Reviewerly/exHarmony huggingface dataset:
An Efficient Method for the Data Monitoring of Photovoltaic Solar Panel

The world’s attention is now being drawn to renewable energy as a result of rising energy needs, unstable energy resources, and harmful gas emissions. As a result, the world’s attention is currently focused on renewable energy sources. In addition, thanks to advancements in software technology, we can now monitor data from any system in real time. The most effective way to gauge how well your solar power system is doing is to monitor solar panel output. This paper uses an arduino and labview-based live monitoring system to offer information about the performance of a PV panel. Additionally, various performance metrics and each panel’s power are determined for real-time monitoring. PV module, Arduino UNO board, voltage and current sensors are among the hardware components. The system design software includes Arduino IDE, LabVIEW, and LIFA.
Malicious Traffic Classification Using Convolutional Neural Network

While the Internet of Things (IoT) continues to see tremendous expansion around the globe, its security continues to lag far behind. IoT devices were the focus of more than thirty percent of all mobile network spam that were discovered. The use of Neural Networks for the detection of malicious traffic on these often-unattended devices has shown encouraging results. This project is primarily aimed to create an automated system for classification of malicious traffic in an IoT network. In this process, the first step is to prepare/collect a proper dataset for the specific task. The CIC IoT Dataset was used in this instance. The second step includes the preprocessing of the data. In the third step, in order to visualize the files, the files are converted into images using a binary visualizer that is capable of converting any binary file into a colored image. After visualizing the images, the final dataset is prepared. In the fourth step, a Convolutional Neural Network is created and the dataset is fed to the neural network model with 90% training size, 10% validation size of the dataset. During training the model, a 99.9% of training, 99.8% of validation and 81% testing accuracy is obtained. The training loss is reduced to 1.3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8</sup> and validation loss is reduced to 1.6×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8</sup> .
Sentiment Analysis using Long Short-Term Memory

Sentiment analysis is becoming increasingly beneficial to monitor social media, allowing us to obtain insight into public opinion surrounding certain topics. Long Short-Term Memory (LSTM) has been widely applied in sentiment analysis tasks due to its ability to model the sequential nature of text data and capture contextual information. By considering the ordering of words and their dependencies, LSTM can better understand the sentiment expressed in a sentence or document. The proposed work focusing on identifying the opinion of the text utilizing Machine Learning procedures like LSTM. We propose LSTM based system to characterize Food Review data. The audits dataset is taken from the Amazon fine food reviews dataset. An audit here will be characterized given the memory of an LSTM cell state in the neural network. In this study, we are doing the effect of dropout layer, Comparison of batch, activation function, optimization algorithm, Single and Multi LSTM layer.
Solving Arithmetic Word Problems Using Block Recurrent Transformer

Recent improvements in deep learning techniques have shown promising results in solving arithmetic word problems. The Block Recurrent Transformer (BRT) is a neural network architecture that coagulates the benefits of transformers and recurrent neural networks (RNNs). This paper investigates the use of BRT for arithmetic problem-solving and compares its performance with other state-of-the-art methods. Furthermore, a detailed description of the methodology that evaluates the performance of BRT on a dataset of arithmetic word problems is given. Our results show that BRT outperforms other methods and reaches state-ofthe-art performance.
A Deep Learning Based Approach For Spelling Error Detection In The Assamese Language

In today’s modern era of digital communication, it is vital to be able to communicate information effectively, and user-generated content has a significant impact on influencing the conversation that occurs online. Misspelled words are prevalent in such user-generated content, and they can have an impact on readability, dependability, and comprehension. Therefore, making corrections to spelling is a crucial stage in improving the quality of user-generated data. This paper describes the development of a model to detect misspelled Assamese words in such digital content. While determining a word’s spelling, the suggested model takes into account its context in the phrase. It emphasizes that some Assamese words have various meanings and might not be appropriate for a particular statement’s intended context even though they are spelled correctly according to the dictionary. The proposed approach employs the use of two types of machine learning techniques: Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM). The results show that the proposed approach considerably enhances spelling error detection by outperforming previous research in Assamese language. The proposed approach obtained the best accuracy of 89.52% for the BiLSTM model.
A Comprehensive Study of Web Phishing Detection Using Machine and Deep Learning Architecture

Phishing is the easiest way to compromise a user or an entity and first choice of malicious users; as it is hard to circumvent and exploit technology & security controls to gain unauthorised access or to steal information. Industry and institutions spending time, effort, money and invest knowledge on combating against phishing attack in two broad aspects; one is cyber security awareness program and the other mean is building machine and deep learning architectures to detect phishing attempts. This systematic study presents the trend analyses of the common attribute present in the dataset and significance of different machine and deep learning architectures and its hyperparameters in detecting web phishing attempts.
Mining Opinions by Means of User Reactions

