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 - Concepts and Techniques.

Biochemical systems analysis: A study of function and design in molecular biology

象(Elephas Maximus)の胆汁成分について

Women who have recovered from breast cancer (BC) always fear its recurrence. The fact that they have endured the painstaking treatment makes recurrence their greatest fear. However, with current advancements in technology, early recurrence prediction can help patients receive treatment earlier. The availability of extensive data and advanced methods make accurate and fast prediction possible. This research aims to compare the accuracy of a few existing data mining algorithms in predicting BC recurrence. It embeds a particle swarm optimization as feature selection into three renowned classifiers, namely, naive Bayes, K-nearest neighbor, and fast decision tree learner, with the objective of increasing the accuracy level of the prediction model.

Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction

In this paper, a Modified Exact Reconstruction Algorithm for Microwave Tomography has been proposed and applied on a diseased semi-human-sized biological target. An Exact Algorithm already developed for imaging has been used on a semi-human-sized normal biological model and, thereby, a reconstructed image on the basis of complex permittivities of the cells is obtained with an average accuracy of (90%-99%) for different regions. When a particular portion of the model is affected by some diseases, those cells are characterized by different values of complex permittivity from the normal ones. But the result provided by the Exact Algorithm is not very accurate for medical diagnosis when applied on such diseased model. It creates suspicion only about any disease on that portion. The aforesaid algorithm is, therefore, modified by considering the deviations in electric fields in all cells on account of change of permittivity in the diseased region. The image is reconstructed iteratively only on clinically suspected region keeping the values of complex permittivity in other cells the same as in its normal states. The reconstructed image thus obtained by using Modified Exact Algorithm is supposed to have sufficient accuracy of 95% for the diagnosis in medical field and may be helpful for detection of disease in human body in future.

A Modified Exact Reconstruction Algorithm for Microwave Tomography for Detection of Disease in Human Body

Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers. Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN). Initially the proposed method is tested against small artificial network without any noise and the efficiency is observed in term of number of iteration, number of population and BA optimization parameters. The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset. In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. coli. The results prove that it can able to identify the maximum number of true positive regulation but also include some false positive regulations. Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model.

Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm

In this paper an Exact Simultaneous Iterative Reconstruction Algorithm is developed and applied on a large semi human size normal biological model and a diseased model (liver region affected) to verify the efficiency of the algorithm. The algorithm is successfully reconstructed the normal model having 15%-20% perturbation i.e. change in permittivity during disease. In diseased case, reconstructed imaginary part of complex permittivity clearly detects the affected zone and it may help the medical diagnosis. Hence it may be a powerful tool for early detection of cancerous tumors as the interrogating wave is a noninvasive one at the ultra high frequency range. The resolution of this system is increased with the reduction of wavelength by immersing the antenna system and the model in saline water region. The advantage of this algorithm is that the calculation of cofactor are done offline to save the computational time and cofactors are expressed as a function of distances irrespective of their positions .

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Tomography of human body using exact simultaneous iterative reconstruction algorithm

Current progress in cellular biology and bioinformatics allow researchers to get a distinct picture of the complex biochemical processes those occur within a cell of the human body and remain as the cause for many diseases. Therefore, this technology opened up a new door to the researchers of computer science as well as to biologists to work together to investigate the causes of a disease. One of the greatest challenges of the post-genomic era is the investigation and inference of the regulatory interactions or dependencies between genes from the microarray data. Here, a new methodology has been devised for investigating the genetic interactions among genes from temporal gene expression data by combining the features of Neural Network and Cuckoo Search optimization. The developed technique has been applied on the real-world microarray dataset of Lung Adenocarcinoma for detection of genes which may be directly responsible for the cause of Lung Adenocarcinoma.

Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid

Due to the outbreak of the pandemic COVID-19 or ‘novel corona virus disease’, the world is facing a global emergency. In case of severe infection, lungs are affected by COVID-19 significantly, whichmay lead to the death of the patient. In this paper, an automated image-assisted system based on artificial intelligence is proposed to extract infected sections from lung CT scan images that are caused due to COVID-19. Multilevel thresholding is a typical example of maximization problem of optimization to identify the threshold(s) levels for image segmentation. In this paper, Elephant Swarm Water Search Algorithm (ESWSA) has been used for multilevel thresholding based on Otsu’s and Kapur’s method and further analysis of lungs images. It has been observed from the obtained simulated results that ESWSA performs better than other state-of-the-art optimization techniques for multilevel thresholding. Moreover, location of infection and severity of infection has also been extracted from the pixel ratio between the infection and lung sections from the lung CT scan images. It is expected that the proposed methodology will support the doctors as it will reduce diagnostic burden and respective treatment process can be planned faster as the severity of infection by COVID-19 is found by this automated methodology. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Modelling & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sudip Mandal

Papers

A Modified Exact Reconstruction Algorithm for Microwave Tomography for Detection of Disease in Human Body

Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm

Tomography of human body using exact simultaneous iterative reconstruction algorithm

Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid

Identification of Severity of Infection for COVID-19 Affected Lungs Images using Elephant Swarm Water Search Algorithm