International Journal of Trend in Scientific Research and Development: Data Miining

International Journal of Trend in Scientific Research and Development: Data Miining

Showing posts with label Data Miining. Show all posts

Showing posts with label Data Miining. Show all posts

Saturday 19 June 2021

Depression Detection in Tweets using Logistic Regression Model

Team IJTSRD June 19, 2021 0

Depression Detection in Tweets using Logistic Regression Model

In the growing world of modernization, mental health issues like depression, anxiety and stress are very normal among people and social media like Facebook, Instagram and Twitter have boosted the growth of such mental health. Everything has its legitimacy and negative mark. During this pandemic, people are more likely to suffer from mental health issues, they are available 24 7 and are cut off from the real world. Past examinations have shown that individuals who invest more energy via online media are bound to be depressed. In this project, we find out people who are depressed based on their tweets, followers, following and many other factors. For this, I have trained and tested our text classifier, which will distinguish between the user who is depressed or not depressed.

by Rahul Kumar Sharma | Vijayakumar A "Depression Detection in Tweets using Logistic Regression Model"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021,

URL: https://www.ijtsrd.compapers/ijtsrd41284.pdf

Paper URL: https://www.ijtsrd.comcomputer-science/data-miining/41284/depression-detection-in-tweets-using-logistic-regression-model/rahul-kumar-sharma

callforpaperlifesciences, lifesciencesjournal, researchpapers

Saturday 3 April 2021

Prediction Analysis of Gaming Cost By Employing Data Mining Algorithms

Team IJTSRD April 03, 2021 0

Prediction Analysis of Gaming Cost By Employing Data Mining Algorithms

Video games are a source of entertainment for different age groups. Players who are seeking quality video games spend more money on their systems. In this way they spend a hefty amount on internet, storage, GPU etc. Due to the addictive nature the cost is not negligible and there are not so many researches done on predicting the cost a player has to suffer. In this paper, the gaming cost is being determined by applying different algorithms. Data was collected from different age groups with different characteristics like the choice of storage options, game genres, internet speed and time they spend on games. Different models are being used like Ada boost, logistic regression, Decision tree and Random forest to check the accuracy of prediction analysis. This research will help in development of further models which can measure the gaming cost more accurately. MD.

by Rhineul Islam | Nakib Aman Turzo | Pritom Sarker Bishal "Prediction Analysis of Gaming Cost By Employing Data Mining Algorithms"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021,

URL: https://www.ijtsrd.com/papers/ijtsrd38566.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/38566/prediction-analysis-of-gaming-cost-by-employing-data-mining-algorithms/md-rhineul-islam

callforpaperlanguages, languagesjournal, bestjournal

Thursday 18 February 2021

Association Rules Analysis using FP Growth Algorithm to Make Product Recommendations for Customer

Team IJTSRD February 18, 2021 0

Association Rules Analysis using FP Growth Algorithm to Make Product Recommendations for Customer

Companies usually have historical data on sales transactions from month to month, but unfortunately, they are only used as weekly and monthly reports. If it is allowed to continue for longer, there will be data growth which results in data richness but poor information. At the same time, companies often still use manual methods in their product marketing strategies that have no reference and are only based on estimates. One of them is the X Fashion Store that sells various local fashions. X Fashion Store has not used data to develop their marketing strategy. This study conducted an association rules analysis to develop a sales strategy. Sales transaction data used is data for December 2020 with a minimum value of support of 25 and a minimum value of confidence of 80 by processing data using Rapidminer application. FP Growth algorithm can produce association rules as a reference in product promotion and decision support in providing product recommendations to consumers based on predetermined minimum support and confidence values. The association rule result with the highest lift ratio is 10.51.

by Ni Putu Priyastini Dessy Safitri "Association Rules Analysis using FP-Growth Algorithm to Make Product Recommendations for Customer"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021,