Nowadays, the social network is enhanced through people interacting with each other and sharing feelings, comments, views, experiences, opinions, and comments about anything. Since there are so many web resources available in digital form for expressing one's viewpoint, like review sites, blogs, and forums on discussion, the importance of sentimental analysis and opinion mining has significantly grown up. Sentiment Analysis is a subfield of Natural Language Processing that is concerned with determining the views of individuals about an article or subject. Sentiment Analysis, frequently recognized as Opinion Mining, is the process of gathering and analyzing customer opinions on a product expressed in blog posts, comments, or reviews. At present, there are some challenges in opinion mining techniques. Firstly, is an opinion that is taken as good in a situation but can also be considered bad in another circumstance? Secondly, the public doesn’t usually put forward opinions in an alike manner. This paper presents an overview of the work done in the past and also about the proposed work.
Hand Gesture Recognition

In previous days, Initially the interaction or communication between the humans occurs through the different sign language. There are no scripts and language to interact. After some centuries the man discovers the fire, water and leaves to eat in forests, there comes the hand gesture as the sign language to communicate between the humans. In initial stages recognition of hand from the other parts of the body becomes most difficult. In this project we had offering you different hand gesture more than alphabets of the English language. For the Gray Scale Image, we have used the hash-based histogram equation. The classification was implemented using the Euclidean distance as well as Fisher Linear Discrimination Analysis (FLDA). The process becomes more effective. The recognition rate was more than 98.23 (Approximately) because of lightning effects varies in different locations, in addition to resolution of the camera pixels the recognition of gesture becomes easier to reach more appropriately.
Assessing the Efficacy of Symbolic Regression for Scientific Data Classification and Regression

Symbolic regression (SR) is a subdomain of evolutionary computing which aims to generate mathematical equations which best fit a given dataset. This is done with the help of genetic algorithms. Symbolic Regression is especially useful in cases where features in a dataset are known to have distinct mathematical relations to each other and its output. Therefore, this paper aims to compare and provide insight on the results of using SR on two datasets with two python libraries. These results are further compared with other ML models to assess their performance in classification and regression tasks. The detailed methodology and results are discussed further test metrics and obtained equations. The results show that symbolic regression can perform very well on certain datasets whose target outputs have a mathematical connection to the input features. SR is found to work better than other ML models in the classification and regression tasks.
A Virtual Reality based Mind Game-Comparative Study of VR Ray Interactor and Hand Tracking for Improving Attention &amp; Elevating Mind

Innovative applications in cognitive training in the improvement of mental health have become possible because of the rapid breakthroughs in virtual reality (VR) technology and our growing understanding of the human brain. The goal of this research is to create an immersive VR environment that involves people in a game that demands their attention while also grabbing their interest. The suggested mind game uses interactive components to encourage prolonged attention and cognitive engagement, such as visual stimuli, hand tracking, and difficult tasks. As they move around a virtual environment, users come across a variety of stimuli that demand their concentrated attention and prompt decision-making. Pilot research was carried out with a group of participants to assess the efficiency of the VR-based mind game. Assessments were given before and after the intervention to track improvements in attention performance. To quantitatively understand the improvement of attention, the attention levels recorded by the NeuroSky equipment were compared. Participants showed improvement in cognitive engagement and attention levels while playing the VR-based mind game and hence had a positive effect on improving attention. The comfort of the participants in the game while using a hand-tracking feature and VR ray interactor was investigated. Hand tracking is more effective for boosting attention and promoting engagement.
Unsupervised KeyPhrase Extraction using Graph and Embedding Techniques

The process of extracting keyphrases from a document automatically, without any supervision, is referred to as Unsupervised Keyphrase Extraction. This method aims to produce a brief summary of the main content of the document. Embedding-based methods comprise computing similarity between candidate keyphrases and documents embeddings. In this paper, we find that filtering candidate keyphrases using graph-based techniques enriches frequent candidates which are reranked using embeddings. On comparing the proposed model to the current state-of-the-art unsupervised Keyphrase Extraction approaches across three KPE benchmarks, it was found that the proposed model outperformed them.
Kathak Dance Movement Recognition and Correction Using Google’s Mediapipe

