Reviewerly/exHarmony huggingface dataset:
Detection of Reviews and Comments using Machine Learning

The modern internet and different sources are the best resources for knowledge, inspiration, and product or service reviews. Millions of comments about a product, a person, or a location are made every day in a variety of settings or sources. It is exceedingly challenging to manage and comprehend such comments due to their enormous volume and size. The user's viewpoint is always expressed in textual form. To identify whether the opinion's tone is favourable, unfavourable, or neutral, artificial intelligence and text analytics are applied. The feedback is gathered from an internet source, then analysed and saved in a databases. Those comments are identified and classified whether it was user keyword related post using naive bayes algorithm. The phrase “Naive Bayes Classification” comes from the premise that each word is statistically independent of the others. The user keywords can be predicted whether it is a best suggestion using polarity. To develop an interactive autom system that can anticipate the emotion of comments. This system talks about the challenges that can come u during sentiment analysis. Comments are taken into consideration because they offer rich data sources for sentiment analysis and opinion mining. The main objective of this system is to analyse gathered comments for emotional performance in real-time and offer time-based statistics to the user via an automated teller machine.
Origin-The Transcoder

ORIGIN-Transcoder is a tool that translates the given code from a source language to a destination language and is also known as the end-to-end translator. It assists the software industry by converting the software code from an old or inflexible language to a modern and more flexible language. Studies suggest that developers are seeking transcoders to make their job easier and these transcoders save a lot of time and money. In the existing system, the Transcoders are based on a Rule-Based Algorithm. Since Rule-Based Algorithms require manual correction and expertise in the programming languages, we propose a solution by using a Neural-Based Algorithm. If an employee wants to convert a particular set of code from one language to another, he/she can copy the code and paste it into our software or just take a snap of the code. The code then is extracted by the method of Optical Character Recognition and categorized by Topic Modelling Algorithm. Tokenization is applied to the categorized text to eliminate the punctuation characters. Finally, Recurrent Neural Network gets the desired final code through our Web, Mobile or Desktop Application. Our technique demands less expertise in the targeted programming languages, requires far fewer manual tweaks and is much more efficient than Rule-Based Algorithms.
An Application For Effective Usage Of Social Platforms

As in this fast-moving world, social media has become an essential part of our day to day lives. Social media is one of the effective ways through which the people all around the world connect with each other. One is able to know what is happening at the other side of the world or in the space within minutes. Much as it helps with attaining one's goals, that much it can also distract an individual from their goals. This application is proposed for the effective usage of social media platforms. The prime advantage of our platform is that it maintains the records for everything and it has three hubs[first stage of deployment] namely music hub, dance hub and reels hub at the top most corner of the feed. The app aims only at the productivity of the user, so that the user can either share their talents or support other's talents. The app gives an information about the planet or history or anything valid when the user logs in and then asks a question related to the information given prior before exiting.
An Overview on Nature- Inspired Learning Approaches for Sentiment Analysis Applications

In recent years, one of the most interested domains of research is Natural Language Processing (NLP). The most prevalent application of NLP is Sentiment Analysis. The data on the users' attitude or opinions is mined to get meaningful insights, applying various techniques. Most of the past research in sentiment analysis were carried out with lexicon based and supervised/unsupervised machine learning algorithms. Inspired with the nature as the significant source of motivation, various Nature-inspired learning algorithms have become the recent research topic to explore new algorithms and to optimize the results of the other algorithms as well. This paper aims at presenting a comparative analysis on the Nature-inspired algorithms employed in sentiment analysis and research challenges identified to extend assistance to the people working in the this research domain.
Real Time Video To Text Conversion of Sign Language

Sign Language is an non-verbal communication in which visible bodily actions are used to convey vital messages, either instead of or in conjunction with spoken words. Hands, face, and other parts of the body are used in sign language. Nonverbal body language, like merely expressive motions, proxemics, or joint displays of attention are distinct from gestures that convey specific messages. Gestures are culturally different and can communicate a wide range of meanings in different social and cultural contexts. In this work, the model will track the evolution of human behavior and recognize language patterns. Each movement of the joint, elbows and face must be identified. The goal of this research is to develop a deep learning model that can classify live video actions of human sign languages such as words and sentences. The classification accuracy of live action gesture types using LSTM is found to be higher than other state of art algorithms. It is expected that, if the LSTM approach is supplemented by the addition of continuous image sequential input, feature extraction methods and correct identification of video types, the success rate of the results produced will increase.
Bots Using Natural Language Processing in Medical Sector

