Reviewerly/exHarmony huggingface dataset:
Kannada Textual Error Correction Using T5 Model

Error creeps into text in various ways. Typing error may come due to either mis-typing or due to poor language expertise. Similarly, recognition technologies while converting textual images and speech into text may generate error due to their limitations. Irrespective of the channel of error induction, presence of error poses a huge challenge for downstream consumption of such textual content. Additionally, error present in Indian language textual documents come with their own set of issues. This necessitates focused study on the textual errors in Indian language documents and the various technologies which may be employed to eliminate them.This work proposes to employ mT5, a very popular deep learning based multi-lingual language model, to eliminate errors present in Kannada, an Indian Language, text. A pretrained model of mT5 is enhanced with transfer learning for a Kannada dataset. The ability of the enhanced mT5 model to reduce error is studied at various levels of noise. Character Error Rate (CER) is employed as the metric. It’s observed that the enhanced mT5 model is effectively able to reduce noise by 12% for input text with 25% CER.
Resume Analysis and Job Recommendation

A job seeker always spends hours searching through the massive amount of recruiting information on the Internet to identify ones that are helpful. The field of job recommender systems for recruiting has just evolved and experienced rapid growth. The job recommender systems view user profiles and design recommendation technologies that have drawn interest, been studied in academia, and been put into practice for some use cases in industries. This study creates and puts into practice a recommendation system for online job searching to lessen this tedious task. In this study, an architecture has been proposed to extract the most suitable professions based on the resume of the individual. Natural Language Processing and Machine learning are used to train the model that predicts the profession. Then using this profession as a key, jobs are web scraped from Naukri.com. The required skills of the jobs are matched with the individual's skills using a cosine similarity algorithm and are ranked and displayed to the user.
Handwritten Character Recognition of Telugu Characters

Given the cursive structure of the writing and the similarity in shape of the letters, Telugu handwritten character identification is an interesting topic. The lack of Telugu-related handwritten datasets has slowed the development of handwritten word recognizers and forced researchers to compare various approaches. Modern deep neural networks find it difficult because they often need hundreds or thousands of photos per class. It has been demonstrated that learning important aspects of machine learning systems can be computationally expensive and challenging when there is a limited amount of data available. This research analysis work proposes a use case on the pre-existing model called EfficientNet and on top of that a custom pooling layer is added to check the trend as the dataset size increases of Telugu characters. The dataset has been divided into three categories, namely, Vowels only dataset, Consonant only dataset, and All character dataset. Proposed model was trained with a considerable amount of dataset containing half a thousand of handwritten Telugu characters and has produced some fascinating results which were worth observing. The accuracies had followed a certain trend. The model was tested on the dataset collected, which were filtered out to record any performance improvement and improvement was observed, where average accuracy went from 55% to 92%.
Automatic Text Summarization For Conversational Chatbot

The communication between humans and machines has benefited from recent developments in speech recognition and natural language understanding. As personal assistants and chatbots are slowly becoming a part of our daily life, we develop a conversational chatbot that can conduct text summarization utilising innovative NLP approaches. Text Summarization is a useful technique for making better use of the vast amount of material that is readily available to us on the Internet and in various archives. So, under the current situation, generating a summary is both necessary and advantageous. The aim of this paper is to review the existing text summarization algorithms do a comparative analysis and integrate them to a conversational chatbot. For generating the summary, we have used six different algorithms: LexRank, TextRank, Latent Semantic Analysis (LSA), T5, BART and PEGASUS. These create useful summaries after reviewing the most recent literature. The performance of these algorithms is compared using the BBC News Dataset, a publicly available dataset, and their results are evaluated using the RecallOriented Understudy for Gisting Evaluation (ROUGE) metric to perform analysis, choose the best algorithm among these, produce the summary, and integrate it to the chatbot. By providing succinct responses to questions in a fraction of the time it would take to read the entire document, this chatbot significantly enhances the user experience.
Convolutional Networks for Skeleton-Based Gesture Recognition Using Spatial Temporal Graphs

