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

Data Mining Practical Machine Learning Tools and Techniques

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

C4.5: Programs for Machine Learning (書評)

Stock market is a promising financial investment that can generate great wealth However, the volatile nature of the stock market makes it a very high risk investment Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment This paper investigates various techniques for the stock market prediction using artificial neural network (ANN) The aim of this paper is to provide a review of the applications of ANN in stock market prediction in order to determine what can be done in the future

A review of stock market prediction with Artificial neural network (ANN)

Problem statement: In solving a classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association. Therefore, data transformation becomes a tedious trial-and-error work and the classification result is often not very promising especially when the number of tables and the degree of one-to-many association are large. Approach: We proposed a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables to facilitate the task of solving a classification problem in relational database. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations. It can be used in two ways. One is user-controlled clustering, where the user may control the result of clustering by varying the compactness of the spherical cluster. The other is automatic clustering, where a non-overlap clustering strategy is applied. In this study, we use the latter method to dynamically cluster multiple instances, as a means of aggregating them and illustrate the effectiveness of this method using the semi-supervised genetic algorithm-based clustering technique. Results: It was shown in the experimental results that using the reciprocal of Davies-Bouldin Index for cluster dispersion and the reciprocal of Gini Index for cluster purity, as the fitness function in the Genetic Algorithm (GA), finds solutions with much greater accuracy. The results obtained in this study showed that automatic clustering (seeding), by optimizing the cluster dispersion or cluster purity alone using GA, provides one with good results compared to the traditional k-means clustering. However, the best result can be achieved by optimizing the combination values of both the cluster dispersion and the cluster purity, by putting more weight on the cluster purity measurement. Conclusion: This study showed that semi-supervised genetic algorithm-based clustering techniques can be applied to summarize relational data with more effectively and efficiently.

Summarizing relational data using semi-supervised genetic algorithm-based clustering techniques

A Named-Entity Recognition (NER) is part of the process in Text Mining and it is a very useful process for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. However, different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities; Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system.

/pdf/malay-named-entity-recognition-based-on-rule-based-approach-12koaulhfb.pdf

Malay named entity recognition based on rule-based approach

Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms These three elements (eg, BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture

/pdf/towards-paddy-rice-smart-farming-a-review-on-big-data-581gy0kuba.pdf

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Problem statement: Orang-utan is classified as a totally protected species and is listed as an endangered species in Borneo. The survival of this species is highly dependent on the existence and quality of the lowland forest of Sabah. However, most of the pristine habitats in the lowland area have been converted into other land use activities such as a large scale plantation. This is due to the fact that most of the lowland forests are facing a continuous degradation process that will decrease its commercial value when it comes to generating revenue to the state government. Thus, the efforts to restore the forest are very vital. The main objectives of this study include establishing the relative spatial distribution of orang-utan in order to assess and determine the effects of the forest conversions in four main wild orang-utan population landscape, demonstrating the orang-utan population movement pattern as a response to the heavy logging activities and also quantifying the effect of logging activities on the status of food trees or plant species for orang-utan in their current forest habitat. Approach: In this research, relevant features are constructed in order to study the impacts of logging and forest conversion on Orang-utan population in Borneo. These features include aerial surveys and feeding behaviors. An aerial survey on orang-utan’s nest in four out of six main forest habitats for orang-utan in Sabah has been conducted between May 2005 and June 2009 in order to map the relative distribution and spatial density of the orang-utans. This is conducted in order to determine the impacts of the forest conversion for the last 20 years upon the orang-utan spatial distribution. In this project, three series of aerial surveys, covering Malua Forest Reserve, have been conducted to demonstrate the dynamic movement and habitat utilization by the orang-utan population, due to the logging activities within the forest habitat. A long term observation of the orang-utan feeding behavior in the degraded forest in North Ulu Segama (NUS) has been also conducted to determine their feeding ecology in a logged over forest. Results: This study suggested that (i) forest conversion and logging activities have effects on the orang-utan habitat utilization and movement pattern; (ii) due to the influx of the forest conversion, some of the orang-utan in the forest reserve is concentrated in certain area, adjacent to the boundary of the forest reserve due to their movement limitation by the river network; (iii) the orang-utan population in degraded forest of NUS consumed almost different species of food plants in 2009, compared to year 1974. Conclusion: These results demonstrated that forest conversion and unsustainable logging activities are the main threat for the orang-utans conservation in Sabah. Therefore, the conservation of orang-utan populations are not guaranteed by the establishment of more protected area, but by ensuring that more forest habitats can be managed in sustainable way in order to avoid further forest degradation and to ensure their habitats remain connected to the large forest landscape.

Summarizing Spatial Distribution Density, Movement Patterns and Food Resources to Study the Impacts of Logging and Forest Conversion on Orang-utan Population

Rayner Alfred

Papers

A review of stock market prediction with Artificial neural network (ANN)

Summarizing relational data using semi-supervised genetic algorithm-based clustering techniques

Malay named entity recognition based on rule-based approach

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Summarizing Spatial Distribution Density, Movement Patterns and Food Resources to Study the Impacts of Logging and Forest Conversion on Orang-utan Population