Design synthesis knowledge and inductive machine learning

2001 ◽  
Vol 15 (3) ◽  
pp. 233-249 ◽  
Author(s):  
S. POTTER ◽  
M.J. DARLINGTON ◽  
S.J. CULLEY ◽  
P.K. CHAWDHRY
Keyword(s):  
Machine Learning ◽  
Conceptual Design ◽  
Design Process ◽  
Learning Algorithm ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Design Synthesis ◽  
Conceptual Design Phase ◽  
Learning Techniques ◽  
Inductive Machine Learning

A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

2020 ◽  
pp. 1314-1330 ◽  
Author(s):  
Mohamed Elhadi Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Texture Feature ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Nearest Neighbour ◽  
Curve Shape ◽  
Distance Curve ◽  
Shape Signature ◽  
Learning Techniques

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.

Early Stage Detection of Heart Failure Using Machine Learning Techniques

2021 ◽  
pp. 75-88
Author(s):  
Zulfikar Alom ◽  
Mohammad Abdul Azim ◽  
Zeyar Aung ◽  
Matloob Khushi ◽  
Josip Car ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Learning Techniques

Predictive Analysis of Diabetes Mellitus Using Machine Learning Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3449-3452
Author(s):  
M. S. Roobini ◽  
Y. Sai Satwick ◽  
A. Anil Kumar Reddy ◽  
M. Lakshmi ◽  
D. Deepa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
The Body ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Pima Indians ◽  
Classification Techniques ◽  
Learning Techniques ◽  
Prediction Of Diabetes

In today’s world diabetes is the major health challenges in India. It is a group of a syndrome that results in too much sugar in the blood. It is a protracted condition that affects the way the body mechanizes the blood sugar. Prevention and prediction of diabetes mellitus is increasingly gaining interest in medical sciences. The aim is how to predict at an early stage of diabetes using different machine learning techniques. In this paper basically, we use well-known classification that are Decision tree, K-Nearest Neighbors, Support Vector Machine, and Random forest. These classification techniques used with Pima Indians diabetes dataset. Therefore, we predict diabetes at different stage and analyze the performance of different classification techniques. We Also proposed a conceptual model for the prediction of diabetes mellitus using different machine learning techniques. In this paper we also compare the accuracy of the different machine learning techniques to finding the diabetes mellitus at early stage.

In-game behaviour analysis of football players using machine learning techniques based on player statistics

2020 ◽  
pp. 174795412095976
Author(s):  
Abraham García-Aliaga ◽  
Moisés Marquina ◽  
Javier Coterón ◽  
Asier Rodríguez-González ◽  
Sergio Luengo-Sánchez
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Football Players ◽  
Machine Learning Algorithm ◽  
Reduction Techniques ◽  
Learning Techniques ◽  
Dimensionality Reduction Techniques ◽  
Player Positions

The purpose of this research was to determine the on-field playing positions of a group of football players based on their technical-tactical behaviour using machine learning algorithms. Each player was characterized according to a set of 52 non-spatiotemporal descriptors including offensive, defensive and build-up variables that were computed from OPTA’s on-ball event records of the matches for 18 national leagues between the 2012 and 2019 seasons. To test whether positions could be identified from the statistical performance of the players, the dimensionality reduction techniques were used. To better understand the differences between the player positions, the most discriminatory variables for each group were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. From the combination of both techniques, we obtained useful conclusions to enhance the performance of players and to identify positions on the field. The study demonstrates the suitability and potential of artificial intelligence to characterize players' positions according to their technical-tactical behaviour, providing valuable information to the professionals of this sport.

Detection and prediction of pluvial flood using machine learning techniques

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
K.A. Oladapo ◽  
F.Y. Ayankoya ◽  
F.A. Adekunle ◽  
S.A. Idowu
Keyword(s):  
Machine Learning ◽  
Flood Risk ◽  
Scientific Method ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Machine Learning Techniques ◽  
Emergency Situations ◽  
Learning Techniques ◽  
Proposed Model ◽  
Flood Detection

The periodical occurrence of emergency situations represents an important issue for mankind. Over the years, the world at large has experienced multiple misadventures both natural and man-made. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. Handling flood risk with the intention of safety and comfort of the citizens as well as saving their environment is one of the major responsibilities of the leadership in each country especially in flood prone areas. Machine learning predictive analytic applications can improve the risk management. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification to appraise the performance of the machine learning algorithm on pluvial flood conditioning variables.

Cataloguing of Attention Deficit Hyperactivity Disorder (ADHD) among Children

2021 ◽  
Author(s):  
Sumathi M ◽  
Dr. G S Mamatha ◽  
Dr. Ramaa A
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Medical Field ◽  
Hyperactivity Disorder ◽  
Learning Techniques ◽  
Chronic Disorder ◽  
Adhd Children ◽  
Anxiety Depression ◽  
Treatment Rules

<p>Children are the dream of parents. Children ADHD is a bygone and chronic disorder which leads to problems in children. If not solved in childhood stages will continue in future till adolescents. The disorder consequences are difficulty to study the tasks which are related to anxiety, depression and other psychological problems. Hence the disorder must be resolved in the early stage to control any type of consequences in future for our children. The medical field is an eminent area in today’s world such as signal processing, Imaging, MRI, EEG etc. to diagnose and offer treatment. Even technology field too contributing to ADHD children by providing different techniques in different areas such as IoT, mobile, Robot, Application, virtual reality, augmented reality, machine learning techniques etc. to give diagnosis and treatment methods. The paper reviews and summarizes the set of features, diagnosis methods, treatment rules for ADHD children.</p>

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