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

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

Objective: Sophora interrupta Bedd (Fabaceae) is one of the well-known medicinal herb used in folk medicine for the treatment of cancer and inflammatory associated diseases. In this paper we aimed to evaluate the antioxidant and anti-cancer, properties of aqueous, methanol and n-hexane leaf extracts. Methods: The leaf extracts were analyzed for antioxidant activity using DPPH free radical scavenging assay and anticancer activity by measuring the cell viability using MCF-7 and PC-3 cancer cell lines. GC-MS analysis was performed to identify anti-cancer compounds present in the active leaf extract. Results: The polar solvent extracts showed good antioxidant at 500 μg/ml as related to non-polar solvents. Moreover, methanol extract exhibited highest percentage of cell death, in both MCF-7 (IC 50 500 μg/ml) and PC-3 cells (IC 50 1000 μg/ml), which is also evident from morphological observations, acridine orange and ethidium bromide dye exclusion assays. Conclusion: Over all, it suggests that leaf methanol extract contain anticancer compounds, which are evident from our GC-MS analysis.

Evaluation of in vitroanticancer activity and gc-ms analysis from leaf sophora interrupta bedd

Structure‐cytotoxicity relationship for apoptotic inducers organotin(IV) derivatives of mandelic acid and L‐proline and their mixed ligand complexes having enhanced cytotoxicity

Progesterone is a major female steroid hormone produced by the ovarian corpus luteum and by the placental syncytiotrophoblast during the second trimester. The biological effects of this steroid hormone are mediated by the ubiquitously expressed progesterone receptor. The exact link between progesterone and female reproductive organ cancer is a controversial issue with various cross-talks. The present review summarizes recent trends in the development of some (anti)progestagen in the cure and management of breast and uterine cancers.

Progesterone receptor agonists and antagonists as anticancer agents.

Text recognition from a natural scene and video is challenging compared to that in scanned document images. This is due to the problems of text on different sources of various styles, font variation, font size variations, background variations, etc. There are approaches for word segmentation from video and scene images to feed the word image into OCRs. Nevertheless, such methods often fail to yield satisfactory results in recognition. Therefore, in this paper, we propose to combine Hidden Markov Model (HMM) and Convolutional Neural Network (CNN) to achieve good recognition rate. Sequential gradient features with HMM help to find character alignment of a word. Later the character alignments are verified by Convolutional Neural network (CNN). The approach is tested on both video and scene data to show the effectiveness of the proposed approach. The results are found encouraging.

Word recognition in natural scene and video images using Hidden Markov Model

Automatic signature segmentation from a printed document is a challenging task due to the nature of handwriting of the signatory, overlapping/touching of signature strokes with printed text, graphics, noise, etc. In this paper, we propose a two-stage approach for signature segmentation from a document page. In the first stage, a document is segmented into blocks and then blocks are classified into two classes: signature block and printed word block. Gradient-based features are used for block feature extraction and support vector machine classifier is used for block-wise classification. In the second stage, printed characters that may be present in isolated form or overlapped/touched with signature part are removed from signature blocks. From each of the detected signature blocks, the isolated printed characters (if exist) are removed using context information. To detect overlapping/touching printed stroke in a signature block, at first some hypothetical zones are detected where possible overlapping/touching may occur. Bounding box information of neighboring printed word block and local linearity of character strings near the signature blocks are used to detect hypothetical zones. Next, to detect the overlapping/touching printed strokes in hypothetical zones of a signature block, the corner points of contours obtained by Douglas and Peucker polygonal approximation algorithm and skeleton junction points are used. Finally, the touching strokes of signature are separated from text characters using the contour smoothness information near skeleton junction points. The experiment is performed in "Tobacco-800" dataset [The legacy tobacco document library (ltdl), available at http://legacy.library.ucsf.edu/, University of California, San Francisco, 2007.] and the results obtained from the experiment are promising.

Partha Pratim Roy

Papers

Evaluation of in vitroanticancer activity and gc-ms analysis from leaf sophora interrupta bedd

Structure‐cytotoxicity relationship for apoptotic inducers organotin(IV) derivatives of mandelic acid and L‐proline and their mixed ligand complexes having enhanced cytotoxicity

Progesterone receptor agonists and antagonists as anticancer agents.

Word recognition in natural scene and video images using Hidden Markov Model

Signature segmentation from machine printed documents using contextual information