Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

分群式取樣法於類別不平衡問題之研究;Clustering-Based Under-sampling in Class Imbalanced Data

Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level are time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the north-eastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.

Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

Analytics of health big data are very crucial for providing cost effective quality health care. Over recent years, the analytics on healthcare big data has evolved into a challenging task for getting insights into a very large data set for improving the health services. This enormous amount of data, which is being generated incessantly over a long period of time, has put a great deal of stress on the write performance as well as on scalability. Moreover, there is a requirement of efficient storage and meaningful processing of these data which is an another challenging issue. The traditional relational databases, which were used in the storage of health data, are now unable to handle due to its massive and varied nature. Besides, these databases have some inherent weakness in terms of scalability, storing varied data format, etc. So there is a necessity for a new kind of data storage management system. This paper proposes a new big data storage architecture consisting of application cluster and a storage cluster to facilitate read/write/update speedup as well as data optimization. The application cluster is used to provide efficient storage and retrieval functions from the users. The storage services will be provided through the storage cluster.

A Novel Storage Architecture for Facilitating Efficient Analytics of Health Informatics Big Data in Cloud

Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India

The devices are becoming ubiquitous and interconnected due to rapid advancements in computing and communication technology. The Internet of Vehicles (IoV) is one such example which consists of vehicles that converse with each other as well as with the public networks through V2V (vehicle-to-vehicle), V2P (vehicle-to-pedestrian) and V2I (vehicle-to-infrastructure) communications. The social relationships amongst vehicles create a social network where the participants are intelligent objects rather than the human beings and this leads to emergence of Social Internet of Vehicles (SIoV). The big data generated from these networks of devices are needed to be processed intelligently for making these systems smart. The security and privacy issues such as authentication and recognition attacks, accessibility attacks, privacy attacks, routing attacks, data genuineness attacks etc. are to be addressed to make these cyber physical network systems very reliable. This paper presents a comprehensive survey on SIoV and proposes a novel social recommendation model that could establish links between social networking and SIoV for reliable exchange of information and intelligently analyze the information to draw authentic conclusions for making right assessment. The future Intelligent IoV system which should be capable to learn and explore the cyber physical system could be designed.

Social Networking and Big Data Analytics Assisted Reliable Recommendation System Model for Internet of Vehicles

Estimation of settling velocity using generalized reduced gradient (GRG) and hybrid generalized reduced gradient–genetic algorithm (hybrid GRG-GA)

There is an immense concern on our vigilance for controlling the spread of pandemics such as Ebola, Zika, and H1N1 etc through state of art technology The dynamics become very complex of epidemics in sweeping population Efficient descriptive, predictive, preventive and prescriptive analyses on the huge data generated by SMAC are very crucial for valuable arrangement and associated responsive tactics In this paper, we have proposed the use of machine learning techniques for performance evaluation of time series forecasting of Ebola casualties By experimenting without lag creation, we achieved the best results in the MAE of 785%, RMSE value of 6114%, and Direction Accuracy of 8599% with Random Tree Classifier Thus we can conclude that by using these models for forecasting epidemic spread and developing public health policies leads the health authorities to ensure the appropriate actions for the control of the outbreak

Manish Kumar Pandey

Papers

A Novel Storage Architecture for Facilitating Efficient Analytics of Health Informatics Big Data in Cloud

Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India

Social Networking and Big Data Analytics Assisted Reliable Recommendation System Model for Internet of Vehicles

Estimation of settling velocity using generalized reduced gradient (GRG) and hybrid generalized reduced gradient–genetic algorithm (hybrid GRG-GA)

Performance Analysis of Time Series Forecasting Using Machine Learning Algorithms for Prediction of Ebola Casualties