Speech to text translation enabling multilingualism.

Speech acts as a barrier to communication between two individuals and helps them in expressing their feelings, thoughts, emotions, and ideologies among each other. The process of establishing a communicational interaction between the machine and mankind is known as Natural Language processing. Speech recognition aids in translating the spoken language into text. We have come up with a Speech Recognition model that converts the speech data given by the user as an input into the text format in his desired language. This model is developed by adding Multilingual features to the existent Google Speech Recognition model based on some of the natural language processing principles. The goal of this research is to build a speech recognition model that even facilitates an illiterate person to easily communicate with the computer system in his regional language

Character recognition using tesseract enabling multilingualism.

Character recognition builds a recognizing factor for identifying the accuracy in characters. The accuracy of classifying the recognizing characters in an image is applied through deep learning methods. The character recognition is mainly focusing on the layers of text recognition through deep learning techniques. Well cleared python code assists to furnish all the levels of image by following deep learning that algorithmically analyse and recognize text from the given input image. This research work has been proposed for recognizing characters using deep learning techniques and recognize the input image with well-furnished and most efficient output. It provides a high level of accuracy-built output after the recognition of characters in the high-resolution image. This recognized character can be converted into user desired languages where the proposed model is trained to recognize some particular languages

Fabric variation and visualization using light dependent factor.

Typically, fabrics are recognised by human touch or feel. However, this study proposes an Internet of Things application that allows for the detection of fabric variation through light-based sensors

Cost-effective and efficient detection of autism from screening test data using light gradient boosting machine.

Autism spectrum disorder (ASD) is a developmental disorder that affects the brain. Autism constrains a person’s ability to interact and communicate with others. The cause of autism, in general, is unknown though genetics does play a role in the manifestation of the condition. In the absence of clear identifiable biomarkers, shortcomings of the available prognostic approaches create a need for a new technique that is speedy, cost-efficient, and provides an error-free diagnosis. The system should also be able to adapt to the varying characteristics of subjects with ASD. The amelioration machine learning brings to automated medical diagnosis which has inspired us to come up with a solution. An adept screening and diagnostic test for patients exhibiting known autistic symptoms is a well-compiled, specific, and approved questionnaire, which facilitates an easy and cheap diagnosis. Autistic Spectrum Disorder Screening Test data is collected from one such questionnaire. We used a combination of three publicly available datasets containing records related to ASD in children, adolescents, and adults. There are a total of 1100 instances along with 21 attributes. The proposed study uses a Light Gradient Boost (LGB) based model for classification, along with Random Search for hyperparameter optimization, which yielded a high accuracy of 95.82%

Stock price prognosticator using machine learning techniques.

Stock market price prediction is one of the favourite research topics under consideration for professionals from various fields like mathematics, statistics, history, finance, computer science engineering etc., as it requires a set of skills to predict variation of price of shares in a very volatile and challenging share market scenario. Share market trading is mostly dependent on sentiments of investors and other factors like economic policies, political changes, natural disasters etc., Many theories were forwarded, mathematical and statistical applications in conjunction with probability, to simplify the complex process. After the advent of computers, it got further simplified but still challenging due to various external influential factors ruling the volatility of the market prices. Thus, AI and ML algorithms were being developed, but for only for next day using Linear Regression procedures.Our project aims to predict the prices of shares more precisely and accurately using special algorithms using RNN by improvising the back propagation, feedback routines to overcome the short-term memory loss involved in RNN thus providing efficiency in LSTM applications.Our project emphasizes how the LSTM applications perform with datasets of extreme, larger and minimal fluctuating data

An analytical prediction of breast cancer using machine learning.

Breast cancer is one of the most frequent cancers among women, affecting about 2 million people. There is 98% chance of 5-years survival rate if detected at early stage. The data about breast cancer used in this paper is the Wisconsin dataset, which is taken from Kaggle. This is a classification problem; there are two classes (0 representing a non-malignant tumor, 1 representing malignancy). Min-max scalar is used for preprocessing of data, to limit data within certain range (known as scaling). The algorithms used for classification are support vector classifier, random forest, naïve Bayes, decision tree and k-nearest neighbours. Evaluation metrics - such as area under curve-rectified operational characteristics curve, confusion matrix, recall score - were used to determine accuracy. To avoid overfitting, cross validation is used where k fold value is 3. Support vector classifier and random forest gave the highest accuracy