Reviewerly/exHarmony huggingface dataset:
Smart Assistant for Visually Impaired People using Deep Learning Algorithms

Vision impairment has a profound influence on quality of life. This proposed work aims to work as an eye for the blind people. It will take in pictures as an input and interpret the details in the given picture as captions. The challenge with this system is that it requires greater accuracy in extracting information from images and associating it with captions because captions for the blind must be clear, concise, and reliable. Deep learning teaches computers to perform human-like tasks through multi-layer architectures. To address the eyesight challenge for the blind population, this study proposes a hybrid deep learning autoencoder ensemble, which is a combination of Convolution Neural Network (CNN) with Gated recurrent units (GRUs) along with a novel attention mechanism to extract the inherent features of the image that are most closely related to the caption. Here, the CNN serves as the encoder and GRU serves as the decoder. The proposed attention mechanism uses dense layers to draws attention to areas of the image and grasp more relevant features of the image associated with the caption. Flickr8K dataset is used to assess the suggested model. which contains 8091 images, each of which includes five captions, for a total of 40455 captions. The efficacy of the proposed CNN-GRU model with attention layer to selectively attend to various components of the image while generating each word of the caption enables it to outperform the existing approach by 10.59% accuracy improvement by perceiving better Bilingual Evaluation Understudy (BLEU) score.
Smart Education based on Blockchain Technology

This research study explores the potential benefits and challenges of integrating blockchain technology into the education sector. Specifically, it examines the concept of “smart education,” which utilizes blockchain to create a secure, decentralized, and transparent learning environment. This study provides an overview of blockchain technology and its applications in education, including the use of smart contracts, digital credentials, and decentralized learning platforms. It also discusses the potential advantages of smart education, such as increased security and privacy, improved student tracking and data analysis, and reduced costs. The ability to securely store, verify, and share educational credentials on a decentralised, transparent platform made possible by blockchain technology which has the potential to revolutionise the education sector. To fully realise the advantages of blockchain in education, there are several obstacles that must be overcome. The practical application of the technology is one of the main obstacles to its use in education. There are few set norms and protocols for using blockchain technology in education yet because it is still in its early phases. Getting educational institutions and businesses to use blockchain technology for the verification of educational credentials is another challenge. Due to a lack of knowledge or confidence in the technology, many educational institutions might be hesitant to adopt it.
Population Density Estimation using Single Shot Detection Algorithm

The proposed system consists of an Artificial Intelligent software that is capable of counting the number of people from the user inputs such as images and video frames more accurately. The system uses the advanced MobileNet Single Shot Detection algorithm to detect the human class in the given frame at the given moment. The count of individuals in the frame is dependent on the threshold set by the end-user. The primary goal of the application is to provide an easy-to-use and efficient tool for analyzing images and videos. The model used for object detection is trained on the mall dataset from Kaggle website and is capable of accurately detecting people in real-time, making it suitable for a wide range of applications. The people in the frame are tracked using centroid tracker algorithm, and the tracked classes are counted. The detection algorithm is built using the popular library keras for its versatile usage. This system uses Keras, a high-level deep learning library, and OpenCV, a computer vision library, to perform real-time people counting. The system utilizes object detection algorithms to detect and track individuals, and then increments a count each time a person crosses a designated line. The system is capable of handling multiple people simultaneously and accurately counting them in real-time.
A Pattern Recognition Model: Hand Gestures Recognition using Convolutional Neural Networks

