- The document analyzes data from a study that tracked activity using smartphone sensors to predict activity type based on quantitative measurements.
- It builds random forest and support vector machine (SVM) models on a training data set and finds the random forest model has a lower error rate of 11%, making it the better predictive model.
- Variable importance analysis of the random forest model identifies 11 highly correlated variables as the most important predictors of activity type. Tuning the random forest model to use just these 11 variables results in a 16% error rate on a validation data set.
- Applying the tuned random forest model to a test data set achieves an error rate of 17%, confirming the 11 variables as key predictors of activity type
This report includes information about:
1. Pre-Processing Variables
a. Treating Missing Values
b. Treating correlated variables
2. Selection of Variables using random forest weights
3. Building model to predict donors and amount expected to be donated.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
Research Project - The objective of this project is to predict the outcome of animals placed in shelters given features such as the animal’s age, breed, and colour. There are 5 possible outcomes for each animal with euthanasia being the worst outcome. The shelter hopes to be able to determine which animals are likely to be euthanized as well as find trends into what features increase the chance for adoption. This provides a chance for shelters to try to aid animals with a low chance of adoption. The overall goal is to decrease the yearly number of animals euthanized.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
This report includes information about:
1. Pre-Processing Variables
a. Treating Missing Values
b. Treating correlated variables
2. Selection of Variables using random forest weights
3. Building model to predict donors and amount expected to be donated.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
Research Project - The objective of this project is to predict the outcome of animals placed in shelters given features such as the animal’s age, breed, and colour. There are 5 possible outcomes for each animal with euthanasia being the worst outcome. The shelter hopes to be able to determine which animals are likely to be euthanized as well as find trends into what features increase the chance for adoption. This provides a chance for shelters to try to aid animals with a low chance of adoption. The overall goal is to decrease the yearly number of animals euthanized.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
Deterministic Stabilization of a Dynamical System using a Computational ApproachIJAEMSJORNAL
The qualitative behavior of a multi-parameter dynamical system has been investigated. It is shown that changes in the initial data of a dynamical system will affect the stabilization of the steady-state solution which is originally unstable. It is further shown that the stabilization of a five-dimensional dynamical system can be used as an alternative method of verifying qualitatively the concept of the stability of a unique positive steady-state solution. These novel contributions have not been seen elsewhere; these are presented and discussed in this paper.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensem...IJECEIAES
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.
The data set is about the 1987 national Indonesia contraceptive prevalence survey. Data Retrieving, cleaning, exploration, modelling with classification using Decision Tree and KNN model.
Preprocessing and Classification in WEKA Using Different ClassifiersIJERA Editor
Data mining is a process of extracting information from a dataset and transform it into understandable structure
for further use, also it discovers patterns in large data sets [1]. Data mining has number of important techniques
such as preprocessing, classification. Classification is one such technique which is based on supervised learning.
It is a technique used for predicting group membership for the data instance. Here in this paper we use
preprocessing, classification on diabetes database. Here we apply classifiers on this database and compare the
result based on certain parameters using WEKA. 77.2 million people in India are suffering from pre diabetes.
ICMR estimates that around 65.1million are diabetes patients. Globally in year 2010, 227 to 285 million people
had diabetes, out of that 90% cases are related to type 2 ,this is equal to 3.3% of the population with equal rates
in both women and men in 2011 it resulted in 1.4 million deaths worldwide making it the leading cause of
death.
Prediction model of algal blooms using logistic regression and confusion matrix IJECEIAES
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...ijcsit
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from ecommerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.
Improved Slicing Algorithm For Greater Utility In Privacy Preserving Data Pub...Waqas Tariq
Several algorithms and techniques have been proposed in recent years for the publication of sensitive microdata. However, there is a trade-off to be considered between the level of privacy offered and the usefulness of the published data. Recently, slicing was proposed as a novel technique for increasing the utility of an anonymized published dataset by partitioning the dataset vertically and horizontally. This work proposes a novel technique to increase the utility of a sliced dataset even further by allowing overlapped clustering while maintaining the prevention of membership disclosure. It is further shown that using an alternative algorithm to Mondrian increases the efficiency of slicing. This paper shows though workload experiments that these improvements help preserve data utility better than traditional slicing.
Data Analysis. Predictive Analysis. Activity Prediction that a subject perfor...Guillermo Santos
Recently, our lives are invaded by small mobile devices, known as smartphones. These devices are mobile mini-computers, they have an operating system that allows it to launch applications, include a set of applications to manage contacts and address book, to create, edit or view different types of documents, to access or browse the Web, too provide us telephony or messaging services, etc. Apart from these previous features, the most of the smartphones have currently begun to incorporate other features such as cameras, GPS and various types of sensors.
