Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

When Eagle Stares into The Eye

Retinal pigment epithelial detachment (PED) is the separation of the retinal pigment epithelium(RPE) from the Bruch’s membrane(BM). Eagle syndrome(ES) is characterized by an abnormally elongated styloid process with/without abnormal direction and/or ossification of the styloid ligament. Presence of both the above entities together is a rarest of rare sight. It may be a coincidence, or the diseases may have an association. Hence, further studies are warranted

Prevalence of female genital tract tuberculosis in suspected cases attending Gynecology OPD at tertiary centre by various diagnostic methods and comparative analysis

Background: The genital tract tuberculosis is one of the most common causes of tubal factor infertility. This study was conducted to compare the results of different diagnostic methods used in screening for female genital tuberculosis in suspected cases attending Gynecology OPD at RMC, Ajmer.Methods: This prospective study was conducted in department of obstetrics and gynecology, J. L. N. Medical College, Ajmer, Rajasthan, for studying incidence of genital tuberculosis by various diagnostic methods (viz. AFB smear examination, AFB Lowenstein Jensen culture method, TB-PCR and CBNAAT).Results: Prevalence of genital TB was 5.5% in study population of 200 selected women meting the inclusion criteria. 72% women were in between 20-30 years age group. Oligomenorrhoea (24%) was found to be significant symptom with P value of <0.05. TBPCR and CBNAAT were found to be statistically significant with P value of <0.001 for diagnosing FGTTB.Conclusions: We concluded that genital tuberculosis is paucibacillary disease, TBPCR and CBNAAT appears to be rapid and sensitive diagnostic modality

GESTURE RECOGNITION SYSTEM

In this paper, the hand gesture of a person is recognised and it identifies which hand of the person is raised. The skin colour is taken to recognise hands and face and the dark background is taken so that the skin detection may become easier. The hands and face are differentiated on the basis of area and centroid. Camera is the only input device used in this algorithm. No other input device is used to differentiate hands from the remaining body. This algorithm can be used both on the captured images and real time images

Deep learning for multi-resident activity recognition in ambient sensing smart homes

Advances in smart home technology and IoT devices has enabled us for monitoring of human activities for their health status and efficient energy consumption. Machine learning has been a great tool for the prediction of human activities. However, Multi-resident activity recognition is still a challenge as there is no direct correlation between sensor values and resident activities. In this paper, we have displayed the state of art deep learning algorithms on the real-world ARAS multi-resident dataset, which consists of data from two houses each with two residents. We have used different variations of RNN on the dataset and measured their performance with fewer data and more data and with data generated with GAN

ADAPTATION OF DOMAIN-SPECIFIC TRANSFORMER MODELS WITH TEXT OVERSAMPLING FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS ON COVID-19 VACCINE

Covid-19 has spread across the world and many different vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, this paper aims to fine-tune pre-trained transformer models on tweets associated with different Covid vaccines, specifically RoBERTa, XLNet and BERT which are recently introduced state-of-the-art bi-directional transformer models, and domain-specific transformer models BERTweet and CT-BERT that are pre-trained on Covid-19 tweets. We further explore the option of data augmentation by text oversampling using LMOTE to improve the accuracies of these models, specifically, for small sample datasets where there is an imbalanced class distribution among the positive, negative and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced, small sample datasets that are used to fine-tune state-of-the-art pre-trained transformer models, and the utility of having domain-specific transformer models for the classification task

Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines

Covid-19 has spread across the world and several vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, we fine-tune various state-of-the-art pre-trained transformer models on tweets associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art pre-trained transformer models RoBERTa, XLNet and BERT, and the domain-specific transformer models CT-BERT and BERTweet that are pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using Language Model based Oversampling Technique (LMOTE) to improve the accuracies of these models, specifically, for small sample datasets where there is an imbalanced class distribution among the positive, negative and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced small sample datasets that are used to fine-tune state-of-the-art pre-trained transformer models, and the utility of domain-specific transformer models for the classification task.Comment: The paper has been accepted for publication in Computer Science journal: http://journals.agh.edu.pl/csc

XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages

Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area