It has been discovered that there aren't enough doctors to meet the needs of the growing population in India due to rising birth rates, declining mortality rates, and advancements in college education. This picture is best appreciated while traveling through city government hospitals, where a lack of doctors is the leading cause of inappropriate patient treatment and, in some cases, death. Even doctors might make mistakes in providing the proper therapy, resulting in the patient's death. A smart and clever Chat bot is needed to handle these circumstances since it can advise doctors and occasionally even patients on what to do, ultimately saving hundreds of lives. Doctors can make mistakes when analyzing symptoms, but a computer that is specifically designed for it will not be able to do so. That is why the ML-based Chat bot on which this study topic is developed deals with providing College guidance in such a circumstance. This ML-based chatbot may make decisions depending on the patient's requests. It accomplishes this by utilizing its own database, and in the unlikely event that the user requests something that isn't already in the database, it gathers data from search engines like Google and provides it to the user in an audio style much to what Google does.
A Comparative Analysis of various Object Tracking Methods from Video Sequences

Due to rapid changes in object motion, occlusions, fluctuation in a scene, appearance, and illumination changes, detection, and tracking of the object are one of the most essential study topics. In object tracking, feature selection is crucial. It's used in a variety of different applications, such as vehicle perception and video surveillance. To circumvent the problem of detection, monitoring of object movement and appearance is used. On the other side, only a few methods take advantage of previously collected data such as item shape, color, texture, and so on. In this study, a tracking method that incorporates the above-mentioned object properties is discussed and assessed. The motive of this research is to examine and evaluate the earlier techniques of detecting and tracking objects from video streams in various stages. However, robot vision, traffic monitoring, surveillance and security, video communication, public locations such as airports, and animation are a few of the advantages of tracking.
Dactylology Sensor Using Raspberry-PI

Vocally impaired individuals find it extremely challenging to communicate with general populace. Since the general populaces are not trained in hand sign language, so interaction is quite challenging. Here, we suggest a smart speaking system that enables individuals with voice impairments to communicate via hand gestures. The systems make use of a speaker unit and a hand motion reading device with motion and flex sensors. Circuitry in this project is powered by batteries. In this research, we provide a sensor-based system that can recognize hand motions used to represent all English alphabets in sign language. We suggest that if a person with speech impairments wears this wearable smart gadget, it would recognize the 26 letters. In order for this technology to fully be able to aid communication of this class of individuals, we have spoken about its difficulties and possibilities in the future. In the field of human computer interfaces, hand gesture recognition is another difficult issue.
Adoption of Extended Reality as a Teaching Tool

Extended reality (XR) facets aid pupils in understanding difficult topics. Regular education has been reliant on 2D technology for a very long time. It no longer draws in pupils due to its lack of immersion and engagement assistance. The suggested remedy involves using AR to develop 3D models and educational components. The augmented reality (AR) application is more appealing to pupils than other approaches and can be utilized as an interactive tool to clarify difficult concepts in the classroom. The functions of the following human anatomy parts-the brain, heart, and pancreas-were supplied in a piece of note, and audio was added predominately. The 3d models were created using the unity3d application as a support system, and Sketchfab was used to import them.
Generative Adversarial Network-a Comparative Analysis

Machine learning (ML) is a subset of artificial intelligence domain that incorporates a wide range of techniques including Genetic algorithms, Artificial Neural Networks, Bayesian Learning, decision trees. Analytical Learning and other supervised or unsupervised learning models. These techniques exploit present and arriving data to recognize the patterns and foresee forthcoming clinical events. There are number of evolving applications of ML in other aspects of patient management that have not yet been analyzed. Generative Adversarial networks (GANs), a deep neural networks which are capable of generating or transforming images and can support in faster imaging by creatingrealistic images utilizing the current imaging protocols in a variety of contrast and modalities. This paper deals with the big picture analysis of GAN along with architecture, comparison with existing approaches, application areas and the challenges associated with it.
Artificial Intelligence Based Smart Robot Predicting Human Activities