In this analysis, we used data obtained from the accelerometer [1] and gyroscope[2] sensor signals of the smartphones. The accelerometer and gyroscope sensors measure 3-axial linear acceleration and 3-axial angular velocity, with these two sensors can monitor device acceleration, positions, orientation, rotation and angular motion. All these data can be stored and used to recognize a user’s activity. Here we refer to physical activities that a human person can perform daily such as walking, walking up, jogging, sitting, laying, etc.
The aim of this analysis consisted of perform a classification’s task. We took a dataset with their attributes (acceleration, orientation,…) and its labeled variable (in this case is activity), and later we created various classification’s models also known classifiers. To create these classification’s models we can use various algorithms of classification. These algorithms use all available information of a dataset to help us to classify or predict that activity is performed by a human person.
To create models of classification (models of classification), we performed a first task that consisted of choose different algorithms or techniques of classification, then for each algorithm or technique of classification we applied what is called cross-validation [3], that is, we trained these algorithm with a set of training data that corresponds to several observations of our available dataset. The following task was tested our classification’s algorithm to observe the accuracy, that is, if our predictive model can classify correctly a human’s activity according to the acquired knowledge in the stage of training. This whole process is known as supervised learning [4].
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Deterministic Stabilization of a Dynamical System using a Computational ApproachIJAEMSJORNAL
The qualitative behavior of a multi-parameter dynamical system has been investigated. It is shown that changes in the initial data of a dynamical system will affect the stabilization of the steady-state solution which is originally unstable. It is further shown that the stabilization of a five-dimensional dynamical system can be used as an alternative method of verifying qualitatively the concept of the stability of a unique positive steady-state solution. These novel contributions have not been seen elsewhere; these are presented and discussed in this paper.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensem...IJECEIAES
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.
The data set is about the 1987 national Indonesia contraceptive prevalence survey. Data Retrieving, cleaning, exploration, modelling with classification using Decision Tree and KNN model.
Preprocessing and Classification in WEKA Using Different ClassifiersIJERA Editor
Data mining is a process of extracting information from a dataset and transform it into understandable structure
for further use, also it discovers patterns in large data sets [1]. Data mining has number of important techniques
such as preprocessing, classification. Classification is one such technique which is based on supervised learning.
It is a technique used for predicting group membership for the data instance. Here in this paper we use
preprocessing, classification on diabetes database. Here we apply classifiers on this database and compare the
result based on certain parameters using WEKA. 77.2 million people in India are suffering from pre diabetes.
ICMR estimates that around 65.1million are diabetes patients. Globally in year 2010, 227 to 285 million people
had diabetes, out of that 90% cases are related to type 2 ,this is equal to 3.3% of the population with equal rates
in both women and men in 2011 it resulted in 1.4 million deaths worldwide making it the leading cause of
death.
Prediction model of algal blooms using logistic regression and confusion matrix IJECEIAES
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...ijcsit
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from ecommerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.
Improved Slicing Algorithm For Greater Utility In Privacy Preserving Data Pub...Waqas Tariq
Several algorithms and techniques have been proposed in recent years for the publication of sensitive microdata. However, there is a trade-off to be considered between the level of privacy offered and the usefulness of the published data. Recently, slicing was proposed as a novel technique for increasing the utility of an anonymized published dataset by partitioning the dataset vertically and horizontally. This work proposes a novel technique to increase the utility of a sliced dataset even further by allowing overlapped clustering while maintaining the prevention of membership disclosure. It is further shown that using an alternative algorithm to Mondrian increases the efficiency of slicing. This paper shows though workload experiments that these improvements help preserve data utility better than traditional slicing.
Data Analysis. Predictive Analysis. Activity Prediction that a subject perfor...Guillermo Santos
Recently, our lives are invaded by small mobile devices, known as smartphones. These devices are mobile mini-computers, they have an operating system that allows it to launch applications, include a set of applications to manage contacts and address book, to create, edit or view different types of documents, to access or browse the Web, too provide us telephony or messaging services, etc. Apart from these previous features, the most of the smartphones have currently begun to incorporate other features such as cameras, GPS and various types of sensors.
In this analysis, we used data obtained from the accelerometer [1] and gyroscope[2] sensor signals of the smartphones. The accelerometer and gyroscope sensors measure 3-axial linear acceleration and 3-axial angular velocity, with these two sensors can monitor device acceleration, positions, orientation, rotation and angular motion. All these data can be stored and used to recognize a user’s activity. Here we refer to physical activities that a human person can perform daily such as walking, walking up, jogging, sitting, laying, etc.
