Reviewerly/exHarmony huggingface dataset:
Good Fellow : A Healthcare Chatbot System

Health is essential to our lives. Every aspect of life is dependent on overall health. Healthcare is critical to living a healthy lifestyle. It is difficult to get a consultation with a doctor in certain unsocialized places. With such a large population, it is also difficult for doctors to attend so many patients. The fundamental aim here is to create a healthcare chatbot that can identify diseases and offer necessary information about the individual ailment before consulting with or seeing a doctor. This will assist in lowering the cost of health care while also improving access to medical information via a medical chatbot.Purpose: Nowadays, individuals are less concerned with their health. People avoid coming to the hospital for issues that could eventually develop into serious illnesses. Instead of looking through the list of potentially pertinent papers from the web, creating question-and-answer forums is increasingly becoming a straightforward method of responding to those inquiries.The purpose is to develop a healthcare chatbot system that, before contacting a doctor, can diagnose the illness and provide basic information about the disease. The main idea is to bring a "Health care chatbot" which can perform not only health checkups but also suggest doctors. [1] With the help of chatbot system, one may converse with a user-friendly bot through a text or voice interface.Based on the user's symptoms, the bot will identify the type of illness users are experiencing and answer any questions the user may have. Additionally, the user session histories of the proposed system would be saved concurrently, allowing users to review chatbot interactions and prior medical history.The method is proposed to help users' health care costs and time because it is impractical for the users to visit doctors or consultants when immediately necessary. This chatbot's UI will be simple to use and user-friendly. Due to 24-hour availability, this chatbot will be highly helpful for patients seeking a quick diagnosis for a specific condition. [2]
Enhancing Credit Card Fraud Detection with hybrid Squirrel Search - Honey Badger Algorithm Optimized Neural Network

Imbalanced datasets are a common challenge in machine learning, where the minority class is often underrepresented, leading to biased model performance. In this paper, we propose a novel approach that combines Weighted SMOTE based on Cross-Validation Uncertainty with a hybrid Squirrel Search -Honey Badger Algorithm (hSS-HBA) optimized neural network to address this challenge. The proposed approach generates synthetic samples that are better aligned with the underlying data distribution, and utilizes the hSS-HBA to optimize the neural network model. Experimental results demonstrate the effectiveness of our approach in improving model performance in terms of accuracy and F1-score. Our model has achieved an accuracy of 99.97% and an F1 Score of 0.9000, offering a promising solution for addressing the challenges of imbalanced datasets in practical use.
Deep Learning for Human Action Recognition

The Understanding of human behavior and assigning a label to each action is what Human Action Recognition (HAR) is all about. Its main objective is to accurately characterize human behaviors and interactions by examining the video-based dataset. Due to its broad applicability in the automatic retrieval of recordings of a specific activity using visual cues, human action recognition has grown in popularity. The model is prepared in such a way that it will access the data in the form of video and then convert them into the fames and then use them for the further process. When it comes to computer vision research, the topic of human action recognition is expanding. Object and human recognition, feature extraction, activity detection, and classification are some of the most frequent steps in action recognition. In our work we are using ANN (Artificial neural network) and SSD (single shot detection) technologies for predicting the class of the particular data set.
Concept-based Hybrid Control Algorithm for Explainable Deep Learning System

Artificial intelligence (AI) is a branch that has been widely growing without being constrained to a single discipline. But, along with the widespread applications, a certain amount of skepticism is involved in it. The said skepticism establishes a need for controlled AI to ensure functionality within operational limits. To this end, explainability provides a strong foundation, as it builds trust between the users and the system, and enables users to further understand the mechanics of the system. But, the research in this area is in its early stages, especially in the area of deep learning. This paper seeks to establish the need and to define the explaining- ability (explainability) and research the current works in this field and propose a concept-based hybrid algorithm to satisfy the established standard of explainability and compensate for the drawbacks in related works. The hybrid control algorithm proposed in this paper provides accurate, meaningful explanations and provides functionality to impose knowledge limits. It also provides a flexible regularizer to aid in a trade-off between explainability and computational cost along with a wide array of other functionality for design flexibility.
Analyzing Online Purchase Behaviour Through Machine Learning Approach Based Ordering Mechanism