Human Beings have the natural ability to recognize body and sign language easily. This is possible because of vision and synaptic interactions formed during brain development. Humans also can pick up contextual information to understand body and sign language. In order to replicate this ability in computers, it is highly required to solve several issues-the ability to differentiate between objects of interest and the background. Choosing image capture technology and image classification algorithms etc. So, Hand gesture recognition is quickly becoming an important and relevant technology. Given the recent growth of AR and VR technologies, Hand gesture recognition has become an exciting new technology field. But currently, hand gestures are very specialized and haven’t acquired mainstream adoption. This study intends to show that gesture recognition can enhance many aspects of computer work and teaching. In our predicted model, the system works by the user starting the program, which would start providing the live webcam feed. The user then makes the hand gesture inside the frame of detection shown on the screen. When the hand gesture is made, the gesture is segmented and isolated. Some possible applications are to reduce the use of a physical keyboard and mouse by using hand gestures recognition to control computer operations like Pause/Play, close and open windows, and manipulate media controls. Another application is sign language. Hand gesture recognition can be used for communication and teaching purposes.
Assessment and Future Directions for Clustering Optimization in Cloud Computing

The current cloud technology offers access to a sharable resource such as networks, storage servers, applications, and other services. The way businesses manage their data, save their files, and use their apps has been completely transformed by technology. Nonetheless, the effective and efficient use of hardware and software resources is essential to the success of cloud computing. In a cloud environment where several processes are executing simultaneously, task scheduling and virtual machine clustering become crucial to ensuring maximum performance. Efficient task scheduling methods enable the distribution of resources among various tasks in a way that maximizes overall throughput. To provide a more effective and scalable computing environment, virtual machines are grouped together in a virtual machine cluster. Metaheuristic techniques need to be introduced for effectiveness of TS optimization. By intelligently and effectively navigating the search area, these algorithms are utilized to find the best answers to challenging scheduling issues. A hybrid algorithm is one that combines the advantages of two or more different algorithms. A hybrid algorithm’s success depends on the thoughtful selection and blending of various algorithms and parameters. Several job scheduling strategies, popular cloud simulators will be presented and examine the outcomes in light of the important criteria in this study. In order to assist academics and practitioners in selecting the best algorithm for their unique needs, this study attempts to shed light on the advantages and disadvantages of various algorithms.
A Comparative Study and Analysis on Toxic Comment Classification

It is the task of identifying and categorizing comments that contain harmful or offensive content, such as hate speech, cyberbullying, or harassment. This task is crucial for maintaining a safe and respectful online community, but it poses several challenges due to the complexity and ambiguity of natural language, as well as the constantly evolving nature of toxic language. Recent techniques for toxic comment classification include deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models like BERT and GPT. These models have achieved impressive performance on benchmark datasets, but it still faces several challenges. One challenge is the lack of diversity in training data, which can lead to biased models that perform poorly on real-world data. Another challenge is the difficulty of detecting and classifying subtle forms of toxic language, such as sarcasm, irony, and euphemisms. The proposed objective of this study is to develop a more robust and accurate toxic comment classification system that addresses these challenges. Specifically, this research study aims to improve the model’s ability to detect and classify subtle forms of toxic language by incorporating additional contextual information and leveraging techniques such as adversarial training and data augmentation to increase the diversity of training data. This study also plans to evaluate the model’s performance on a range of real-world datasets to ensure its effectiveness in practical settings.
Object Detection and Action Recognition using Computer Vision

One of the main issues with computer vision is the recognition of objects and actions. Deep learning has significantly changed how society uses artificial intelligence since it first emerged a few years ago. The study was primarily designed for monitoring and proctoring reasons. Action recognition is used to keep track of the subject’s motions, and object detection is used to locate objects in the scene. This proposed model is built in Python using real-time computer vision frameworks like Open CV and YOLO. Moreover, Open CV is used in image processing and machine learning. Up to 80 different things can be recognized from the data set of objects provided. You Only Look Once(YOLO), Faster Recurrent Convolutional Neural Networks (RCNN), and Single Shot Detector (SSD). When performance is more important than accuracy, YOLO excels while Faster RCNN and SSD do better. This technique efficiently detects objects without degrading performance. There are few challenges faced during the study auch as Viewpoint Variation, Deformation, Occlusion, Illumination Condition, Cluttered, Intra-Class Variation.
A Graph Neural Network Approach To Combatting Fake News