The aim of this analysis consisted of perform a classification’s task. We took a dataset with their attributes (acceleration, orientation,…) and its labeled variable (in this case is activity), and later we created various classification’s models also known classifiers. To create these classification’s models we can use various algorithms of classification. These algorithms use all available information of a dataset to help us to classify or predict that activity is performed by a human person.
To create models of classification (models of classification), we performed a first task that consisted of choose different algorithms or techniques of classification, then for each algorithm or technique of classification we applied what is called cross-validation [3], that is, we trained these algorithm with a set of training data that corresponds to several observations of our available dataset. The following task was tested our classification’s algorithm to observe the accuracy, that is, if our predictive model can classify correctly a human’s activity according to the acquired knowledge in the stage of training. This whole process is known as supervised learning [4].
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Explore the latest techniques and technologies used in classifying fetal health, from traditional methods to cutting-edge AI approaches. Understand the importance of accurate classification for prenatal care and fetal well-being. Join us to delve into this critical aspect of healthcare. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
ON THE PREDICTION ACCURACIES OF THREE MOST KNOWN REGULARIZERS : RIDGE REGRESS...ijaia
The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the
predication accuracy depends heavily on the nature of the sampled datasets.
A survey of modified support vector machine using particle of swarm optimizat...Editor Jacotech
The main objective of this survey paper is to provide a detailed description of Wireless Sensor Networks with Medium Access Control layer and Routing layer. In the medium access control layer, Event Driven Time Division Multiple Access protocol is studied and in Network layer, two routing protocols Bellman-Ford and Dynamic Source Routing are studied.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
OPTIMIZATION IN ENGINE DESIGN VIA FORMAL CONCEPT ANALYSIS USING NEGATIVE ATTR...csandit
There is an exhaustive study around the area of engine design that covers different methods that try to reduce costs of production and to optimize the performance of these engines.
Mathematical methods based in statistics, self-organized maps and neural networks reach the best results in these designs but there exists the problem that configuration of these methods is
not an easy work due the high number of parameters that have to be measured.
OPTIMIZATION IN ENGINE DESIGN VIA FORMAL CONCEPT ANALYSIS USING NEGATIVE ATTR...cscpconf
There is an exhaustive study around the area of engine design that covers different methods that try to reduce costs of production and to optimize the performance of these engines. Mathematical methods based in statistics, self-organized maps and neural networks reach the best results in these designs but there exists the problem that configuration of these methods is not an easy work due the high number of parameters that have to be measured. In this work we extend an algorithm for computing implications between attributes with positive and negative values for obtaining the mixed concepts lattice and also we propose a theoretical method based in these results for engine simulators adjusting specific and different elements for obtaining optimal engine configurations.
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Data analysis_PredictingActivity_SamsungSensorData
1. Karen Yang
Title: Predicting Activity
Introduction:
Understanding the relationship between activity and the variables that help to predict it
holds tremendous value in terms of finding ways to increase movement to improve
health, reduce medical costs, and time lost from work. Tracking activity is important for
obtaining a baseline measurement prior to finding ways to improve it. Models and their
features can be used to test which measurements predict activity with high accuracy.
This data analysis tells the story of model and feature selections.
To begin, which model predicts activity better? Is it random forest or Support Vector
Machines (SVM)? Also, which features are important in predicting activity? This study
looks at these questions, using data from a previous experiment, namely “Human
Activity Recognition Using Smartphone Dataset”[1]. The purpose of this study is to build
a model that predicts what activity a subject is performing based on the quantitative
measurements tracked from a Samsung phone.
To discuss briefly the background of the previous experiment, two sensor signals, called
accelerometer and gyroscope, were used to gauge acceleration and angular velocity as
measured along x, y, and z axes, to capture movement. These sensors are contained
within a Samsung Galaxy S II smartphone that was worn on the waist by 30 subjects
who volunteered for the study. As these volunteers carried on within their daily routine,
these sensors recorded measurements of their activity as classified as walking, walking
up, walking down, sitting, standing, and laying [1]. Thus, the data obtained are used for
the purpose of this data analysis.
Methods:
Data Collection
The data come from the “Human Activity Recognition Using Smartphones Data Set” at
the UCI Machine Learning Repository: Center for Machine Learning and Intelligent
Systems[1]. The entire data set consists of 7352 observations with 563 variables. The
data were downloaded from the following website on February 27, 2013 using the R
programming language [3]: https://sparkpublic.s3.amazonaws.com/dataanalysis/samsungData.rda. The raw data and study
description can be viewed at this website:
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones[
1].