In recent years, the rise of user-generated content has significantly influenced consumer behavior in online shopping. The analysis of product-related data, particularly sentiments expressed by consumers on e-commerce platforms, has garnered considerable attention. It is crucial to examine questions related to various aspects to assist sellers in making informed decisions and maximizing their profits. While it is feasible to sort purchasers into pre-buy or post-buy stages in light of procurement data, there are situations where purchasers might have gained the item from an outside source or on the other hand assuming the buy was made by another relative. To tackle this challenge, a novel text classification method has been proposed in this study that employs an ordering mechanism to determine whether a text was posted before purchase (pre-purchase) or after purchase (post-purchase). The approach presented in this study combines the strengths of transfer learning and sequential learning techniques within a hybrid deep learning model. This integration leads to improved performance of the proposed model, BBRCA, when compared to other machine learning models, as evidenced by higher accuracy, precision, recall, and F1-score.
Key Generation using Pseudorandom Number Generator

Cryptography is crucial for securing data, and the rise of online transaction processing has raised the necessity for security. Cryptography uses various encryption methods to protect sensitive data from threats, which calls for cryptographic keys. Any encryption or decryption method is often built on cryptographic keys. The cryptographic key gives strength to the encryption algorithm. It increases its security, so the generation of keys are essential to data encryption. The significance of any algorithm lies in the fact that the keys are not duplicated so that it is not possible to break. The key is truly random, then perfect secrecy can be obtained. In the paper, we are generating the key using a pseudorandom number generator so that it is complex for any attacker to break the cryptographic key.
A Conditional Generative Adversarial Network Model for Sketch-to-Image Translation

The sketch-to-image translation is a challenging task that involves generating realistic photographs from hand-drawn sketches. While Generative Adversarial Networks (GANs) have achieved remarkable success in generating real images, sketch-to-image translation remains difficult due to the limited information in the input sketches. Generative modeling problems can be addressed using GANs, which are a category of artificial intelligence algorithms. The objective of a generative model is to discover the probability distribution by analyzing training samples. This estimated probability distribution is then utilized by GANs to produce additional instances. In this paper, we propose a conditional GAN-based model for sketch-to-image translation tasks. By incorporating a loss function in the training process and learning mappings from input sketches to output images, these networks can handle various problems that previously required separate loss formulations. This approach allows for a versatile problem-solving technique using the same fundamental principles. Our suggested approach involves adversarial training of a generator and a discriminator, both of which are integrated into our model. The generator network takes the sketch and a textual description as input and generates a corresponding image. The discriminator network within our model is trained to distinguish between generated and authentic images. Consequently, the generator network is trained to produce images that can deceive the discriminator network into perceiving them as genuine.
Camera to Cloud: A Robust Solution for Seamless Video Data Integration for OTT and Film production

Camera-to-Cloud technology is the first step toward transforming OTT and film production via cloud-powered disruptions, which enables filmmakers to analyze video as soon as cameras stop recording. The camera-to-cloud workflow and its effect on OTT and cinema production concerning the post-production procedure is analyzed. The camera-to-cloud workflow records media with cameras or other external recording equipment, uploads media files to cloud storage and works remotely on post-production activities with cloud-based sound design, visual effects, and editing software. The technical components of establishing camera-to-cloud workflows, like data transmission protocols, cloud storage choices, integration with current processes, and utilization of cloud-based software programs, are covered.The results demonstrate the potential of camera-to-cloud workflows in motion picture production, offering knowledge to professionals in the field and paving the way for more effective and cooperative post-production procedures. The study also covers the challenges when implementing camera-to-cloud processes, including the need for internet speed, data security and privacy issues, and potential restrictions on remote editing capabilities and opportunities. Their advantages include adaptability, scalability, remote collaboration, quicker turnaround times, cost savings, increased security, and integrated asset management. The research is limited to this field, and OTT and film production industries will gain insights from this paper.
Enhancing Performance of Deep Learning Models with a Novel Data Augmentation Approach

Data augmentation is a popular deep learning strategy for increasing the size of the training set and improving the generalisation capacity of deep learning models, particularly in computer vision problems. This paper provides an overview of popular data augmentation techniques used in deep learning models (like CNNs), such as rotation, flipping, cropping, scaling, neural style transfer, grayscale and adding noise to the input images. We propose a new data augmentation technique which involves four sequential steps: rescaling, rotating, flipping, and converting to grayscale to enhance the performance of CNNs further. Using a dataset with numerous parameters, we show that the suggested strategy outperforms state-of-the-art data augmentation methods. The accuracy of the model trained using the suggested technique was 93.33%, representing a 3.33% improvement over the accuracy of the model built using typical data augmentation techniques.
Sign Language Recognition: Current State of Knowledge and Future Directions

