In this paper,a new modeling approach is proposed by hybriding the features of expectation-maximization algorithm(GMM) and fuzzy c-means algorithm(FCM). Based on the analysis over conventional GMM technique, we suggested a new speaker identification system by fusing GMM (optimized using EM algorithm) and FCM, to improve the identification rate further in multilingual speaker identification system. The proposed technique and GMM technique was evaluated in mono and multilingual environments. Experiments were done also by varying the initial code books for generating speaker model. The experimental result shows improvements on a combined FGMM system, which employs fusion for the multilingual context with varying initial code books gives an improvement of minimum 2.98% than existing GMM approach. MFCC technique is used for extracting the features. The algorithms were compared using TIMIT database of 54 speakers speaking 3 languages like English, Hindi and Tamil.
Researchers face many challenges in finding the opt web-based resources by giving the queries based on keyword search. Due to advent of Internet, there are huge biological literatures that are deposited in the medical database repository in recent years. Nowadays, as many web-based medical researchers evolved in the field of medicine, there is need for an intelligent and efficient extraction technique required to filter appropriate and opt literature from the growing body of biomedical literature repository. In this research work, new combination of model is proposed in order to find the new insights in applying the combination of algorithm on biological data set. The information in the biomedical field is the basic information for healthy living. National Center for Biotechnology Information (NCBI)'s PubMed is the major source of peer-reviewed biomedical documents for researchers and health practitioners in the field of health-related management. In this paper, abstracts available in PubMed database is used for experimentation. In recent years, deep learning-based neural approach models provide an efficient way to create an end-to-end model that can accurately measure classification labels. This research work is a systematic analysis of performance of the supervised learning models such as Naïve Bayes (NB), support vector machine (SVM) and long short-term memory (LSTM) by implementing on textual medical data. The novelty in this work is the process of incorporating certain topic modelling techniques after the pre-processing phase to automatically label the documents. Topic modelling is a useful technique in increasing the efficiency and improves the ability of researchers to interpret biological information. So, the classification algorithms thus proposed are implemented in combination with popular topic modelling algorithms such as latent Dirichlet algorithm (LDA) and non-negative matrix factorization (NMF). The final performance of the combination of algorithms is also analysed and is found that SVM with NMF outperforms the other models.
For the fight against obesity, precise food and energy intake measurement techniques are essential. One of the most important lessons for long-term prevention and effective treatment programmes is the provision of users and patients with practical and intelligent solutions that assist them in measuring their food intake and gathering dietary information. In this article, we suggest a calorie measurement technique to assist patients and medical professionals in their battle against dietrelated illnesses. In this document, we suggest a food identification system that, when given the appropriate quantity of data, can assist a user in keeping track of daily caloric consumption. Calorie estimation for the current method must be done by hand. The proposed model will use a deep learning algorithm to offer a special method of calculating calories. In the world of medicine, calorie calculations for food are crucial. because the calories in this food are beneficial to your health. This measurement is derived from photographs of various foods, including fruits and vegetables. Our suggested solution relies on cell phones, which enable the user to take a picture of the food and instantly calculate the number of calories consumed. We classify food photos for system training using deep convolutional neural networks to reliably identify the food in the system. In this study, we use a convolutional neural network (CNN) to detect and identify images of food. Given the huge range of food types, picture recognition of food products is frequently very difficult. Whatever the case, deep learning has recently been shown to be an incredibly innovative image identification approach, and CNN is the greatest way to use deep learning.
Falling losses from aquatic disease is the main public condition goal in developed countries. The present study terms the application of Physical Sciences and Environmental Sciences techniques to solve the real-life problem associated with the society. Water samples are taken from a river, pond, bore well, and municipality at Kabisthalam in Kumbakonam Taluk, Tanjore District, Tamil Nadu, South India. The current work defines the expansion of mathematical typical to expect the interaction parameters which were used to determine the quantitative characteristics of the water. Analysis of Variance (ANOVA) was employed to identify the level of importance in interaction parameters on their characteristics. The effect of these parameters on interaction has been investigated using experimental designs. The new results have been correlated using the Fuzzy Evidence Theory. The salient aspect of the work is that a very simplified analytic output of the fuzzy model is achieved when all fuzzy sets in the fuzzy partition of the output space have the same power (the area under the membership function), and the determination of basic probability assignments associated with fuzzy Dempster-Shafer belief structure using fuzzy focal elements.
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