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

https://hal.archives-ouvertes.fr/hal-01715576/document

A survey on health monitoring systems for health smart homes

Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action

In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF) The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center The most repetitive noiseless pixel value within the window is set as the new pixel value By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density

/pdf/a-new-method-based-on-pixel-density-in-salt-and-pepper-noise-1o8k50zgaf.pdf

A new method based on pixel density in salt and pepper noise removal

Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires and acts proactively. This paper proposes neural prediction techniques to anticipate a person’s next movement. We focus on neural predictors (multi-layer perceptron with back-propagation learning) with and without pre-training. The optimal configuration of the neural network is determined by evaluating movement sequences of real persons within an office building. The simulation results, obtained with one of the pre-trained neural predictors, show accuracy in next location prediction reaching up to 92%.

/pdf/person-movement-prediction-using-neural-networks-48ua49dgca.pdf

Person Movement Prediction Using Neural Networks

Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user's desires and acts proactively. This paper introduces Hidden Markov Models, in order to anticipate the next movement of some persons. The optimal configuration of the model is determined by evaluating some movement sequences of real persons within an office building. The simulation results show accuracy in next location prediction reaching up to 92%.

/pdf/person-movement-prediction-using-hidden-markov-models-40wu4feji6.pdf

Person Movement Prediction Using Hidden Markov Models

A study on forecasting electricity production and consumption in smart cities and factories

A new image denoising method for impulse noise in greyscale images using a context-based prediction scheme is presented. The algorithm replaces the noisy pixel with the value occurring with the highest frequency, in the same context as the replaceable pixel. Since it is a context-based technique, it preserves the details in the filtered images better than other methods. In the aim of validation, the authors have compared the proposed method with several existing denoising methods, many of them being outperformed by the proposed filter.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2015.0702

Context-based prediction filtering of impulse noise images

In this work we propose a prediction by partial matching technique to anticipate and prefetch web pages and files accessed via browsers. The goal is to reduce the delays necessary to load the web pages and files visited by the users. Since the number of visited web pages can be high, tree-based and table-based implementations can be inefficient from the representation point of view. Therefore, we present an efficient way to implement the prediction by partial matching as simple searches in the observation sequence. Thus, we can use high number of states in long web page access histories and higher order Markov chains at low complexity. The time-evaluations show that the proposed PPM implementation is significantly more efficient than previous implementations. We have enhanced the predictor with a confidence mechanism, implemented as saturating counters, which classifies dynamically web pages as predictable or unpredictable. Predictions are generated selectively only from web pages classified as predictable, improving thus the accuracy. The experiments show that the prediction by partial matching of order 4 with a history of 500 web pages is the optimal.

Arpad Gellert

Papers

Person Movement Prediction Using Neural Networks

Person Movement Prediction Using Hidden Markov Models

A study on forecasting electricity production and consumption in smart cities and factories

Context-based prediction filtering of impulse noise images

Web prefetching through efficient prediction by partial matching