Exploratory Analysis
2. Exploratory analysis was performed with use of tables and plots of the data. Exploratory
analysis was used to (1) identify missing values, (2) verify the quality of the data, and (3)
determine the models used in analysis relating the activity variable to the other identified
variables [2].
With 7352 observations and 563 variables in the downloaded dataset, there were no
missing observations. For the outcome variable, called activity, there are six categories
with the following levels: 1) laying; 2) sitting; 3) standing; 4) walking; 5) walking down;
and 6) walking up. Using the table function, the most frequent activity in terms of count
was laying with 1407 tallies. The least frequent activity was walking down with 986 tallies.
Sitting, standing, walking, and walking up had tallies of 1286, 1374, 1226, and 1073,
each respectively. For the subject variable, there were 30 subjects with counts in the 300
to 400 tallies for each subject, individually.
In cleaning the data, minor changes were made. I removed “,”, “.”, “%”, “”, “-“, “( “, and
“)” from the variable names for better readability. Also, lower cases letters were used for
uniformity in the variable names’ appearance. For the outcome variable, activity, I
transformed the class of the data from character to factor for purpose of a multi-class
analysis, which is standard practice for purposes of computation and analysis.
The downloaded dataset was split into three groups for data analysis: 1) the training
data set; 2) the validation data set; and 3) the testing data set. Subjects
1,3,5,6,10,12,13,16,20, and 23 were assigned to the training data set, which totaled
2053 observations with 563 variables. Subjects 2,7,8,9,14,17,19,21,24, and 26 were
assigned to the validation data set, which totaled 2440 observations and 563 variables.
Subjects 4,11,15,18,22,25,27,28,29, and 30 were assigned to the testing data set, which
totaled 2859 observations and 563 variables.
For preliminary data analysis, I ran two models, namely random forest and Support
Vector Machine (SVM) with use of the training data set. Next, using the validation data
set, I then tested and tuned the model. I then compared the error rates to assess which
model performed better at classification and, later, to determine which features were
important in predicting the outcome. Finally, I applied the better model with the identified
important variables to the test data set and reported the final result.
Statistical Modeling
To relate the activity variable with the other variables, I first ran a random forest model
because this model is best suited for large data set with several predictors without
knowing beforehand which variables are the best features to use to tune the model. Also,
the outcome variable is a factor variable with classes or levels, which is appropriate for
this type of model. Model selection was performed on the basis of exploratory analysis to
assess variable importance amongst 562 variables (excluding the subjects variable).
This random forest modeling method looks at a large group of decision trees by
3. bootstrapping. Bootstrapping is when you take a random sample of the original data with
replacement, Groupings of trees are generated and the number of variables chosen at
random is what determines the node splitting. Finally, an error rate can be calculated to
determine accuracy in classification [4].
I then ran a Support Vector Machine (SVM) model. Similar to random forest modeling,
this model incorporates all features and does a global classification [5]. An error rate can
be calculated and a table can be generated to see if the predicted values match the
actual values in terms of classification. Finally, an error rate can be calculated to
determine accuracy in classification [5].
Results:
Using the training data set to build the random forest model, the call to the function
shows that 500 trees were generated and 23 variables were tried at each split. The out
of bag error rate is 1.02%. The walking up class had no errors in classification.
The plot in figure 1 shows that there were 11 variables that were influential in predicting
activity. The range for the mean decrease in the Gini measurement is between 20 and
55. Gini measures how much this variable in the model reduces classification error [6].
For simplicity, I will refer to their variable names since a codebook does not exist to
clearly define and describe them, each individually. For each of these “important”
variables, I calculate the correlation with the activity variable as a cross validation to see
what their magnitude (strength in relationship) looks like. We should expect medium to
high correlations if these are truly the “important” variables that wield prediction power as
identified by the random forest model [6].
These 11 include: 1) tgravityacc.min.x (correlation=0.6365321); 2) angle.x.gravitymean
(correlation=-0.6049978); 3) tgravityacc.energy.x (correlation=0.6318179); 4)
tgravityacc.mean.x (correlation=0.6432291); 5) tgravityacc.max.x
(correlation=0.6485595); 6) tgravityacc.max.y (correlation=-0.6850469); 7)
tgravityacc.min.y(correlation=-0.695804); 8) angle.y.gravitymean
(correlation=0.6662157); 9) tgravityacc.energy.y (correlation=-0.500473);10)
tgravityacc.mean.y (correlation=-0.6927581); and 11) tbodyacc.max.x
(correlation=0.8150434). These 11 variables made the cutoff as the most important
based on the decision criterion that a natural break occurs at the mean decrease in Gini
measurement at 20 as shown in figure 1. Overall, the correlations for these 11 are
medium to high, ranging from 50% to 82%, thereby adding a cross validation that these,
in fact, are important variables.
