Other affiliations: Bengal College of Engineering & Technology, Jadavpur University, St. Joseph's College of Engineering ...read more
Bio: Nilanjan Dey is an academic researcher from Techno India College of Technology. The author has contributed to research in topics: Image segmentation & Thresholding. The author has an hindex of 48, co-authored 475 publications receiving 9160 citations. Previous affiliations of Nilanjan Dey include Bengal College of Engineering & Technology & Jadavpur University.
TL;DR: A particle swarm optimization-based approach to train the NN (NN-PSO), capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistory reinforced concrete building structure in the future.
Abstract: Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.
TL;DR: The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy on the considered image dataset compared with the alternatives.
Abstract: Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives.
10 Apr 2020
TL;DR: This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques and provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
Abstract: COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
TL;DR: The paper concludes that such mass-market health monitoring systems will only be prevalent when implemented together with home environmental monitoring and control systems.
Abstract: Wireless technology development has increased rapidly due to it’s convenience and cost effectiveness compared to wired applications, particularly considering the advantages offered by Wireless Sensor Network (WSN) based applications. Such applications exist in several domains including healthcare, medical, industrial and home automation. In the present study, a home-based wireless ECG monitoring system using Zigbee technology is considered. Such systems can be useful for monitoring people in their own home as well as for periodic monitoring by physicians for appropriate healthcare, allowing people to live in their home for longer. Health monitoring systems can continuously monitor many physiological signals and offer further analysis and interpretation. The characteristics and drawbacks of these systems may affect the wearer’s mobility during monitoring the vital signs. Real-time monitoring systems record, measure, and monitor the heart electrical activity while maintaining the consumer’s comfort. Zigbee devices can offer low-power, small size, and a low-cost suitable solution for monitoring the ECG signal in the home, but such systems are often designed in isolation, with no consideration of existing home control networks and smart home solutions. The present study offers a state of the art review and then introduces the main concepts and contents of the wireless ECG monitoring systems. In addition, models of the ECG signal and the power consumption formulas are highlighted. Challenges and future perspectives are also reported. The paper concludes that such mass-market health monitoring systems will only be prevalent when implemented together with home environmental monitoring and control systems.
TL;DR: This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics.
Abstract: The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: 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.
01 Jan 2002
01 Jan 1997
TL;DR: This new edition of Ann Bowling's well-known and highly respected text is a comprehensive, easy to read, guide to the range of methods used to study and evaluate health and health services.
Abstract: This new edition of Ann Bowling's well-known and highly respected text has been thoroughly revised and updated to reflect key methodological developments in health research. It is a comprehensive, easy to read, guide to the range of methods used to study and evaluate health and health services. It describes the concepts and methods used by the main disciplines involved in health research, including: demography, epidemiology, health economics, psychology and sociology.The research methods described cover the assessment of health needs, morbidity and mortality trends and rates, costing health services, sampling for survey research, cross-sectional and longitudinal survey design, experimental methods and techniques of group assignment, questionnaire design, interviewing techniques, coding and analysis of quantitative data, methods and analysis of qualitative observational studies, and types of unstructured interviewing. With new material on topics such as cluster randomization, utility analyses, patients' preferences, and perception of risk, the text is aimed at students and researchers of health and health services. It has also been designed for health professionals and policy makers who have responsibility for applying research findings in practice, and who need to know how to judge the value of that research.