Potential role of machine learning techniques for modeling the hardness of OPH steels

2020 ◽  
pp. 101806
Author(s):  
Omid Khalaj ◽  
Moslem Ghobadi ◽  
Alireza Zarezadeh ◽  
Ehsan Saebnoori ◽  
Hana Jirková ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Role ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine Learning Techniques for Underwater Wireless Sensor Networks

2021 ◽  
pp. 194-211
Author(s):  
Deepti Rani ◽  
Anju Sangwan ◽  
Anupma Sangwan ◽  
Tajinder Singh
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Seeking ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques ◽  
Primary Focus

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

scholarly journals The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Bader Alouffi ◽  
Radhya Sahal ◽  
Naglaa Abdelhade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Multimodal Data ◽  
Learning Techniques ◽  
Early Progression ◽  
Neuroimaging Data

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

2021 ◽  
Vol 12 (2) ◽  
pp. 510-516
Author(s):  
Anshul, Et. al.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Severe Acute Respiratory Syndrome ◽  
Impact Analysis ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
The World ◽  
Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

scholarly journals Basic polar and hydrophobic properties are the main characteristics that affect the binding of transcription factors to methylation sites

2020 ◽  
Vol 36 (15) ◽  
pp. 4263-4268
Author(s):  
Zijie Shen ◽  
Quan Zou
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Transcription Factors ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Analysis Process ◽  
Learning Techniques ◽  
Key Factor ◽  
Learning Analysis

Abstract Motivation Methylation and transcription factors (TFs) are part of the mechanisms regulating gene expression. However, the numerous mechanisms regulating the interactions between methylation and TFs remain unknown. We employ machine-learning techniques to discover the characteristics of TFs that bind to methylation sites. Results The classical machine-learning analysis process focuses on improving the performance of the analysis method. Conversely, we focus on the functional properties of the TF sequences. We obtain the principal properties of TFs, namely, the basic polar and hydrophobic Ile amino acids affecting the interaction between TFs and methylated DNA. The recall of the positive instances is 0.878 when their basic polar value is >0.1743. Both basic polar and hydrophobic Ile amino acids distinguish 74% of TFs bound to methylation sites. Therefore, we infer that basic polar amino acids affect the interactions of TFs with methylation sites. Based on our results, the role of the hydrophobic Ile residue is consistent with that described in previous studies, and the basic polar amino acids may also be a key factor modulating the interactions between TFs and methylation. Supplementary information Supplementary data are available at Bioinformatics online.

From information- to knowledge-management: the role of rule induction and neural net machine learning techniques in knowledge generation

1989 ◽  
Vol 15 (4-5) ◽  
pp. 299-304 ◽  
Author(s):  
Nigel Ford
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Rule Induction ◽  
Machine Learning Techniques ◽  
Knowledge Generation ◽  
Neural Net ◽  
Knowledge Resources ◽  
Learning Techniques ◽  
Knowledge Manager

Developments in artificial intelligence mean that it is now increasingly possible to store not only information but also knowledge as an exploitable resource. Insofar as he or she is concerned with creating, organizing and monitoring knowledge resources to support effective decision making within an organization, the information manager is developing the role of knowledge manager. As well as its organization and dissemina tion, the generation of storable knowledge is very much on the agenda of the knowledge manager. The extent to which com puters can help in the process of knowledge generation is central to his or her concerns. Machine learning techniques have been developed which are capable of giving us an increasing amount of help in this process. The contributions of rule induction and artificial neural net systems are discussed. It is likely that such tech niques will prove to be useful tools both for the information/knowledge manager requiring practical working systems enabling the cost-effective exploitation of knowledge resources, and for the information/knowledge scientist requir ing advances in our more fundamental theoretical knowledge of the nature of information and ways of processing it.

scholarly journals Role of Machine Learning in Manufacturing Sector

2019 ◽  
Vol 8 (4) ◽  
pp. 2320-2328
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Relationship Of ◽  
The Impact ◽  
Maintenance Process

This paper discusses the basic concept Machine learning and its techniques, algorithms as well the impact of Machine Learning in Manufacturing processes and Industrial Production. There has been an unprecedented increase in the data available in the last couple of decades. This has enabled machine learning to be applied in various fields. Machine learning is field of study which enables the computer system to learn automatically as well as improve from experience and perform various tasks without explicit instructions. The primary aim of machine learning is to allow the computers learn automatically without human assistance or intervention and adjust actions accordingly It is being employed in many fields. Manufacturing one area where machine learning is very useful. This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among others. This can be done by employing various machine learning techniques and algorithms using concepts such as deep learning, neural networks, supervised, unsupervised and reinforcement learning. The relationship of how machines and humans can co-exist and work together to improve the efficiency of production is also discussed. Industry 4.0 or fourth revolution that has occurred in manufacturing which deals with advent of automation in manufacturing industry and its significance is discussed. We take a look at the various benefits and applications of machine learning in the field of manufacturing engineering. This paper also discusses the various challenges and future scope of employing machine learning in the manufacturing

scholarly journals Role of Machine Learning & Artificial Intelligence Techniques in Software Testing

2021 ◽  
Vol 12 (6) ◽  
pp. 2913-2921
Author(s):  
Nilofar Mulla, Dr. Naveenkumar Jayakumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Machine Learning Techniques ◽  
Business Growth ◽  
Growth Machine ◽  
Learning Techniques ◽  
Test Suites ◽  
Applications Of Ai

This study provides information about the use of artificial intelligence (AI) and machine learning (ML) techniques in the field of software testing. The use of AI in software testing is still in its initial stages. Also the automation level is lesser compared to more evolved areas of work.AI and ML can be used to help reduce tediousness and automate tasks in software testing. Testing can be made more efficient and smarter with the help of AI. Researchers recognize potential of AI to bridge the gap between human and machine driven testing capabilities. There are still number of challenges to fully utilize AI and ML techniques in testing but it will definitely enhance the entire testing process and skills of testers and will contribute in business growth. Machine learning research is a subset of overall AI research. The life-cycle of software is increasingly shortening and becoming more complicated. There is a struggle in software development between the competing pressures of developing software and meeting deadlines. AI-powered automated testing makes conducting full test suites in a timely manner on every change. In this article a detailed overview about the various applications of AI in software testing have been demonstrated. Also the implementation of machine learning in software testing has been discussed in detail and use of different machine learning techniques has been explained as well.

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