scholarly journals THE STUDY USING ENSEMBLE LEARNING FOR RECOMMENDING BETTER FUTURE INVESTMENTS

2020 ◽  
Vol 11 (06) ◽  
pp. 23-32
Author(s):  
Kajal Bholashankar Jaiswal ◽  
◽  
Dr. Harshali Patil
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Size Structure ◽  
Support Vector ◽  
Single Family ◽  
Exact Procedure ◽  
Nearest Neighbours ◽  
Type Size ◽  
The Cost ◽  
Planning Methodology

Generally, House estimation record addresses the summarized esteem changes of private housing. While at a single-family house cost desire, it needs more exact procedure reliant on the spot, house type, size, structure year, close by improvements, and some various parts which could impact house demand and deftly. With limited dataset and data incorporates, a sensible and composite data pre-taking care of, creative component planning methodology is assessed in this paper. People are careful when they are endeavouring to buy another house with their money related plans and market strategies. The objective of the paper is to measure the sensible house costs for non-house holders reliant on their financial courses of action and their desires. By analysing the earlier item, entry ranges and besides alerts enhancements, guessed costs will be evaluated. The paper includes expectations utilizing diverse Regression procedures like Ridge, LASSO, Random Forest, SVM (support-vector machine), KNN (k-nearest neighbours), Ada Boost Regression, Stacking (decision tree, lasso and random forest), Decision Tree. House estimation figure on an instructive file has been done by using all the recently referenced systems to find the best among them. The reason of this paper is to help the vendor with assessing the selling cost of a house perfectly and to assist people with foreseeing the time slap to store up a house. A part of the related segments that influence the cost were furthermore taken into examinations, for instance, states of being, thought, area and territory, etc.

scholarly journals Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

2019 ◽  
Vol 11 (11) ◽  
pp. 3222 ◽  
Author(s):  
Pascal Schirmer ◽  
Iosif Mporas
Keyword(s):  
Random Forest ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Nearest Neighbours ◽  
Energy Disaggregation ◽  
Vector Machines ◽  
Non Linear ◽  
Load Monitoring ◽  
Sinusoidal Current

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.

scholarly journals Machine Learning Framework to Predict Chronic Kidney Disease using Ensemble Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 1-6
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Performance Metrics ◽  
Weighted Average ◽  
Gradient Boosting ◽  
Support Vector ◽  
The Individual

Chronic Kidney Disease (CKD) is a worldwide concern that influences roughly 10% of the grown-up population on the world. For most of the people the early diagnosis of CKD is often not possible. Therefore, the utilization of present-day Computer aided supported strategies is important to help the conventional CKD finding framework to be progressively effective and precise. In this project, six modern machine learning techniques namely Multilayer Perceptron Neural Network, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Logistic regression were used and then to enhance the performance of the model Ensemble Algorithms such as ADABoost, Gradient Boosting, Random Forest, Majority Voting, Bagging and Weighted Average were used on the Chronic Kidney Disease dataset from the UCI Repository. The model was tuned finely to get the best hyper parameters to train the model. The performance metrics used to evaluate the model was measured using Accuracy, Precision, Recall, F1-score, Mathew`s Correlation Coefficient and ROC-AUC curve. The experiment was first performed on the individual classifiers and then on the Ensemble classifiers. The ensemble classifier like Random Forest and ADABoost performed better with 100% Accuracy, Precision and Recall when compared to the individual classifiers with 99.16% accuracy, 98.8% Precision and 100% Recall obtained from Decision Tree Algorithm

scholarly journals Predição de sinistros agrícolas: uma abordagem comparativa utilizando aprendizagem de máquina

2020 ◽  
Vol 24 (4) ◽  
pp. 533-554
Author(s):  
Arthur Lula Mota ◽  
Daniel Lima Miquelluti ◽  
Vitor Augusto Ozaki
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Support Vector ◽  
Nearest Neighbours

O seguro agrícola tem ganho maior atenção no Brasil desde o início da década passada, com a implementação do Programa de Subvenção ao Prêmio do Seguro Rural. O presente estudo testou o desempenho de algoritmos de Machine Learning para as seguradoras anteciparem a ocorrência de sinistro, elaborando previsões por meio de dados de apólices e bases de dados climáticas entre os anos de 2006 e 2017. Foram testados os algoritmos Random Forest, Support Vector Machine e k-Nearest Neighbours. O segundo mostrou melhor performance preditiva de sinistros. No entanto, todos os métodos apresentaram baixa capacidade preditiva para a ocorrência de sinistros.

scholarly journals Detecting Fake News Tweets from Twitter

2021 ◽  
Vol 23 (08) ◽  
pp. 532-537
Author(s):  
Cherlakola Abhinav Reddy ◽  
◽  
Sai Nitesh Gadiraju ◽  
Dr. Samala Nagaraj ◽  
◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Online Media ◽  
Fake News ◽  
Breaking News ◽  
Learning Techniques ◽  
Vector Machines