Indian Classical Dance (ICD) is a continuing tradition of India. This legacy has traditionally been guarded by gurus (teachers). They practice and teach the tradition to their Shishyas, who are frequently undocumented. Consequently, historical preservation is limited in time and scope. The advent of digital multimedia technology has enabled the preservation of history by preserving its accessibility over time. However, more efforts must be made to implement effective technology in dance pedagogy or maintain ICD history. This document describes a tutoring system for the ICD dialect Kathak. Using Media Pipe Holistic, Machine Learning Models, and Fast Dynamic Time Warping to detect postures and motions, we develop a system that can assist a trainee dancer in identifying deviations in her dancing postures and movements relative to previously recorded performances.
Fine-Tuned Transformer Models for Question Answering

Effective question-answering is crucial for applications involving natural language processing such as conversational artificial systems. The pre-trained BERT (Bidirectional Encoder Representations from Transformers) model achieved outstanding results on the Stanford Question Answering Dataset (SQuAD), a well-known benchmark for question answering tasks. The three different BERT models (BERT-base, BERT-large, DistilBERT) are pre-trained on general text corpus. These BERT models are trained to identify the right context in a paragraph to give answers. Further, fine-tuning the models with few-shot learning on domain related questions and answers improves the performance. Hence, our work proposes different strategies to improve the performance of BERT models on tasks requiring domain-specific question-answering tasks using a customized SQuAD dataset. Our work highlights the significance of optimizing the BERT models on domain-specific data for enhanced performance in particular tasks. The results of this study have implications for domain-specific knowledge in natural language processing. With a score of 0.9632, the BERT-Large model has the best accuracy out of all the BERT models tested. The performance of the BERT-Large model consistently beats that of the other models in several parameters, including precision, recall, and F1 score.
A Palm Index and Hand Gesture-Based User Authentication System

Hand gesture recognition has emerged as an exciting technology that offers a more authentic and interpersonal way of human-machine interaction. This work proposes a new authentication method for Internet of Things (IoT) devices based on palm index and gesture recognition. The aim is to explore the possibility of a contactless authentication approach that overcomes the limitations of traditional methods like PIN passwords. The proposed approach involves capturing a series of palm index photographs and using the length between the palm index as the user’s password. At first, a password is created for a user using the length between his palm index. The user’s authentication is confirmed by the recognized hand gesture, where the password previously generated from the palm index is verified. If the password is matched, the system is unlocked for the user. Therefore, the proposed mechanism has a much stronger password strength compared to traditional PIN passwords. Moreover, the proposed authentication method is also resistant to physical observation threats. The performance of the proposed model is analyzed through a Raspberry Pi-based real life implementation, where it is observed that the user authentication can be performed correctly.
Agriculture Chatbot(AGRIBOT)Using Natural Language Processing

Agri-bot is a text-to-text chatbot which will help the farmers in answering the queries posed by them, in text form. Through text-to-text communication, farmers can interact with the chatbot to obtain information on various aspects of agriculture, such as weather updates, crop management practices, pest and disease control, and market prices for agricultural products. The chatbot uses different Natural Processing Techniques like lemmatization and tokenization and it is trained using sequential Model. The text-to-text chatbot is made in English which shows the accuracy 80% and Hinglish which shows the accuracy 85%. The use of an agriculture chatbot can improve farmers' productivity, reduce costs, and increase their access to information, particularly in areas where internet access is limited.
AssignExpert - Host and Grade Programming Assignments in Educational Institutions

AssignExpert is created using Node.js and runs Docker under the hood to sandbox the execution of user-submitted programs. The platform allows faculties and students to sign up with their employee or student roll numbers. Faculties can create a class and give assignments with a deadline. Students can join a class and see the assignments. The assignment page is bundled with a code editor, using which the assignment can be submitted. Faculties have the option to reveal the points scored or defer this action to a later period of time. This paper further describes the theoretical and practical motivations for all the design and implementation decisions. The code for the platform is open-source and is available on GitHub.
Bangla E-mail Body to Subject generation using sequence to sequence RNNs

Recent times have been difficult for deep learning and natural language processing in terms of topic generation. The production of a subject reduces the length of a lengthy email body comment. Our goal is to design a Bengali subject line generator that is both efficient and effective, and that can provide a subject line that is both understandable and illuminating based on the Bengali text of an email. In preparation for this, we gathered educational, commercial, and other email bodies. These writings will be used as the basis for themes generated by our algorithm. While bi-directional RNNs and LSTMs are used in the encoding layer, an attention model is utilized in the decoding layer. Our sequence-to-sequence paradigm develops topics. Text pre-processing, missing word counts, vocabulary counting, identifying new words, word embedding, and other tasks presented challenges during the construction of this model. This model's goals were to come up with a subject and cut down on training time lost. We were able to cut our study trial training loss down to 0.001 by generating a clear and coherent subject line from the text of an email.
Energy Efficient Algorithms in Green Cloud Computing- A Comprehensive Study