URL: https://www.ijtsrd.com/papers/ijtsrd38459.pdf

Paper Url: https://www.ijtsrd.com/computer-science/data-miining/38459/association-rules-analysis-using-fpgrowth-algorithm-to-make-product-recommendations-for-customer/ni-putu-priyastini-dessy-safitri

callforpaperarts, artsjournal, peerreviewedjournal

Monday 15 February 2021

Forecasting the Drought in Bali using the Multilayer Perceptron Method

Team IJTSRD February 15, 2021 0

Forecasting the Drought in Bali using the Multilayer Perceptron Method

Disasters have a huge impact on a country and a region. Bali is one of the provinces in Indonesia which has some disaster, one of the disasters that occurred in Bali was drought. Forecasting of droughtinn Bali is necessary so that the government can prevent and manage this kind disastersand can make wise decisions based on information regarding the number of drought. This study aims to predict the number of drought in the next five years. The method used is the Multilayer Perceptron because it is able to predict time series events. The data used are drought disaster events from 2011 to 2019. The results of the analysis in this study indicate that the best Learning Rate and Hidden Layer for forecasting the number of disaster events are Learning Rate 0.7 and Hidden Layer 3.2 respectively with MAPE accuracy is 19.91 . Forecasting results in the coming years show that there is an increase and decrease in the number of drought in 2020 to 2024.

by Ni Putu Ratih Andini Putri "Forecasting the Drought in Bali using the Multilayer Perceptron Method"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021,

URL: https://www.ijtsrd.com/papers/ijtsrd38460.pdf

Paper Url: https://www.ijtsrd.com/computer-science/data-miining/38460/forecasting-the-drought-in-bali-using-the-multilayer-perceptron-method/ni-putu-ratih-andini-putri

callforpapermedicalscience, medicalsciencejournal

Thursday 4 February 2021

Twitter Big Data Analysis

Team IJTSRD February 04, 2021 0

Twitter Big Data Analysis

Internet usage is increasing today. As a result, internet use changes their lifestyle. Many activities such as shopping, meeting, social environment take place on the internet. Thus, a large amount of data is generated. As the amount of data increased, studies were carried out to store this data. Storage is generally used to analyze data. Data analysis studies are used to realize the advertisements and investments of organizations in the right area and in the right way. Therefore, big data analysis has a place in many areas. The data analyze people personally, except to give information specific to only one area. As a result, the desired effect is created on the analyzed people. Therefore, one of the most used fields is the political field. The most used platform for those who want to express their opinions in the political field is Twitter. Therefore, my aim is to obtain big data via Twitter, to store this data, and to analyze the political approach of the person on the stored data.

by Hamza Pekdogan | Dr. Atilla ErgÃ¼zen "Twitter Big Data Analysis"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021,

URL: https://www.ijtsrd.com/papers/ijtsrd38266.pdf

Paper Url: https://www.ijtsrd.com/computer-science/data-miining/38266/twitter-big-data-analysis/hamza-pekdogan

callforpaperpapersconference, highimpactfactor, manuscriptpublication

Thursday 10 December 2020

Forecasting the Number of Unemployment in Bali Province using the Support Vector Machine Method

Team IJTSRD December 10, 2020 0

Forecasting the Number of Unemployment in Bali Province using the Support Vector Machine Method

Unemployment has an impact on economic development in Indonesia. Bali is one of the provinces in Indonesia which has had a high unemployment rate in the last 13 years. Forecasting the number of unemployed in Bali Province is needed so that government policies can more optimally handle unemployment. This study aims to forecast the number of unemployed in the next five years. The method used is the Support Vector Machine because it is capable of forecasting a certain time series or time series. The data used are unemployment data from 2007 to 2019. The results of the analysis in this study show that the best SVM kernel type for forecasting the number of unemployed is radial. This type of kernel is used because it shows the smallest error value, namely MSE 0.007022, MAE 0.071292, and MAPE 23.24 . Forecasting results in the coming year an increase in the number of unemployed people from 2020 to 2024.

by Imelda Alvionita Tarigan "Forecasting the Number of Unemployment in Bali Province using the Support Vector Machine Method"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd38242.pdf

Paper URL : https://www.ijtsrd.com/computer-science/data-miining/38242/forecasting-the-number-of-unemployment-in-bali-province-using-the-support-vector-machine-method/imelda-alvionita-tarigan

ugcjournallist, listofugcapprovedjournals, researchpublication

Saturday 21 November 2020

Predicting Chronic Kidney Disease using Data Mining Techniques

Team IJTSRD November 21, 2020 0

Predicting Chronic Kidney Disease using Data Mining Techniques

Kidney is a significant aspect of a human body. Kidney infection or disappointments are expanded in every year. Presently a day’s chronic kidney infection is the most well known disease for the individuals. Today numerous individuals pass on due to chronic kidney disease. The principle issue of CKD is, it will influence the kidney gradually. A few people dont have side effects at all and are analysed by a lab test. It depicts the steady loss of kidney work. Early recognition and therapy are viewed as basic variables in the management and control of chronic kidney disease. Data mining techniques is utilized to extract data from clinical and laboratory, which can be useful to help doctors to recognize the seriousness stage of patients. Using Probabilistic Neural Networks PNN algorithm will get better prediction for determining the severity stage of chronic kidney disease.