Online media has progressively obtained integral to the route billions of individuals experience news and occasions, frequently bypassing writers—the conventional guardians of breaking news. Occasions,in reality, make a relating spike of posts (tweets) on Twitter. This projects a great deal of significance on the validity of data found via online media stages like Twitter. We have utilized different managed learning techniques like Naïve Bayes, Decision Trees, and Support Vector Machines on the information to separate tweets among genuine and counterfeit news. For our AI models, we have utilized tweet and client highlights as our indicators. We accomplished a precision of 88% utilizing the Random Forest classifier and 88% utilizing the Decision tree. Notwithstanding, we accept that breaking down client records would build the accuracy of our models.

scholarly journals IDENTIFIKASI JENIS IKAN MENGGUNAKAN MODEL HYBRID DEEP LEARNING DAN ALGORITMA KLASIFIKASI

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
Author(s):  
Anifuddin Azis
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Deep Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Output

Indonesia merupakan negara dengan keanekaragaman hayati terbesar kedua di dunia setelah Brazil. Indonesia memiliki sekitar 25.000 spesies tumbuhan dan 400.000 jenis hewan dan ikan. Diperkirakan 8.500 spesies ikan hidup di perairan Indonesia atau merupakan 45% dari jumlah spesies yang ada di dunia, dengan sekitar 7.000an adalah spesies ikan laut. Untuk menentukan berapa jumlah spesies tersebut dibutuhkan suatu keahlian di bidang taksonomi. Dalam pelaksanaannya mengidentifikasi suatu jenis ikan bukanlah hal yang mudah karena memerlukan suatu metode dan peralatan tertentu, juga pustaka mengenai taksonomi. Pemrosesan video atau citra pada data ekosistem perairan yang dilakukan secara otomatis mulai dikembangkan. Dalam pengembangannya, proses deteksi dan identifikasi spesies ikan menjadi suatu tantangan dibandingkan dengan deteksi dan identifikasi pada objek yang lain. Metode deep learning yang berhasil dalam melakukan klasifikasi objek pada citra mampu untuk menganalisa data secara langsung tanpa adanya ekstraksi fitur pada data secara khusus. Sistem tersebut memiliki parameter atau bobot yang berfungsi sebagai ektraksi fitur maupun sebagai pengklasifikasi. Data yang diproses menghasilkan output yang diharapkan semirip mungkin dengan data output yang sesungguhnya.  CNN merupakan arsitektur deep learning yang mampu mereduksi dimensi pada data tanpa menghilangkan ciri atau fitur pada data tersebut. Pada penelitian ini akan dikembangkan model hybrid CNN (Convolutional Neural Networks) untuk mengekstraksi fitur dan beberapa algoritma klasifikasi untuk mengidentifikasi spesies ikan. Algoritma klasifikasi yang digunakan pada penelitian ini adalah : Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN),  Random Forest, Backpropagation.

scholarly journals Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment

2021 ◽  
Vol 17 (9) ◽  
pp. e1009336
Author(s):  
Sepideh Mazrouee ◽  
Susan J. Little ◽  
Joel O. Wertheim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Hiv Transmission ◽  
Genetic Data ◽  
Network Reconstruction ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Transmission Network ◽  
Viral Sequences

HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.

scholarly journals Modelos de machine learning para predição do sucesso de startups

2021 ◽  
Vol 12 (2) ◽  
pp. 28-55
Author(s):  
Fabiano Rodrigues ◽  
Francisco Aparecido Rodrigues ◽  
Thelma Valéria Rocha Rodrigues
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Initial Public Offering ◽  
Gradient Boosting ◽  
Support Vector ◽  
Trade Offs ◽  
Extreme Gradient Boosting ◽  
Public Offering

Este estudo analisa resultados obtidos com modelos de machine learning para predição do sucesso de startups. Como proxy de sucesso considera-se a perspectiva do investidor, na qual a aquisição da startup ou realização de IPO (Initial Public Offering) são formas de recuperação do investimento. A revisão da literatura aborda startups e veículos de financiamento, estudos anteriores sobre predição do sucesso de startups via modelos de machine learning, e trade-offs entre técnicas de machine learning. Na parte empírica, foi realizada uma pesquisa quantitativa baseada em dados secundários oriundos da plataforma americana Crunchbase, com startups de 171 países. O design de pesquisa estabeleceu como filtro startups fundadas entre junho/2010 e junho/2015, e uma janela de predição entre junho/2015 e junho/2020 para prever o sucesso das startups. A amostra utilizada, após etapa de pré-processamento dos dados, foi de 18.571 startups. Foram utilizados seis modelos de classificação binária para a predição: Regressão Logística, Decision Tree, Random Forest, Extreme Gradiente Boosting, Support Vector Machine e Rede Neural. Ao final, os modelos Random Forest e Extreme Gradient Boosting apresentaram os melhores desempenhos na tarefa de classificação. Este artigo, envolvendo machine learning e startups, contribui para áreas de pesquisa híbridas ao mesclar os campos da Administração e Ciência de Dados. Além disso, contribui para investidores com uma ferramenta de mapeamento inicial de startups na busca de targets com maior probabilidade de sucesso.   

scholarly journals Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Syed Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Farm Machinery ◽  
Learning Techniques

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

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