽  
Real Person ◽  
Shed Light

The use of Artificial Intelligence and Machine Learning algorithms in everyday life is common nowadays in several areas, bringing many possibilities and benefits to society. However, since there is room for learning algorithms to make decisions, the range of related ethical issues was also expanded. There are many complaints about Machine Learning applications that identify some kind of bias, disadvantaging or favoring some group, with the possibility of causing harm to a real person. The present work aims to shed light on the existence of biases, analyzing and comparing the behavior of different learning algorithms – namely Decision Tree, MLP, Naive Bayes, Random Forest, Logistic Regression and SVM – when being trained using biased data. We employed pre-processing algorithms for mitigating bias provided by IBM's framework AI Fairness 360.

The teaching reform of financial technology machine learning under the course group of intelligent information processing

2021 ◽  
pp. 002072092098354
Author(s):  
Han He ◽  
Dong Tian ◽  
Weiwei Liu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Processing ◽  
Science And Technology ◽  
Theoretical Research ◽  
Teaching Reform ◽  
Intelligent Information Processing ◽  
Intelligent Information ◽  
High Level ◽  
Financial Technology

Artificial intelligence is one of the most popular topics in today's era, and it is also an important development strategy of our country. In order to train high-level talents of artificial intelligence, the major of machine learning of financial science. China has gradually explored a set of relatively fixed teaching methods for the major of financial science and technology machine learning. However, in combination with the needs of the current era, industrial production puts forward higher requirements for the study of this major, It makes the traditional teaching method backward and unsuitable. In order to seek a more efficient teaching mode, it is urgent to reform the current teaching of financial technology machine learning. In this context, combined with the advanced teaching concept of intelligent information processing course group, this paper re plans the related courses of financial science and technology machine learning specialty, enhances the relevance between courses, enables the courses to connect and cooperate with each other, and forms a chain of excellent course group. strengthen the theoretical research, and strive to build a high-level teaching team to form a more three-dimensional and more close to the needs of the times. In order to investigate the rationality of the teaching reform, this paper carries on the verification analysis under the massive real data, obtains the reform method is scientific, is feasible through the analysis, and will play the positive role to the financial science and technology machine learning curriculum teaching reform under the intelligent information processing curriculum group.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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