by Seethal V | Kuldeep Baban Vayadande "Predicting Chronic Kidney Disease using Data Mining Techniques"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd37974.pdf

Paper URL : https://www.ijtsrd.com/computer-science/data-miining/37974/predicting-chronic-kidney-disease-using-data-mining-techniques/seethal-v

callforpaperlifesciences, lifesciencesjournal, researchpapers

Thursday 17 September 2020

User Personality Prediction on Facebook Social Media using Machine Learning

Team IJTSRD September 17, 2020 0

User Personality Prediction on Facebook Social Media using Machine Learning

In recent years, Social network use is increasingly build up. The various statistics are split widely through social media Such as Facebook, Twitter. Data about the person and what they communicate through the status updates are important for research in human personality. This paper intends to scrutinize the forecasting of personality traits of Facebook users bases on machine learning and part of the Big ve model this experiment uses my personality data set of Facebook users are used for linguistic factors respective to personality correlation. We used the Data Prepossessing concept of data mining after that feature Extraction. Next, we will work on feature selection. The Personality Prediction system built in the XGboosting classi cation model.

by Poonam L Patil | Dr. S. R. Jadhao "User Personality Prediction on Facebook Social Media using Machine Learning"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd33414.pdf

Paper Url: https://www.ijtsrd.com/computer-science/data-miining/33414/user-personality-prediction-on-facebook-social-media-using-machine-learning/poonam-l-patil

openaccessjournalofengineering, engineeringjournal, paperpublicationforengineering

Thursday 10 September 2020

A Research on Bitcoin

Team IJTSRD September 10, 2020 0

A Research on Bitcoin

A considerable lot of you may have caught wind of Bitcoin, an advanced token or digital currency that lets you send cash to any individual on the planet to pay for products and ventures dependent on the Peer to Peer Network engineering. It was designed by Satoshi Nakamoto whose genuine character is as yet mysterious for which the white paper was discharged on 2009. Exchanges would allow online payment to be sent genuinely where there is no need of other monetary establishments. Advanced mark can fill the need yet that costs the twofold spending and the fundamental advantages is lost. So the answer for the Double spending arrangement is the shared system. The distributed system records the interchange and hash a continuous chain, which formulate without repeatedly trying the evidence of work. This block chain confirms that it is originated from biggest pool of CPU. According to the efforts made messages are broadcasted, nodes are allowed to connect and disconnect at will.

by Vikrant R. Singh | Prof. Abhijit Desai "A Research on Bitcoin"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd33348.pdf

Paper Url: https://www.ijtsrd.com/computer-science/data-miining/33348/a-research-on-bitcoin/vikrant-r-singh

callforpapermedicalscience, medicalsciencejournal, manuscriptsubmission

Thursday 3 September 2020

An Analysis on IoT Methodologies for Smart Health Care and Surgical Treatment using Haptics

Team IJTSRD September 03, 2020 0

An Analysis on IoT Methodologies for Smart Health Care and Surgical Treatment using Haptics

The emerging technologies that make up Smart health care and surgical treatment, involve the Internet of Things. The haptic interfaces were used in various industries, but obviously, they are not incorporated with the two tools discussed above. This study seeks to know what the present usage of IoT has been incorporated into haptic interfaces in the smart health care and surgical treatment. This article describes the necessity for haptics feeling of touch in medical modeling systems and explains a wide range of laparoscopic training systems and other surgical simulators.

by B. R. Kavitha | P. T. Jamuna Devi "An Analysis on IoT Methodologies for Smart Health Care and Surgical Treatment using Haptics"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd33135.pdf

Paper Url: https://www.ijtsrd.com/computer-science/data-miining/33135/an-analysis-on-iot-methodologies-for-smart-health-care-and-surgical-treatment-using-haptics/b-r-kavitha

internationaljournalofscience, openaccessjournalofscience, ugcapprovedjournalsforscience