Perceiving human activities from video groupings or still pictures is an inciting task due to issues, for occasions, establishment wreck, midway hindrance, changes in scale, viewpoint, lighting, and appearance. Human action acknowledgment is a difficult time series order task. It includes anticipating the development of an individual in light of sensor information and generally includes profound area mastery and techniques from signal handling to accurately design highlights from the crude information to fit an Artificial Intelligence (AI) model. Human activity recognition assumes a critical part in human-to- human and human-computer association. Physically determined frameworks are exceptionally tedious and costlier. So, to plan a Human action recognition framework turns into a requirement for this present world situation. This framework won't just be practical yet in addition as a utility-based framework that can be consolidated in an enormous number of uses that will save time and help in different exercises that require acknowledgment cycle and save a great deal of time with great precision. The current Human Action Recognition (HAR) models are costlier that can't be managed for ongoing application. In this undertaking, video dataset is utilized for planning the framework. Highlight extraction strategies like optical stream and spatial fleeting procedures are being used to remove the elements. These datasets are trained using VGG19 after BoW Vector Extraction to predict normal and abnormal actions of human. The abnormal actions are live streamed to Police officials through a mobile application. In the interim, the doors of the automatic teller machine will be consequently shut to keep the robbery from taking off. Caution can be turned on to alarm the close by individuals. Subsequently, this task effectively gives a Human Action Recognition model which can be utilized progressively applications, for example, an answer for forestall and give proof of an Automatic teller machine, burglary.
AIB's Gesture Language Convertor

In recent days the requirement to communicate with a character audibly and freely may be a basic need and should be on the market to which will assist for deaf and dumb persons. Sign language is sometimes the only form of communication used by persons with hearing and speech disabilities. Because of this, communication between the deaf, dumb, and normal people is seriously affected. The likelihood of learning sign language is not very high among those who speak vocal languages. The goal of this project is to create an interpreter that can translate spoken language into text or speech that can be understood as a sentence utilizing CNN's segmentation of the various sign languages into their English alphabets. This project possesses the ability to sight the alphabetic sentence. This project conjointly proposes inclusion of action gif video (from deaf and dumb) to text model (normal people) and vice versa so as to facilitate seamless two way approach communication.
A Reliable Integrated Storage And Management Using Blockchain

Using database storage is considered to be inefficient in protecting documents that contain important or sensitive data. So the paper herein will show the way to store the documents in a safe and secure way using blockchain technology. Blockchain is technology that prevents unauthorized access to data and also makes the document tamper resistance.
Patient Monitoring and Assisting: A Real Life Unity3D Application

Patient monitoring without any contact is an essential task, especially in the wake of the pandemic. Game Engine provides a framework for programmers to create and simulate models. These game engines can be used to design a model which will monitor the patients. In this paper, the significance of Unity 3D game engine is considered for the application of patient monitoring and assistance. Unity 3D engine allows an accurate depiction of the real world in the virtual environment.
Complexity reduction in GraphSAGE with importance based selection in homophilic arrangements

Graph Neural Networks have challenges in processing of large datasets. Although several models have attempted to reduce complexity of processing large graphs, the problem is still not fully resolved. This work attempts to provide a homophily based approach to improvised on the earlier approaches. The important-features based homophlic approach is a pre-processing technique that reduces the data size which has been observed to reduce the complexity significantly. Although the technique is only implemented in the GraphSAGE model but as it is a pre-processing technique, it can be applied to other Convolutional Graph Neural Network models also thereby improving their performance.
Mobile Blood Bank Application - BLOOD

Nowadays, blood donation is quite popular and important as it can help to save others' life. From here, the project team developed a Mobile Blood Bank Application, also called BLOOD. This application serves as a communication channel for blood banks, hospitals, and donors. The primary purpose of this application is to allow blood donors to schedule appointments with blood banks for blood donations; hospitals can request blood from blood banks when there is a need, and blood banks can also request blood from blood donors when there is a need. However, those requests are only allowed to request via email. In this project, there are using Android Studio to develop it and Firebase as the database that stores the data of this application. In the future improvement, the project team hopes it can solve and fulfil the improvement from the feedback of the tester of this application.
Real-time Crime Detection Using Customized CNN