A fast-emerging topic, Sign Language Recognition(SLR) focuses on creating technologies that allow computers to interpret sign language. In order to bridge gaps in communication between the hearing and deaf populations and to improve accessibility for those who are deaf, sign language should be acknowledged. These systems can precisely recognise sign language by detecting and tracking hand gestures, face expressions, and body language. The goal of this paper is to provide researchers with a thorough understanding of current SLR technology. To ensure a thorough and accurate analysis, the evaluation includes a number of literature sources, which are academic journals, conferences, and proceedings. The analysis underlines the need for consistent evaluation methodologies and standards and also discusses the datasets and evaluation metrics that are currently available for sign language recognition. Lastly, the report concludes the existing limitations of SLR and offers a roadmap for future possibilities in SLR.
Unmasking Cyberbullies on Social Media Platforms Using Machine Learning

Cyberbullying refers to an aggressive act or behavior with the intention of hurting someone through electronic communication tools. It involves insulting and sending threatening messages to the victim. Cyberbullying incidents are increasing worldwide, posing challenges in combating this form of victimization. Due to the vast amount of online data, tracking cyberbullying is difficult, making detection crucial. Several studies have utilized text mining techniques to analyze and detect cyberbullying. These studies involve classifying conversations or posts using supervised learning, labeling them with N-grams, and applying TF-IDF weighting. In the current system, comments on social media platforms are manually gathered and tagged, then processed using various binary and multiclass classification algorithms. The proposed technique offers practical insights into cyberbullying detection by utilizing deep learning-based models to comprehensively assess cyberbullying across multiple social media platforms and diverse subjects. To address the enormous volume of streaming texts from these platforms, an automatic cyberbullying detection system is being developed. The cluster and discriminant analysis stage analyze the text input to recognize abusive communications. The primary goal of this project is to empower victims by generating thorough reports that can be sent to the appropriate cyber cell for action.
Forecasting of Solar Photovoltaic Power Output using machine learning

A precise estimation of solar energy is necessary for a higher integration of renewable energy for better functioning of the current power system. Data-driven algorithms may be used to enhance solar energy estimates as a result of the availability of data at previously unheard-of granularities. The Linear Regression algorithm is provided in this work as the core model for the enhanced, broadly applicable stackable ensemble method. It is used to aggregate the outcomes from the core models, improving forecasting accuracy for solar PV power. In this paper comparison of two power plants has been done and it is found that one of them have better conversion accuracy than the other.
Solar Irradiance Forecasting Using Machine Learning

Accurate forecasting of solar irradiance is crucial for minimizing uncertainty of the solar energy output from PV plants, as solar irradiance can be utilized to determine the solar energy produced. The proposed models use the historical solar irradiance data along with 8 features namely temperature, dew, humidity, wind gust, wind speed, cloud cover, pressure, and visibility. The primary objective is to create a model that forecasts solar irradiance accurately. LSTM, GRU, SVR, and BiLSTM models are used and they are trained and tested on a dataset of 7 months with the frequency of one hour collected from a site in Tamil Nadu. By calculating the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), the performance of the models are evaluated. The LSTM model has performed better than other models and gave better accuracy in terms of RMSE and MAE.
Multi- Noise Reduction Technique Using Triangular Fuzzy Membership Function

Image noise is a random shift in the brightness or color information in images, and it is sometimes confused with electrical noise. It is an unwanted by-product of image capture that obscures the information that should be seen. De-noising contaminated images through the use of essential image processing technologies in various methods, resulting in the retrieval of lost information to improve and analyze images greatly. A fuzzy-based logic is proposed in this paper as a method for effective image filtering; specifically, it is used to de-noise an image by replacing noisy pixels with non-noisy ones based on the degree of membership of neighboring pixels within a filter mask. Results from experiments show that the triangular fuzzy-based membership filter outperforms competing filters in removing multiple kinds of image noise, including Speckle noise, Gaussian noise, Salt & Paper noise, and Poisson noise.
Weapons and Related Object Classification in Digital Forensic Using Machine Learning

The use of machine learning algorithms for the bracket of munitions and affiliated objects has come increasingly important in the field of digital forensic. This paper presents a comprehensive review of recent studies on the operation of machine learning ways (Convolutional Neural Network) to identify munitions and affiliated objects from multiple sources, including images of different of guns, cutter, vest etc. The review highlights the strengths and limitations of machine learning approaches, similar as unsupervised and pre-trained model learning. The performance of these models in terms of accuracy, sensitivity, and specificity is also estimated. The paper discusses several issues that need to be addressed in the operation of machine learning models to forensic examinations, similar as the need for large annotated datasets, robust point birth styles, and interpretability of the models. The paper concludes with a discussion of unborn exploration directions and the implicit impact of machine learning on the forensic analysis of munitions and affiliated objects.
Multi-Factor Authentication System With ID Card Credentials For Secure Transactions

