There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

International Journal of Computer and Applications

Healthcare using body sensor data has been getting huge research attentions by a wide range of researchers because of its good practical applications such as smart health care systems. For instance, smart wearable sensor-based behavior recognition system can observe elderly people in a smart eldercare environment to improve their lifestyle and can also help them by warning about forthcoming unprecedented events such as falls or other health risk, to prolong their independent life. Although there are many ways of using distinguished sensors to observe behavior of people, wearable sensors mostly provide reliable data in this regard to monitor the individual’s functionality and lifestyle. In this paper, we propose a body sensor-based activity modeling and recognition system using time-sequential information-based deep Neural Structured Learning (NSL), a promising deep learning algorithm. First, we obtain data from multiple wearable sensors while the subjects conduct several daily activities. Once the data is collected, the time-sequential information then go through some statistical feature processing. Furthermore, kernel-based discriminant analysis (KDA) is applied to see the better clustering of the features from different activity classes by minimizing inner-class scatterings while maximizing inter-class scatterings of the samples. The robust time-sequential features are then applied with Neural Structured Learning (NSL) based on Long Short-Term Memory (LSTM), for activity modeling. The proposed approach achieved around 99% recall rate on a public dataset. It is also compared to existing different conventional machine learning methods such as typical Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) where they yielded the maximum recall rate of 94%. Furthermore, a fast and efficient explainable Artificial Intelligence (XAI) algorithm, Local Interpretable Model-Agnostic Explanations (LIME) is used to explain and check the machine learning decisions. The robust activity recognition system can be adopted for understanding peoples' behavior in their daily life in different environments such as homes, clinics, and offices.

/pdf/human-activity-recognition-using-wearable-sensors-5dlg0ew3e3.pdf

Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning.

https://research.birmingham.ac.uk/portal/files/18560768/Mokbela_et_al_Metric_learning_for_sequences_Neurocomputing_2015.pdf

Metric learning for sequences in relational LVQ

This paper presents a novel approach to human chromosome classification. Human cell contains 22 pairs of autosomes and a pair of sex chromosomes. In this research, 22 types of autosomes represent 22 classes to be distinguished. New method of classification is based on the special organized committee of 462 simple perceptrons, called Competitive Neural Network Teams (CNNTs). Each perceptron is trained to differentiate two classes (i.e. two types of chromosome), hence there are 22 × 21 learning machines. Moreover, dummy perceptrons are set to zero for the chromosomes from the same class. The final outcome of the testing data is a 22×22 decision matrix, containing outcomes of each machine. With the special interpretation of these decisions, higher correct classification rate is achieved, reaching over 95%. The method can be further improved when testing is performed on a cell-by-cell basis by using CNNT complemented by Nearest Neighbor technique. The classification is applied to the Copenhagen chromosome data set and Sarajevo chromosome data set.

Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN's). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this study we represent a special organized committee of 462 simple perceptrons to improve the rate of correct classification of 22 types of human chromosomes. Each of these simple perceptrons is trained to distinguish between two types of chromosomes. When a new data is entered, the votes of these 462 simple perceptrons and additional 22 dummy perceptrons create a decision matrix of the size 22×22. By a special assembling of these votes we get a higher rate of correct classification of 22 types of human chromosomes.

/pdf/an-organized-committee-of-artificial-neural-networks-in-the-15035ggf7p.pdf

An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes

The healthcare services in developed and developing countries are critically important. The use of machine learning techniques in healthcare industry has a vital importance and increases rapidly. The corporations in healthcare sector need to take advantage of the machine learning techniques to obtain valuable data that could later be used to diagnose diseases at much earlier stages. In this study, a research is conducted with the purpose of discovering further use of the machine learning techniques in healthcare sector. Research was conducted by analyzing a well-established dataset called MHEALTH, comprising body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities. Dataset was analyzed using certain classification algorithms such as Multilayer Perceptron and Support Vector Machine, then results from these algorithms were compared to determine the most utile algorithm for analyzing such dataset. Study aims to determine irregularities using data from body motion and vital signs of volunteers, then these findings can be used either to diagnose particular diseases before they occur and avoid them. Results can also be used to monitor movements of ill or elderly people and observe whether they are doing any prohibited movements that would lead them to injuries or further illnesses.

/pdf/application-of-machine-learning-in-healthcare-analysis-on-466ypq0h2e.pdf

Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset

In this paper, a unified algorithm for extracting gray level profiles of Human chromosomes is presented. It is a unified approach since we do not discriminate chromosomes as straight and bended.  This is a very helpful procedure which extends the domain of success of most of the previously reported algorithms to highly curved chromosomes. The gray image of the chromosome is thresholded at the gray level 0.9, and the matrix of gray image is transformed into a list of pixel coordinates whose gray level is less than 0.9. To the list of two dimensional points, the most appropriate smooth curve is fitted. Then this smooth curve subdivided into n arcs of equal lengths, and straight lines are drawn that are normal to the curve at the end points of the subdivision. The points of the list are classified into n bins according to their distance to these n straight lines. The average of gray levels of each bin gives the gray levels at the points of the gray level profile of the chromosome. It is seen that the gray level profiles of the bended chromosomes have a high similarity with the straight counterparts.

Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

This paper is concerned with automatic segmentation of high resolution digitized metaphases. Firstly using a thresholding technique, a binary image of the cell picture is obtained. This binary image contains the addresses of darker pixels of the gray image of the colored cell picture. Several thousand of random points are assigned from among these addresses, and then using a distance condition, typically 50 pixels, and the number of centers is reduced to near 100. These points are search centers for chromosome segmentation.   Algorithm first searches eight pixels surrounding the center. Picks the coordinates of the pixels darker than the gray level 0.9, then passes to one of the pixels recently recorded as dark enough, and repeat the same procedure to the neighbors which are not visited before. If none of the new neighbors are not darker than 0.9, search reaches at the boundaries of the chromosome, and ends. Then we call the pixels of the chromosomes in the colored image from the addresses in the binary counterparts to finish segmentation.

/pdf/automatic-segmentation-of-human-chromosomes-d6z2wpywpp.pdf

Sadina Gagula-Palalic

Papers

Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes

Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset

Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

Automatic Segmentation of Human Chromosomes