Reviewerly/exHarmony huggingface dataset:
Betterment of Classroom and Examination Performance Using Historical Data - A Survey

In higher education, understanding, modelling, and forecasting student performance presents considerable hurdles in terms of developing accurate and effective diagnostic models. Several researchers have attempted to construct intelligent classifiers for predicting student accomplishment, but they failed to recognise the necessity of identifying the main components that contribute to the obtained performance. Such recognition is vital for programme directors to evaluate the strengths and limitations of their academic programmes and, as a result, make the required corrections to improve student accomplishment. Consequently, there is room to present a model that improves the accuracy of predicting student academic performance, as measured by future grades in various courses, and, also an optimized multi-label classifier that predicts qualitative values for the influence of various factors on the obtained student performance, primarily using a feed-forward neural network. The goal of the experimental findings are to demonstrate significant gains over single baseline models (e.g., linear regression, matrix factorization), indicating the applicability of the suggested technique in identifying several factors influencing student performance. This study underlines the need of predicting student achievement of learning outcomes in future studies. Along with this, another model that proved to be precise and accurate in predicting the outcome in a similar but not identical scenario, was the KNN classifier, which used data to classify the graduation chances of students. Using the classifiers in the data sets, there are several analyses that can be covered in terms of question pattern, format, and more. This study aims to push a new service that can be established in several organizations and locations to benefit educational professionals and students. The datasets are naturally created in the course of the university’s classes. This allows for a lot of flexibility in terms of output variables and classifiers. Along with this, using PyCharm or another Python based IDE and Python’s built in packages and libraries to perform the analyses and build the neural models. This allows for widespread application and use.
Application of AI techniques for Face Mask Detection

Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we’ll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine.
Human Activity Recognition with Video Classification

Human Activity Recognition (HAR), is a broader place of research area concerned with recognition and motion of someone primarily based totally on information obtained from one-of-a-kind sensors. Movements are the one-of-a-kind activities executed inside or outdoors, which include running, jumping, waving, and reading. There also are different activities which includes cleaning, eating, and riding a car. The goal is to document sensor information and the corresponding actions performed by a particular subject, to form a model primarily based on the provided information, and what's more, to sum up the model to order the activities of a completely new, inconspicuous informational collection in view of the sensor data.
Content Based Recommender System: Methodologies, Performance, Evaluation and Application

Content based recommendation system is an effective online information filtering tool, widely popularized by changing computer user habits, personalization trends, and the emergence of Internet access. Although current content-based recommender systems are well-known for providing correct recommendations, they nevertheless face a number of limits and issues such as serendipity, scalability, cold start, technical analysis, and so on. Because of the variety of strategies available, selecting one becomes a difficult issue when developing an application - targeted recommendation systems. Furthermore, each technique has its own set of traits, benefits, and drawbacks, raising even more questions that must be answered. This study aims to provide a comprehensive assessment of current developments in the field of content-based recommendation systems, with a focus on a variety of applications such as research papers, novels, entertainment, the health sector, products, and so on. First, each recommendation system's many uses are examined. Following that, an algorithmic study of several recommendation systems is undertaken, and taxonomy is constructed to account for the many components required to develop an efficient recommendation system. Furthermore, data sets are gathered, and the simulation platform and performance metrics specific to each contribution are reviewed and scored. Finally, this analysis provides much-needed insight into the field's current state of research and exposes existing gaps and obstacles to assist future researchers in developing an effective recommendation system.
BERT-based models’ impact on machine reading comprehension in Hindi and Tamil

"Multilingual Machine Comprehension" is a QA sub-task that comprises citing an answer to a question from a context, even if that answer written in a separate language from the excerpt itself. A lot of models have been trained to answer the question from a given short context which is a limitation of MRC, few models are considering this problem and adapting to handle the large input context to make the MRC more accessible and applicable to open domain scenarios. In this study, we examine Multilingual Representations for Indian Languages (MuRIL), rebalanced multilingual BERT (RemBERT), and XLM-RoBERTa, which are all BERT-based deep learning models. We trained these models to work on multilingual MRC particularly for two of the most used Indian languages Hindi and Tamil The datasets utilized in this study are freely available. The results of our research reveal that RemBERT outperformed other BERT-based deep learning models. For the dataset employed, the model received an F1 score of 84.58, an Exact Match of 74.05, and a Jaccard Index of 0.81.
Text Classification Using Feature Extraction and Classification Model