Green cloud computing is becoming an increasingly popular approach for organizations aims- to decrease their environmental impact and energy costs. There are several challenges in achieving energy efficiency in green cloud and one of the important challenges are task-scheduling and resource-allocation approaches. In recent years, several energy-efficient task-scheduling and resource-allocation algorithms have been proposed to address these challenges. This work is to investigate strategies that enable energy-efficient in green cloud computing. A comprehensive study on energy efficient task-scheduling and resource-allocation algorithms is presented to identify the strengths and limitations of existing algorithms and highlight the areas for future research.
Detection and Staging of Invasive Ductal Carcinoma Using Deep Learning and CNN

Breast Cancer occurs when cells in the breast grow and multiply in an uncontrolled manner. It can be localised or spread to and invade other parts of the body. Invasive Ductal Carcinoma is the most frequent sub-type of Breast Cancer documenting for over 75% of all Breast Cancer cases. Detection and diagnosis of IDC involves a combination of tests and procedures. This process can be challenging and time consuming. Usage of AI can greatly aid this process by reducing false positives and as an implication, reduce diagnosis times. Early detection and diagnosis can improve survival numbers significantly. This paper explores the prediction of malignancy, detection of IDC, predicts its appropriate stage and compares the performance of several algorithms with varying accuracies with the highest one yielding 97.32%
Deepfake Detection Using Custom Densenet

Deepfake detection has grown to be an increasingly important research area due to the potential harm that deepfakes can cause to individuals and society. In recent years, deep learning techniques have shown promising results for detecting deepfake images and videos. In this research, we provide a deep learning-based strategy using Custom DenseNet for deepfake image detection. This model used a big dataset of actual and deepfake photos to train a convolutional neural network (CNN) using the Custom DenseNet architecture. The Custom DenseNet architecture is a deep residual network that has shown excellent performance in various computer vision tasks. To categorize images as real or fake, the CNN is trained using a binary cross-entropy loss function. To evaluate the approach, we used several performance indicators including F1-score, recall, accuracy, and precision. We also contrasted our strategy with other cutting-edge deepfake detection techniques. Our test results demonstrate that our solution employing Custom DenseNet surpasses existing deepfake detection techniques in terms of accuracy, precision, recall, and F1-score. We achieved an accuracy of 97%, a precision of 95%, and an F1- score of 75%. These results demonstrate the effectiveness of our approach in detecting deepfake images. Overall, our study shows that deep learning techniques, specifically the Custom DenseNet architecture, can be highly effective in detecting deepfake images.
Minutes: Hybrid Text Summarizer for Online Meetings

Online meetings have become commonplace, making it difficult to keep up. A summarizer for meetings, that captures the meeting content concisely, can prove to be beneficial. Meeting Summarization involves two key modules: Speech-to-text conversion that extracts transcripts from the meeting audio, and a hybrid text summarization model, incorporating extractive and abstractive summarization approaches. Extractive summarization was implemented using a centroid-based method that utilizes the compositional capabilities of word embeddings to capture the semantic relationships between sentences. The extracted sentences are fed to an abstractive model which uses the BART model as a general baseline. The BART model was fine-tuned on a dataset and optimized to generate better quality summaries. The T5 model has been used to generate one-line abstractive summaries. The models were evaluated using quantitative and qualitative testing. The summarizer thus assists the user to run through the important aspects of the meeting at a later stage.
Handwritten Character Recognition using Dynamically Mapped CNN Approach

This paper introduces a novel approach to developing a handwriting recognition system by leveraging deep learning techniques. The proposed method involves several stages. Firstly, a dataset is obtained from Kaggle, followed by a thorough data-cleaning process to eliminate unreadable samples. The data is then preprocessed by converting the images into the grayscale format, resizing them to a width of 256 and a height of 64, and finally normalizing the pixel values to fit within the range of [0, 1]. Subsequently, the dataset has been split into training sets and testing sets. with appropriate labels prepared for the CTC loss function. The model architecture is constructed by combining convolutional neural networks (CNNs) for feature extraction with sequence layers containing bidirectional LSTM (bi-LSTM) units. The model is trained using the training set and then evaluated using the testing set, utilizing n-gram analysis as the evaluation metric. The evaluation results show that the developed handwriting recognition system is efficient and successful.