In the recent years, recognition of human actions and the interactions of human body bones provide crucial data. It has been applied in many fields from video intelligence to computer vision. The idea behind working of these have a common approach of using deep learning methods that include Convolutional Networks. The Graph convolution networks (GCN) is extensively used in recognition of skeleton action-based data. We point out that current GCN-based methods generally rely on specified graphical patterns (i.e., a hand-crafted structure of the joints in the skeleton), which hinders their potential to gather intricate connections between joints. Thus a better advanced model can be proposed out of the GCN-based model. This paper aims in delivering a novel model of Spatial Temporal Graph Convolutional Networks (ST-GCN) are interactive skeletons that learn from the spatial and temporal variability of input data(ST-GCN) [1]. We here use a large dataset –Kinetics to perform the analysis and predict the output for given skeletal data.
Sentiment Analysis on Indonesia-English Code-Mixed Data

Social media users nowadays tend to use code-mixed language to express their opinion. The users of social media has exponentially risen in some countries like Indonesia, it has given rise to large volumes of code-mixed data, in which users use more than one language in a single text. Data with code-mixed is often noisy and most importantly, the monolingual model usually does not work well on it. This has been a challenge for Natural Language Processing (NLP) for processing and analyzing the data. In this work, we conduct experiment of sentiment analysis on English-Indonesian code-mixed data. The approach that is by utilizing a multilingual pre-trained model, mBERT. By analyzing the sentiment analysis models’ predictions, we may assess how effectively the model can adjust to the implicit noises inherent in code-mixed data. The classification model’s performance was tested using batch size and epochs parameters to discover and obtain the highest accuracy. The experimental result shows that the highest accuracy we obtained from the mBERT model that is trained with our dataset obtained was 76%, with 16 batch size and epochs used 7.
Facial Key points Detection using MobileNetV2 Architecture

With the popularity of social media like Instagram and Snapchat, facial filters or beautifying filters are used more often. These applications cannot store raw images of faces when used every time. Thus, they need unique characteristics of a face to which the filters can be applied. These unique characteristic points of every face are called facial key points. With the increasing use of such applications, facial key point detection has become a popular topic. The objective of key point detection is to extract the coordinates of the unique points in the face which are necessary and sufficient to detect the image. Every person has a different face and the key point coordinate for each is very different from the other. Thus, detecting the key points becomes a difficult task. The detection becomes more challenging depending on the angle with which the image is taken and the light exposure of the image. In this paper, we propose the use of deep learning architecture, MobileNet version2 (MobileNetV2) [26] to solve this complex problem as it proves to be better than the traditional architectures available. Our aim is to detect 15 key points of the given facial image using CNN with MobileNetv2 architecture to obtain lower loss and better accuracy. The baseline model used is a single hidden layer neural network and convolutional the advanced model is a Convolutional Neural Network with MobileNetV2 architecture. The experimental results have shown 84% accuracy as compared to the present state-of-art algorithms.
Empirical evaluation of Amazon fine food reviews using Text Mining

Approximately 1.6 million individuals use the e-commerce website “amazon” to buy things from a variety of categories, including food. Reviewing products by consumers who have already purchased them is beneficial to those who are considering doing so, however reviews can be either positive or negative. The buyer finds it difficult to read through such many evaluations before making a purchase, but machine learning ideas and training models make it possible. Our objective is to categorize the reviews based on the attributes that are present in the dataset in order to address issues like these. Redundancy is present in data when it is presented to us in its raw form. So, since evaluations with a score of 3 are regarded as impartial, we delete them along with redundancy. After that, we use the NLP tool kit (a column in the data set) to preprocess the text by removing any stop words (such as in, as, is, on, and punctuation), and we lowercase each letter. The suggested approach renders the text into machine-understandable language using word embedding techniques. Text processing is necessary because customer reviews written in language that is understood by humans cannot be read by machines. The data must be in a machine-readable language in order to apply any classification technique. We separate the data into train and test set after the preprocessing is complete. After the training is complete, we use this model on a test set of data to determine its accuracy. Next, we utilize classification methods like logistic regression and XG Boost to see how accurate our model is. This study’s conclusion involves using the model we developed to predict the review based on previous reviews. In this project, we build a model, feed it with existing reviews, apply it to upcoming reviews, and then forecast if the product is good or not. For this work we have taken the data set from Kaggle.
A Novel Approach for Sentiment Analysis on social media using BERT &amp; ROBERTA Transformer-Based Models

