What makes a leader

Machine learning is as growing as fast as concepts such as Big data and the field of data science in general. The purpose of the systematic review was to analyze scholarly articles that were published between 2015 and 2018 addressing or implementing supervised and unsupervised machine learning techniques in different problem-solving paradigms. Using the elements of PRISMA, the review process identified 84 scholarly articles that had been published in different journals. Of the 84 articles, 6 were published before 2015 despite their metadata indicating that they were published in 2015. The existence of the six articles in the final papers was attributed to errors in indexing. Nonetheless, from the reviewed papers, decision tree, support vector machine, and Naive Bayes algorithms appeared to be the most cited, discussed, and implemented supervised learners. Conversely, k-means, hierarchical clustering, and principal component analysis also emerged as the commonly used unsupervised learners. The review also revealed other commonly used algorithms that include ensembles and reinforce learners, and future systematic reviews can focus on them because of the developments that machine learning and data science is undergoing at the moment.

A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science

Alternaria leaf blotch, and rust are two common types of apple leaf diseases that severely affect apple yield. A timely and effective detection of apple leaf diseases is crucial for ensuring the healthy development of the apple industry. In general, these diseases are inspected by experienced experts one by one. This is a time-consuming task with unstable precision. Therefore, in this paper, we proposed a LOW-COST, STABLE, HIGH precision apple leaf diseases identification method. This is achieved by employing MobileNet model. Firstly, comparing with general deep learning model, it is a LOW-COST model because it can be easily deployed on mobile devices. Secondly, instead of experienced experts, everyone can finish the apple leaf diseases inspection STABLELY by the help of our algorithm. Thirdly, the precision of MobileNet is nearly the same with existing complicated deep learning models. Finally, in order to demonstrated the effectiveness of our proposed method, several experiments have been carried out for apple leaf diseases identification. We have compared the efficiency and precision with the famous CNN models: i.e. ResNet152 and InceptionV3. Here, the apple disease datasets (including classes: Alternaria leaf blotch and rust leaf) were collected by the agriculture experts in Shaanxi Province, China.

MobileNet Based Apple Leaf Diseases Identification

In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Among the agricultural products, tomato is one of the most used crops. Thus, preventing significant loss in quantity and yield of tomato is majorly dependent on recognition and classification of diseases a tomato plant might possess. Latest and fostering technologies like Image processing is used to rectify such issues using different types of techniques and algorithms. Initially, the leaves of a tomato plant get affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended with the treatment. Mostly live plants are adversely affected by the diseases. This paper imparts representation of leaf disease detection employing image processing that can identify drawbacks in tomato plant from images, based on color, bound and texture to give the brisk and reliable results to the farmer.

https://iopscience.iop.org/article/10.1088/1742-6596/1712/1/012012/pdf

Plant Leaf Disease Classification and Detection System Using Machine Learning

Investigating the effect of quality of grammar and mechanics (QGAM) in online reviews: The mediating role of reviewer crediblity

Music Genre classification is very important in today's world due to rapid growth in music tracks, both online and offline In order to have better access to these we need to index them accordingly Automatic music genre classification is important to obtain music from a large collection Most of the current music genre classification techniques uses machine learning techniques In this paper, we present a music dataset which includes ten different genres A Deep Learning approach is used in order to train and classify the system Here convolution neural network is used for training and classification Feature Extraction is the most crucial task for audio analysis Mel Frequency Cepstral Coefficient (MFCC) is used as a feature vector for sound sample The proposed system classifies music into various genres by extracting the feature vector Our results show that the accuracy level of our system is around 76% and it will greatly improve and facilitate automatic classification of music genres

Automatic Music Genre Classification using Convolution Neural Network

This paper traces the evolution of emotional intelligence as a theory and goes on to give a literature review of the same. It discusses the different concepts and beliefs pertaining to emotion and cognition and how it culminated in the theory of emotional intelligence. It also discusses the three major models of emotional intelligence, their contribution to the theory and finally closes with a brief discussion on future improvement of the theory.

http://www.ijoar.org/journals/IJOARHS/papers/IJOARHS23.pdf

A literature review of emotional intelligence

In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

https://iopscience.iop.org/article/10.1088/1742-6596/1000/1/012100/pdf

A Data mining Technique for Analyzing and Predicting the success of Movie

Machine learning is a kind of data analysis technique which provides a flexible way of learning information about the data, so that necessary action can be predicted accurately. Machine learning techniques provide the way of analyzing and predicting the valuable information from the available data, so that further actions can be carried out accurately. There is several kind of machine learning approaches are available based on their behavior and working procedure. In this analysis work different kind of methodologies are discussed which are used to learn the knowledge about the program. The various machine learning approaches differs in their working procedure and inputs and output processed by them. Various applications adapted the machine learning approaches for learning the information which are discussed in detail in this paper. The analysis work provides the detailed view of working procedure of different research works implemented by various authors. It also gives overview of merits and demerits of different machine learning techniques that are proposed earlier. The main goal of this analysis work is to identify the better machine learning approach which can lead to accurate learning with less false positive rate. The analysis of the work concluded with the performance results of different approaches that carried down and shows better approach which can lead to more accurate learning of health care data.

K. Meenakshi

Papers

Automatic Music Genre Classification using Convolution Neural Network

A literature review of emotional intelligence

Plant Leaf Disease Classification and Detection System Using Machine Learning

A Data mining Technique for Analyzing and Predicting the success of Movie

A Novel Study of Machine Learning Algorithms for Classifying Health Care Data