Sentiment analysis represents context-based text mining that classifies and abstracts particular information from the source data, and helps to interpret people's feelings. It adopts the NLP approach to classify people's opinions regarding goods or feedback. Sentiment analysis handles people's opinion and behavior in the context of feelings and attitudes concerning an episode or incidence. There are many domains that use opinion mining widely such as commercial goods review, social media study and film evaluation and so on. Semantic analysis provides major support in building recommendation frameworks. User provides textual feedback such as web-based reviews, remarks or responses on social networking and business sites. Machine learning is a common approach used for classification. This project devises a nascent methodology through the integration of dissimilar frameworks. This work applies Random Forest as a feature extraction method for taking out crucial features of the data suite. The K-mean clustering is enforced which can cluster similar type of features and in the last voting classifier is applied. Voting classifier consists of LR, KNN, SVM models. Python is the tool used in this work for the implementation of the devised methodology and evaluates new methodology with respect to some global performance indices.
Digital Transformation and Smart Sensors

The "new economy" is approaching, and it will necessitate new rules, adjustments, and ways of thinking. A step toward the digital economy is digital transformation.Data-driven work practices are at the heart of digital transformation. Sensors provide real-time data, automating manual data collection. There is no digital transformation without sensors. Any number of variables can be transmitted by a sensor using digital communication, Digital signals must be supported by plants.Sensor Technologies has been powered by an electronic circuit that operates at a very high speed and low cost. Due to different method that are novel Processing method and very Advanced Manufacturing Technology which make it low cost .The Availability and the very wide range of application sensors of low cost are in very high demand .Integrated sensors are being develop to met all the criteria of designer’s need for simple design. now a days smart Sensors are becoming the integrals parts of the system performing functions ,that previous sensors could not able to performed or were not that economically variable. The presence of digital processing power has a liberating effect on a sensors design.
Ensemble Summarization Models to Leverage Performance

Summaries underpin a majority of relevant information needed to quickly make an informed decision from a large corpus of text; Natural Language techniques have been developed to generate these summaries using either abstractive or extractive methods. Presently, state-of-the-art approaches involve using neural network-based solutions akin to seq2seq, graph2seq, and other encoder-decoder architectures. These models make different contributions to prediction quality. In this paper, we build a model that ensembles two distinct pretraining NLP models to leverage their summarization performance using a TextRank process we constructed. We evaluate our model using the CoronaNet Research Project COVID-19 dataset, which contains how governments responded to the Covid-19 pandemic. We compared the ROUGE scores of the individual models on the test set to our ensemble method. The experiment results show that our proposed ensemble method performs better than using the models individually.
Effectiveness Analysis Of Blockchain Mechanisms Using Consensus Algorithms

Recently, along with the development of information technology, blockchain technology has been developing all over the world. On the basis of blockchain technology, it will be possible to develop the digital economy, ensure information security, and increase reliability in data exchange. Due to the high potential of blockchain technology, it has led to the study of its wide application in various fields. These processes have led to the creation of blockchain-based systems for various purposes. Some blockchain mechanisms can also be used to increase data reliability in information systems that provide services in computer networks. However, at the same time, most of the blockchain-based systems have several disadvantages related to their performance and security. To solve these shortcomings, it is necessary to improve the security of blockchain-based systems. The main component of any blockchain-based system is its important consensus algorithm, which in many ways makes it possible to ensure the authenticity and security of information. Consensus algorithms serve to prove the authenticity of executed transactions. The application of consensus algorithms to the blockchain technology, depending on the functional task, allows to increase the reliability of the data. In this article, the effectiveness of using consensus algorithms based on blockchain mechanisms is studied.
A Framework for the Localisation process of websites

Culture plays a vital role in shaping the web- interaction related conventional model that aids to maintain a congenial interdependence between the local users and the website. Therefore, the localisation process of non-native websites needs a reliable procedure in an effort to deliver an intelligible and adequate localised site. Incorporating a brief overview of different cultural models and theoretical approaches, this paper explores the vivid viewpoints and techniques of localisation and provides a collective framework which encompasses multiple phases for research, analysis and design iterations within the life-cycle of website development in order to increase the effectiveness of the localisation process with due respect to the target culture.
Sentiment Analysis on Amazon Product Review

Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today’s age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches.
Optical Character Recognition system with Projection Profile based segmentation and Deep Learning Techniques

Optical character recognition is the solution to convert text from printed or scanned documents into editable data. This project is aimed at building a Optical character recognition system that recognizes digital text. A document is first detected using contour-based detection technique without altering the angle of the image and is segmented into lines, once the lines are segmented the words embedded in them are extracted. This segmentation is done using projection profiling method. Characters are then segmented words with vertical projection profiling from the extracted words. These characters are fed into an image recognition model for recognition. The recognition model is CNN based deep learning model. Modified VGG16 architecture is used here to extract maximum features from the images and then classify them. To train the model a dataset is created from a repository of digital character dataset. The dataset consists of images of 153 font variants.
Transforming online class recording into useful information repositories using NLP methods: An Empirical Study