Sentiment analysis on social media platforms has gained immense popularity in recent years due to its ability to analyze and classify users’ emotions and opinions on different topics. This paper proposes a sentiment analysis model for Twitter that utilizes two powerful transformer models BERT and ROBERTA, to classify tweets based on their sentiment. The use of BERT and ROBERTA transformers offers several advantages over traditional sentiment analysis models. These transformers can capture contextual information and understand the relationships between words in a sentence, making them well-suited for the nuanced language used in social media platforms like Twitter. To train the model, transfer learning is employed, allowing the transformers to learn from a vast dataset of tweets before fine-tuning on the sentiment analysis task. This approach enables the model to understand the nuances of informal language and slang used in tweets, improving its ability to accurately classify sentiment. Another advantage of the proposed model is its ability to handle the informal language used in tweets. However, BERT and ROBERTA transformers have been trained on a vast corpus of informal language, enabling them to accurately analyze and classify sentiment in tweets. Lastly, the proposed model’s scalability and efficiency are also advantages. This model can handle large volumes of data with high efficiency and can be fine-tuned to adapt to new domains with minimal training data, making it highly versatile and applicable in various applications.
Bangla Sign Language Recognition using YOLOv5

Bangla Sign Language (BdSL), also known as Bengali or Bangladeshi Sign Language, is the sign language that is commonly used for communication with the deaf people in Bangladesh and some parts of India, e.g., West Bengal (formally known as Calcutta), Assam, Tripura, and the Andaman and Nicobar Islands. Bangla is one of the sweetest languages in the world, according to the UNESCO survey, and the 7th most spoken language in the world. In Bangladesh, about 13.7 million people have a hearing problem, and the Bangladesh National Federation of the Deaf (BNFD) was established on December 25, 1963, to provide training, skill development, and education to deaf people. In this study, we have applied transfer learning in machine learning for BdSL recognition that reuses a pre-trained classifier named YOLOv5 to detect and classify BdSL. Transfer learning speeds up the training process with a minimum number of instances and improves classification performance. Transfer learning for deep learning has become very popular for solving computer vision and natural language processing (NLP) tasks nowadays. Initially, we collected a 24.2 GB video of BdSL from SignBD-word and extracted images from the video using the FFmpeg multimedia framework. Then, we annotated the images using the LalelImg image annotation tool. Next, we checked the image annotation and built the model using YOLOv5. YOLOv5 is a family of compound-scaled object detection models, which stands for "You Only Look Once", commonly used for detecting objects by separating images into a grid structure. Finally, we have implemented the prototype with an average accuracy of 91.62%.
Analysis for Determining Best Machine learning Algorithm for Classification of Heart Diseases

Numerous data points are generated by the healthcare sector and processed using certain procedures. There are many methods for processing a data among which data mining is one of the methods frequently employed. Heart condition is the main cause of death in the globe. This project determines the best algorithm for the system that anticipates the possibility of cardiac disease. The outcomes of this system provide the likelihood in percentage of acquiring heart disease. The datasets are categorised using medical parameters. To analyse such factors, our system employs a data mining classification method. The datasets are analysed using Naïve Bayes, Logistic Regression, Random Forest, K-Nearest Neighbour, XGboost, Decision Tree and Support Vector Machine, Machine learning algorithms with hybrid Classifiers and Neural Network.
Vocals - An App for Vocally Impaired using NLP Conversational Model

Conversations between humans and machines have become an increasingly common occurrence in today’s digital age. As technology progresses, automated systems are becoming more adept at comprehending and reacting to human language through advancements in language analysis and computational linguistics. While this has brought many benefits, such as improved customer service and faster access to information, there are also some potential drawbacks to consider. Though technology is advancing rapidly, they still lack in many aspects such as understanding human emotions, intentions, empathy, flexibility, privacy and security. The main challenge here is how the machine responds relevantly and quickly in a continuous conversation with humans. This research is all about developing an android application that helps vocally impaired people to book appointments easily using a bidirectional LSTM model, text-to-speech and speech-to-text to have a natural conversation with human.
A Semantic Approach for Automated Hiring using Artificial Intelligence &amp; Computer Vision

This research paper presents an automated interview system that utilizes natural language processing and deep learning techniques to conduct job interviews. The system comprises 4 modules, the first one being resume classification system, which is built on state-of-the-art NLP architecture and transformer-based BERT model. The model uses Named Entity Recognition and Text Classification to extract relevant information from the resume. The resume classification algorithm is based on the principle of context relevance, and skill mentions in the resumes are validated through an analysis of the candidate's experience and completed projects before being classified. The subsequent module utilizes the output from the resume model to generate a set of relevant interview questions for the candidate through utilizing natural language processing techniques and semantic analysis. The difficulty of the following questions is adjusted owing to the interviewee’s answer, very much like what human HRs do. Such questions are extracted with the help of web scraping by selenium driver. One of the most innovative parts of this system is its ability to reduce bias in the hiring process by using machine learning models to evaluate candidate responses. The following module assesses the candidate's personality during the interview using the CNN and LSTM models. In addition to generating relevant interview questions, the system also detects potential instances of misconduct or malpractice during the interview process using Open CV’S DNN module and a quantized model. Overall, this system has the potential to revolutionize the way companies conduct job interviews and improve the hiring process.
Sentiment Analysis of Hotel Reviews - a Comparative Study