Thursday 18 June 2020

Analysis of Philanthropist for Internal NGO Management using Data Mining

Team IJTSRD June 18, 2020 0

Analysis of Philanthropist for Internal NGO Management using Data Mining

Non governmental organizations NGOs make significant contributions to diverse areas. Similar to for profits they need to manage their knowledge, but often lack resources for this. Social software may give a ""new hope"" for knowledge management in NGOs particularly by implementing social knowledge environments SKEs . Since majority of international NGOs have a website, is it possible to use it to support SKE This paper proposed a theoretical framework for creating a SKE on the base of NGO website. Proposed website model considers NGO features from this perspective and shows the approach to SKE development. Web mining a process through which meaningful data and patterns are acquired from large data sets, can benefit the charitable sector. Since the techniques used for mining meaningful data relies on computer science and coding, it is often automatic or semi automatic once the algorithms are in place. Therefore, this process can be a feasible and effective tool for donor profiling in India.

by Nikhita Singh | Roshan Singh | Rupal Singh | Pravendra Kumar Singh ""Analysis of Philanthropist for Internal NGO Management using Data Mining""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd31408.pdf

Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31408/analysis-of-philanthropist-for-internal-ngo-management-using-data-mining/nikhita-singh

ugcjournallist, listofugcapprovedjournals, researchpublication

Saturday 9 May 2020

Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model

Team IJTSRD May 09, 2020 0

Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model

Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy.

by G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar ""Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf

Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal

internationaljournalsinengineering, callforpaperengineering, ugcapprovedengineeringjournal

Thursday 12 March 2020

Impact of Big Data Analytics on Social Media

Team IJTSRD March 12, 2020 0

Impact of Big Data Analytics on Social Media

Social Media has wider scope in todayâ€™s World. There are over 900 social media sites available in the market. so that the massive information from that sites and problem with that info is storage. It is a popular way for people to expressing their thoughts and feelings and another important aspect in this study is Sentimental Analysis which is a study that include to analyze peopleâ€™s opinion and importance is to decide the achievement of social network The main aim of this study is to find different technique for analyzing social media information.

by Jignesh Sunil Naik ""Impact of Big Data Analytics on Social Media""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30223.pdf

Paper Url :https://www.ijtsrd.com/computer-science/data-miining/30223/impact-of-big-data-analytics-on-social-media/jignesh-sunil-naik

callforpaperpapersinjournals, multidisciplinaryjournal

Thursday 13 February 2020

Artificial Bee Colony Based Multiview Clustering ABC MVC for Graph Structure Fusion in Benchmark Datasets

Team IJTSRD February 13, 2020 0

Artificial Bee Colony Based Multiview Clustering ABC MVC for Graph Structure Fusion in Benchmark Datasets

Combining data from several information sources has become a significant research area in classification by several scientific applications. Many of the recent work make use of kernels or graphs in order to combine varied categories of features, which normally presume one weight for one category of features. These algorithms don't consider the correlation of graph structure between multiple views, and the clustering results highly based on the value of predefined affinity graphs. Artificial Bee Colony is combined to Multi view Clustering ABC MVC model in order to combine each and every one of features and learn the weight for each feature with respect to each cluster separately by new joint structured sparsity inducing norms. It also solves the issue of MVC by seamlessly combining the graph structures of varied views in order to completely make use of the geometric property of underlying data structure. ABC MVC model is based on the presumption with the purpose of intrinsic underlying graph structure would assign related connected part in each graph toward the similar group. Implementation results shows that the proposed ABC MVC model gets improved clustering accuracy than the other conventional methods such as Graph Structure Fusion GSF and Multiview Clustering with Graph Learning MVGL . The results are implemented to Caltech 101 and Columbia Object Image Library COIL 20 with respect to clustering accuracy ACC , Normalized Mutual Information NMI , and Adjusted Rand Index

by ARI N. Kamalraj ""Artificial Bee Colony Based Multiview Clustering (ABC-MVC) for Graph Structure Fusion in Benchmark Datasets""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30170.pdf

Paper Url : https://www.ijtsrd.com/computer-science/data-miining/30170/artificial-bee-colony-based-multiview-clustering-abc-mvc-for-graph-structure-fusion-in-benchmark-datasets/n-kamalraj

callforpapercommerce, ugcapprovedjournalsincommerce, commercejournal

Wednesday 25 December 2019

A Review Paper on Big Data and Hadoop for Data Science

Team IJTSRD December 25, 2019 0

A Review Paper on Big Data and Hadoop for Data Science

Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thous

ands of machines, each offering local computation and storage.