Every day, there are more crimes committed and criminals are on the loose, which is making people fear for their safety. The primary goal is to detect and deter illicit activity before it occurs. With the aid of cutting-edge technology, CCTV is commonly used in both private and public spaces. It is possible to control crime in this area, but human supervision is required to oversee it. It's difficult for a human to keep track of multiple screens at the same time. Human error is a possibility in many situations. To overcome this drawback, we stipulate a Deep Learning-based Real-Time Crime Detection Technique that analyzes real-time CCTV footage and alerts a nearby supervisor about the crime in the current region. The model tracks the movement of people and classifies it as aggressive or nonviolent behavior using the Multiple Object Detection with Localization technique. Any aggressive conduct filmed by the camera will be detected and instantaneously alerted by the system.
A Study on a Decentralized Network Secured Data Sharing using Blockchain

Sharing data is a crucial part of the research process that ensures the community as a whole benefit from everyone's efforts. The current data-sharing infrastructure requires the involvement of an impartial third party (TTP). Trust, transparency, security, and immutability are all lacking in TTP-based systems. This research intends to cut down on the amount of time it takes to effectively transmit a huge file by developing a unique idea of multihop concurrent communication across blockchain network peers to ease the sharing of massive data via threads in a multithreaded environment. If there is no network congestion, this three-tier scheme will guarantee the same throughput for every given node, regardless of how many others it is linked to. In this study, we explore how decentralization has the potential to aid in such situations. With decentralization, more people would be working to reroute data from a server node to a client node, and the blockchain would ensure the safety of this transaction. When using the suggested methodology, large files may be sent in a short amount of time.
Machine Learning as a Data Science Tool to Predict Absenteeism for Factory Workers

Employee absenteeism results in the loss of billions of dollars in revenue for organizations around the globe. Studies estimate a 3% loss of scheduled labor hours due to these unexpected and uncertain absences. Hence predicting these unforeseen absences would allow efficient workforce planning and help lower overtime costs for organizations. This paper has looked at the issue from the POV of the management to explore the absenteeism in blue-collared workers employed at the Research and Development center of one of India’s biggest fast-moving consumer goods companies. The paper has used basic Machine Learning algorithms models such as Logistic Regression, XGBoost, Random Forest, and Support Vector Machines to predict absenteeism in these employees by analyzing the data collated from various employment records in the company over two years (2017-2019). The paper has also compared their accuracy in making these predictions, thereby letting us know if these algorithms would suffice in making these predictions or if there is a need to deploy more superior and powerful computational models to do so.
Simply Book - Easier Way to Find Books

In this degital era, searching for a particular E-book in the intarnet by just using a crepped image from a random page of the book, a hend-writtan note or a diagram is a very exhaustive and time censuming process. Even if we find it, it would often be a different book with closely resembling centent and not be the one we are specifically searching for. If we can reduce the searching time and improve quality of the search result, then the user can carry on with their reading work without any hessle. These days, most of the search queries are done through Smertphones, and so the best way to provide a solution would be to produce a medium that works on a smertphone at the click of a button.
Digitalized Face Recogonition Railway Boarding

Particularly when there are many individuals travelling by train, the current system for railroad tickets in the modern world can be rather onerous and challenging to manage. Numerous problems result from this, including never-ending lineups and resource loss of time, paperwork, and even manpower. This might discourage commuters from choosing trains in the future. Any railroad organization can effectively implement this project's broad scope for efficient passenger verification. Passengers and travelers of all ages can benefit from this simple method of ticket verification because they won't have to worry about forgetting their ticket or waiting in a never-ending line. The ticket checking system can be put into place by installing a camera at the entrance, and after the passenger has been verified, they may enter. The check-in process would be quick and secure as a result. Additionally, this would speed up the verification process.
Survey and Comparative Study for Drone Detection Using Deep Learning

The widespread use of drones and the reduction of costs have made studies with drones popular. Especially the studies using artificial intelligence are followed carefully. The increase in these studies has paved the way for the integration of artificial intelligence algorithms such as computer vision, object tracking, and object detection into drones to perform more complex tasks autonomously. In addition, unmanned aerial vehicles are used in many useful tasks to eliminate illegal security threats such as border violations and drug trafficking. For this reason, the importance of drones is increasing day by day. In this article, the articles related to drone detection using state-of-art deep learning algorithms in the last 3 years have been reviewed and compiled. In particular, the methods used, suggested approaches, analysis methods, and the results obtained in these articles are summarized. In terms of the algorithms used in the reviewed articles, it is seen that it is preferred more frequently due to the success of radio signals and the success of one-stage detectors in terms of detection methods.