Sentiment analysis is an important domain in Natural Language Processing (NLP) since it is an efficient way to extract features and user sentiments from textual data. Performing sentiment analysis of big data in the tourism industry is useful for businesses to understand the needs of their customers and improve hotel facilities to increase customer satisfaction. This paper aims to compare, analyze and employ different types of supervised, unsupervised, and pre-trained models. The supervised models - Decision Trees, XGBoost, Multinomial Naïve Bayes, Multinomial Logistic Regression, SVM, and Stochastic Gradient Descent were tested and the parameters were optimised using GridSearchCV. Two unsupervised models, K-means clustering and Latent Dirichlet Allocation were implemented with TF-IDF and Word2Vec embeddings. The pre-trained models, VADER and TextBlob were also implemented. The labelled dataset used for this study contains user reviews of hotels around the world, where each review is classified as positive, neutral, or negative. The SVM model resulted in the highest weighted F1 score of 0.8516.
CNN-MFCC Model for Speaker Recognition using Emotive Speech

Finding the appropriate speaker using voice recognition is called "speaker recognition." Emotive Environment Speaker Recognition (EESR) identifies speakers using distinct emotional speech. A real-life situation that becomes a requirement for many applications is speaker recognition, which utilizes various moods. If there is no emotion in the conversation, speaker recognition algorithms work almost flawlessly. This work aims to improve the accuracy of text-dependent and emotional speaker recognition system in emotional speech contexts. The proposed method is developed using Mel-Frequency Cepstral Coefficient (MFCC) feature and the classifier considered is Convolutional Neural Networks (CNN) for various emotions. The suggested system's performance is assessed based on emotional datasets from the Kannada Language and Emotional Database (EmoDB). These emotions are present in both datasets: happy, sad, angry, fear, and neutral. Due to the complexity of emotions, speaker recognition in various emotional states is challenging. The proposed system offers an accuracy of 96.2% in the EmoDB and 97.8% in the Kannada dataset. The proposed method provides a high recognition rate for different emotions.
Sentiment Analysis on Indonesian-Sundanese Code-Mixed Data

In this work, we conduct sentiment analysis on Indonesian-Sundanese code-mixed tweets. Sundanese is one of Indonesia’s regional languages with over 42.000.000 speakers. We use a pre-trained language model, IndoBERT, to tackle the sentiment analysis task. Our evaluation result shows that the best accuracy is 81%. We analyze the errors and find that most mislabeled tweets are because the words on the wrongly predicted tweet contain many words from other labels. It is also possible that it happens since the sentence in the tweet is ambiguous, the words used in the tweet are unavailable in the training data set, or the use of abbreviated words in the tweet.
Machine Translation using Dictionary based Techniques

Translating words and sentences is a major challenge, let alone a whole book. Tradition-ally, publishers used to manually translate a whole book. As computers and Internet came into being, it was used for translating words and sentences from one language to other using various methods. Dictionary based techniques are one of the oldest machine translation techniques. Mod-ern day apps use statistical machine translation techniques along with neural networks and/or natural language processing methods, where there is a high chance of error as they focus more on the language diversity. The major mo-tivation of this research work is to improve the English learning skills of ru-ral South Indian school students. As per a survey conducted by the authors, the rural students lack in command over English, due to the difficulty in translation services. The authors created a dataset consisting of English words from Tamil Nadu State textbooks of classes up to grade 10, to execute the translation. In this paper, we have used dictionary mapping, such as lin-ear, binary and Trie search methods, for performing word translation from English to Tamil, and compared them in different conditions to identify the best one. It is observed that the Binary search method performs better in all cases and hence it was selected to be implemented in the translation app. It is then clubbed with natural lan-guage processing (NLP) techniques and Rule Based Machine Translation (RBMT) techniques to carry out the trans-lation of a whole sentence. This whole technique is integrat-ed into an app, which is intended for students who are not fluent in English and requires an assistance.