BY Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade

international journal of science, call for paper computer science, ugc approved journals for engineering

Tuesday 12 November 2019

Sentiment Analysis on Twitter Dataset using R Language

Team IJTSRD November 12, 2019 0

Sentiment Analysis on Twitter Dataset using R Language

Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language.

by B. Nagajothi | Dr. R. Jemima Priyadarsini "Sentiment Analysis on Twitter Dataset using R Language"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi

international journals in engineering, call for paper science, ugc list of journals

Friday 11 October 2019

Skin Lesion Classification using Supervised Algorithm in Data Mining

Team IJTSRD October 11, 2019 0

Skin Lesion Classification using Supervised Algorithm in Data Mining

Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and skin lesions is crucial.J48 Algorithm and SVM SUPPORT VECTOR MACHINE based techniques to estimate effort. In this work proposed system of the project is using data mining techniques for collecting the datasets for skin cancer. So that system can overcome to diagnosing the disease quickly and accuracy. Comparing to other algorithm proposed algorithm has more accuracy. When we have to using two kind of algorithm .They are J48, SVM. J48 Algorithm produced better accuracy more than SVM algorithm. The accuracy of the proposed system is 90.2381 . It means this prediction is very close to the actual values.

by G. Saranya | Dr. S. M. Uma ""Skin Lesion Classification using Supervised Algorithm in Data Mining""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd29346.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29346/skin-lesion-classification-using-supervised-algorithm-in-data-mining/g-saranya

call for paper health science, ugc approved engineering journal, social science journal

Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Dataset

Team IJTSRD October 11, 2019 0

Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Dataset

Human activity recognition is an important area of machine learning research as it has much utilization in different areas such as sports training, security, entertainment, ambient assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Nowadays mobile phone is well equipped with advanced processor, more memory, powerful battery and built in sensors. This provides an opportunity to open up new areas of data mining for activity recognition of human's daily living. In the paper, the benchmark dataset is considered for this work is acquired from the WISDM laboratory, which is available in public domain. We tested experiment using AdaBoost.M1 algorithm with Decision Stump, Hoeffding Tree, Random Tree, J48, Random Forest and REP Tree to classify six activities of daily life by using Weka tool. Then we also see the test output from weka experimenter for these six classifiers. We found the using Adaboost,M1 with Random Forest, J.48 and REP Tree improves overall accuracy. We showed that the difference in accuracy for Random Forest, REP Tree and J48 algorithms compared to Decision Stump, and Hoeffding Tree is statistically significant. We also show that the accuracy of these algorithms compared to Decision Stump, and Hoeffding Tree is high, so we can say that these two algorithms achieved a statistically significantly better result than the Decision Stump, and Hoeffding Tree and Random Tree baseline.

by Khin Khin Oo "Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Dataset"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd28073.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28073/daily-human-activity-recognition-using-adaboost-classifiers-on-wisdm-dataset/khin-khin-oo

science journal, open access journal of science, paper publication

Sunday 6 October 2019

A Markov Chain Approach on Daily Rainfall Occurrence

Team IJTSRD October 06, 2019 0

A Markov Chain Approach on Daily Rainfall Occurrence

Markov modeling is one of the tools that can be used to help planners for assess precipitation. The first order Markov chain model was used to predict daily precipitation intervals using transition probability matrices. The demand for precipitation is increasing, not only for data invention, but also to provide useful information in numerous applications, including water properties organization and the hydrological and agricultural subdivisions. In this study, the objective is to predict the probability of future precipitation of the city of Pyin Oo Lwin using the Markov chain model. The system was developed on the basis of the Markov method to forecast the occurrence of precipitation. The results show that models can forecast the state of a given day by 74 on average.

by Phyu Thwe | Ei Khaing Win | Hnin Pwint Myu Wai "A Markov Chain Approach on Daily Rainfall Occurrence"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd28075.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28075/a-markov-chain-approach-on-daily-rainfall-occurrence/phyu-thwe

special issue publication, call for paper international journal, call for paper mathematics

Tuesday 17 September 2019

Applied SPSS for Data Forecasting of Sale Quantity

Team IJTSRD September 17, 2019 0

Applied SPSS for Data Forecasting of Sale Quantity

SPSS is powerful to analyze business and marketing data. This paper intends to support business and marketing leaders the benefits of data forecasting with applied SPSS. It showed the sale quantity forecasting based on unit price and advertising. As SPSS's background algorithms, it showed the regression algorithm for data forecasting and ANOVA algorithm for data significant. It includes one sample data was downloaded from Google and was analyzed and viewed. It used IBM SPSS statistics version 23 and PYTHON version 3.7.

by Aung Cho | Aung Si Thu ""Applied SPSS for Data Forecasting of Sale Quantity""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd26378.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/26378/applied-spss-for-data-forecasting-of-sale-quantity/aung-cho

call for paper health science, ugc approved engineering journal, social science journal

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