A physical document that can be utilized to validate a person's identity is known as ID card, usually comes in standard size same as that of credit cards (3.370 inch height and 2.125 inch width).The machine readable identification information within ID card is the primary means of controlling individual access to everything starting from employee areas to special lounges. Transparency level of identification information within the document (ID card) varies from organization to organization. Some uses visible credentials like Barcodes and QR codes while others use RFIDs. Impersonation and the MFA- SYSTEM represents two distinct phases of the project. Former gives a clear-cut idea on how an impersonation attack is carried out by using methods in image processing, computer vision for extracting the visible features and decoding the information from the images or videos recorded using smartphones or surveillance cameras. SURF and SIFT are initially extracted, FLANN for feature mapping and Homography estimation using RANSAC algorithms. Latter one is making a multifactor authentication system that can improve the security of the ID card credentials. The proposed system in phase 1 can is able to successfully locate the position of barcodes and QR codes within the image and successfully decode information within the QR code, phase two helps to solve some of the challenges in authentication like shoulder surfing, securing data in MongoDB, accuracy during storing and retrieval from database etc
Single Image Super-Resolution Network with Enhanced Octave Convolution for Separating Image Frequencies

In recent years, deep learning-based algorithms for image super-resolution (SR) have been increasingly thriving. However, in most existing algorithms, the model takes the whole low-resolution (LR) image as input and treats the low-frequency (LF) and high-frequency (HF) contents in the image equally. Considering the nature of Bicubic down-sampling, which is the most used degradation in SR models, high-resolution (HR) and LR images share the most part of the LF in the image while differing in HF. To this end, this paper proposes a model that separates frequencies in the image with an enhanced octave convolution (EOctConv) layer. EOctConv applies channel attention to highlight HFs in the image while considering the relation between the weighted HFs with LFs, respectively. Separating image frequencies not only eases the learning of the relation between LR and HR images, it can significantly enable the model to focus most of the computation on recovering the lost details. The proposed model incorporates significantly fewer numbers of parameters which is indeed a remarkable factor to considerably reduce the computation time and complexity yet compares favorably with recent results in literature qualitatively and quantitatively on benchmark datasets.
Multilingual Speech Recognition Using Reinforcement Learning

A system that properly converts spoken language into written text is what the voice recognition and transcription project seeks to create. The system will analyze audio inputs and turn them into text using a variety of methods, including neural networks, language modeling, and acoustic modeling. The project's main goal is to increase transcription accuracy by tackling issues, including speaker accents, background noise, and audio quality. To make sure the system is capable of properly transcribing various speech varieties, it will be put to the test using a range of audio sources. The system's performance and limitations will be covered in a report that will accompany a functioning prototype of the voice recognition and transcription system. The project's findings will help numerous businesses that rely on voice recognition and transcription technologies improve communication, accessibility, and production.
Data Security in AWS S3 Cloud Storage

Cloud computing has changed the way the world works. From computing to storage, everything in between and beyond is being developed for the cloud. With corporates, enterprises and governments moving towards cloud and leveraging its benefits, it also raises questions about the security of data being stored and processed in the cloud. Due to the nature and architecture of the cloud security is the most sensitive and often the only factor which dictates the weather an organization will move towards the cloud. In this study, the aim is to address the traditional vs cloud-based scenarios particularly for AWS since it is the pioneer of the cloud revolution. The proposed method is making better use of the cloud and its strengths for storing and securing data automatically.
Saturation-Based Boolean Conjunctive Query Answering and Rewriting for the Guarded Quantification Fragments

Abstract Query answering is an important problem in AI, database and knowledge representation. In this paper, we develop saturation-based Boolean conjunctive query answering and rewriting procedures for the guarded, the loosely guarded and the clique-guarded fragments. Our query answering procedure improves existing resolution-based decision procedures for the guarded and the loosely guarded fragments and this procedure solves Boolean conjunctive query answering problems for the guarded, the loosely guarded and the clique-guarded fragments. Based on this query answering procedure, we also introduce a novel saturation-based query rewriting procedure for these guarded fragments. Unlike mainstream query answering and rewriting methods, our procedures derive a compact and reusable saturation, namely a closure of formulas, to handle the challenge of querying for distributed datasets. This paper lays the theoretical foundations for the first automated deduction decision procedures for Boolean conjunctive query answering and the first saturation-based Boolean conjunctive query rewriting in the guarded, the loosely guarded and the clique-guarded fragments.