Most educational institutions have adapted to the mode of online teaching which has resulted in an increase of online video recordings. Learner community can be benefited with the ability to retrieve required information from the online class recordings. In this paper, we propose a methodology for converting video transcript data into useful information repositories for the purpose of retrieving class transcripts relevant to user's information needs. We focus on the online video recording transcript data. We also discuss challenges in transcribing which are crucial to understand preliminary processing. Our dataset consists of transcripts from diverse subject domains deeper experimental insights. We use interactive transcripts obtained from ASR (automatic speech recognition) services and non-interactive human generated transcripts. State-of-the-art methods for keyword retrieval: Latent Dirichlet Topic Modelling (LDA), Term Frequency (TF.IDF) and Text Rank (graph based) are applied on the video transcript data. Further, cosine similarity metric is applied to obtain the similarity measure between the transcript documents and keywords.
Fuzzy logic-based Student Placement Evaluation and Analysis

ICT and AI have been widely used in the field of education. Non-classical methods like fuzzy logic are increasingly being used for evaluation. The primary objective of this analysis is to propose and support the usage of fuzzy logic in the process of evaluating test results for placements in engineering colleges with Computer Science. The contributing attributes that have been brought into consideration for the research are CGPA, Aptitude, English, and Coding score. A sample analysis is provided in which both the average method and the fuzzy logic are applied and compared for evaluation. The outcomes from this case study are presented and reviewed. The flexibility, transparency, and objectivity of using fuzzy logic as an evaluation method provide it an edge over the other classical methods and hence a new evaluation method is proposed.
Tweet Summarization Using Clustering Mechanisms

Due to the growth of online sites like Twitter, this’s resulted in a rise in the quantity of user-generated content. It is difficult for users to absorb so enormous amounts of data. The outcome is creating automated technologies to summarise massive problematic. Twitter is a well-known platform for microblogging. Users post messages.in form of twitter posts. It serves as a framework for compiling tweet summaries in this article. Occasionally, in times of catastrophes or environmental hazards. Users tweet frequently. Just a few twitter posts will be beneficial. Displaced people find these twitter posts useful. The details can be gleaned out of those twitter posts. Emergency team can execute rescue efforts with an information gleaned from such twitter posts. The categorization of tweets serves as the preliminary step of the suggested strategy dividing twitter posts into relevant/irrelevant categories accompanied by a clustering mechanism. BERT paradigm, also referred to as the "transformers classifier," is utilized for categorization. This system considered clustering mechanism is DB SCAN. An innovative technique relying on neural networks is used to separate and simplify relevant Twitter posts. An unguided approach for categorizing Twitter posts as relevant/irrelevant will be used to build this model.
LS-Gossip: An Improved Block Distribution Algorithm in Blockchain Networks

With the rapid development of blockchain technology, more and more blockchain projects use the Gossip algorithm to synchronize block data, but its efficiency is dissatisfactory, which restricts the data transmission performance of the blockchain. The random transmission mechanism of the Gossip algorithm causes nodes to send and receive messages repeatedly, which results in a large amount of redundant traffic. In addition, the failure of some nodes may lead to data loss in transmission, which prolongs the overall propagation time. To solve these problems, we propose the LS-Gossip algorithm. It marks the latest block status of adjacent nodes for on-demand transmission, reducing the probability of selecting duplicate nodes. Then, it optimizes the propagation path by prioritizing nodes with higher reliability to propagate blocks, reducing the propagation time delay caused by node anomalies. The results indicated that the data distribution efficiency of LS-Gossip was significantly improved compared with Gossip, which greatly reduced the propagation convergence time and communication overhead.
Vision Based Vehicle-Pedestrian Detection and Warning System

Road Sense must be respected and obeyed by both the pedestrian and the driver. Moreover, urbanization has led to a steadfast rise in the fleet of vehicles, their speed, as well as non-compliance with road safety measures, and other such factors have provoked an inescapable increase of accidents in road traffic involving pedestrians. Pedestrian collisions can be predicted and prevented. At the very basic, there has to be vehicle and pedestrian detection along with speed estimation, which can be further applied to Vehicle-Pedestrian Collisions and various emerging fields like Industrial Automation, Transportation, Automotive, Security/Surveillance, or in Dangerous environments. This paper reviews the literature on vehicle and pedestrian detection based on two significant categories: pre-processing phase and detection phase, with a detailed comparative analysis. The papers reviewed cover video-based surveillance systems.
An Effecient Approach to Detect Fraud Instagram Accounts Using Supervised ML Algorithms

Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest.
Automatic Attendance System Using Biometric Authentication and Varying QR Code

In educational institutions, the performance of the student is directly related to the attendance percentage of the student, which is also the factor that will decide the eligibility of a student to write the exams of that particular course. Moreover, recording student attendance manually by teacher intervention is a taxing and time-consuming process if the strength of the class is highTo avoid this, we have implemented an automatic attendance taking system which involves QR codes which are scanned by the student to give their attendance. Biometric authentication and time limited QR codes have been seen as a possible way to avoid fake attendance in a quick and easy manner.