Both models used the validation data set to obtain predicted values. With the exception
of the subject variable, all the features were used in both models. The error rate for the
random forest model was 0.1122951, roughly 11%, and for the SVM, the error rate was
4. 0.1467213, roughly 15%. Clearly, the random forest model does a better job of
prediction by 4%.
It is still worth it to briefly discuss the results of the SVM model. According to the
Confusion Matrix, the overall SVM model statistics showed an accuracy of 0.8533,
hence 85% (95% confidence interval of 0.8386 to 0.8571). Thus, it is a pretty good
model.
Based on the error rates alone, however, the random forest model is the better model.
With this model selected, I next turn to model tuning. I use the validation data set to
rerun the random forest model with the 11 important variables and to again check the
error rate, which was 0.2057377, for the tuned model. Recall that the initial random
forest model had an error rate of roughly 11%. Thus, there is a difference of about 10%
in error rate between using all of the features and only 11 of the features. The validation
data set had more observations than the training data set and the increase in error rate
could have been due to tuning the model to this smaller data set.
Next, I applied the tuned model, which uses only the 11 variables of importance, to the
test data set. The call to the random forest function gives an out of bag error rate of
2.87% with 3 variables tried at each split. The error rate between the predicted values
and the actual values of activity in the test data set is 0.1699895, which is a difference of
6% in comparison to the original random forest model with all the predictors except the
subject variable.
Overall, the findings show that the random forest model was better than the SVM model
in terms of having greater accuracy. By sorting the variable by importance, I was able to
identify the most influential variables. Using the validation data set, I tuned the random
forest model, using only the 11 important variables and arrived at an error rate of 21%,
approximately 10% more than the original model. Applying the tuned model to the test
data set, which was a separate and much larger data set, I obtained an error rate of
roughly 17%, which is a difference of 6% compared to the original error rate with the
unturned random forest model.
Conclusions:
This study demonstrates that random forest or SVM modeling are appropriate in dealing
with large data sets with lots of variables without a priori knowledge as to which features
(variables) are appropriate to select as predictors. In this particular study, the error rate
for the random forest model was lower than the SVM model by 4%. Moreover, the
random forest model proved powerful in that it was able to identify the features of
importance that wield the greatest influence over the outcome variable. Excluding the
subject variable, there were 11 variables of importance out of a total of 562 possible
variables. In the tuned model applied to the testing data set, hence the final model, the
error rate was 17%, which is a difference of 6% compared to the original model.
5. One speculation for this difference is that the data set for the testing data set was much
larger than the training data set by 806 observations. The model identifying the 11
important variables used the training dataset so the model was tuned to that particular
data set. The larger testing data set carries its own nuances and is distinct from the
training data set. As a result, the 6% error rate could capture these nuances.
References
1. UCI Machine Learning Repository: Center for Machine Learning and Intelligent
Systems. URL:
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.
Accessed 2/27/2013.
2. Coursera: "Data Analysis". Online course given by Jeff Leek. The Johns Hopkins
Bloomberg School of Public Health. Dates: 22.01.12-19.03.13. URL:
https://www.coursera.org/course/dataanalysis.
3. de Vries, Andrie, and Joris Meys. R for Dummies. John Wiley & Sons, 2012.
4. Random Forests Leo Breiman and Adele Cutler. URL:
http://www.stat.berkeley.edu/%7Ebreiman/RandomForests/cc_home.htm. Accessed
3/5/2013.
5. Data Mining Algorithms in R/Classification/SVM. URL:
http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/SVM. Accessed
3/6/2013.
6. Stackoverflow. R Random Forests Variable Importance. URL:
http://stackoverflow.com/questions/736514/r-random-forests-variable-importance.
Accessed 3/7/2013.
6. Caption
Figure 1. Variable importance plot for random forest model. The mean decreases
in Gini measurement is a measure of accuracy in random forest related to the out
of bag calculation. Higher scores indicate greater mean decreases, essentially
greater importance of the variable to classification [6]. Itt means that a particular
predictor variable plays a greater role in partitioning the data into the defined
classes [6]. The variables are sorted by importance in decreasing order. The
random forest model uses the training data set. The data come from UCI
Machine Learning Repository: Center for Machine Learning and Intelligent
Systems.