One of the facts regarding social media or internet is that bad information is everywhere. It is difficult for a common man to decide whether it is a misleading or a false information. And believing false information can lead to many problems such as making bad decisions, making unnecessary arguments, disturbances in relationships between people and can also lead to mental stress. Hence to avoid these consequences, this study proposes a novel model to detect the fake news efficiently and effectively. Proposed model is based on Graph Neural Networks (GNN). GNN belongs to the class of deep learning models, and it can work on nonlinear and multidimensional data. GNN can perform prediction tasks on graphs at node-level, edge-level and also entire graph-level. The reason for choosing GNN is Convolutional Neural Networks cannot be applied on nonlinear data as proposed dataset consists of nonlinear data and GNN can provide better accuracy for the model than Convolutional Neural Networks. Most of the models based on graph neural networks will need both text information and node features, but the proposed model can perform the prediction using only node features. This makes easier for us to detect the fake news with minimal computations without comprising in terms of accuracy. Proposed GNN model achieves a test accuracy of 95%.
Revolutionizing Malaria Diagnosis: A Deep Learning Approach

Malaria is a hazardous disease affecting millions worldwide, and early, accurate diagnosis is crucial for leveraging effective treatment. The proposed method involves training the VGG19 algorithm on a large dataset of images that detects the presence of malaria parasites. The outcomes show the effectiveness of the suggested strategy, with a 95 percent accuracy rate in identifying cells infected with malaria. The suggested method can potentially increase the effectiveness of malaria diagnosis, especially in an environment with limited resources and access to qualified medical personnel.
XLNet Transfer Learning Model for Sentimental Analysis

In natural language processing, an important objective is to perform sentiment analysis, which involves categorizing textual content based on whether it expresses a positive, negative, or neutral sentiment. Sentiment analysis systems face challenges such as ambiguity, subjectivity, contextual understanding, and domain adaptation. These challenges make accurately determining sentiment in text a complex task. To address these challenges, the proposed objective for sentiment analysis on movie review datasets is to develop a transfer learning-based XLNet model. The utilization of transformer-based models has resulted in notable advancements across several NLP tasks in recent years. In this work, the feasibility of employing the XLNet model for sentiment analysis is examined, which involves fine-tuning the XLNet model on a labeled sentiment analysis dataset. First, the dataset is preprocessed, and the XLNet model is loaded. In addition, the classification layer is added to the model and transfer learning is applied for fine-tuning on the sentiment analysis dataset. The effectiveness of the proposed work is evaluated on a test set, and various metrics such as accuracy, precision, recall, and F1 score are reported. Experimental results indicate that the XLNet model attained goood results than other transformer based models on movie review dataset for sentiment analysis and it shows the effectiveness of transfer learning with XLNet in the sentiment analysis domain.
Naive Bayes Classifier based Movie Recommendation System

In 21st Century almost every single piece of data is available over the internet. All we need is to surf through the internet. User sometimes spend more time in surfing the internet to get information about a movie. So the emphasis is to make every piece of data available in a single page which will help user to surf faster. Our approach is to build a website with customized search engine for Hollywood movies. Initially, the movie database gathers data from IMDB and Kaggle movie dataset. When the user enters the movie title in the webpage. TMDB API gathers the data from the TMDB website. This provides the user with the simplistic and user-friendly environment. It also provides the basic information about the movie, classification of the movie review based on classifier algorithm and generates a list of top 10 similar movies based on cosine-similarity measure and content-based filtering approach.
Self Sovereign Identity - Blockchain based Blood Donation Management Method

Developing an efficient blood donation management system is a challenge for healthcare sectors. In the medical field, the blood supply chain is continuously or indirectly tied to the need for blood to be supplied on demand. For this, a robust blood delivery chain system is required, allowing blood-related records to be traceable at every stage of the blood delivery (e.g., from donor to blood recipient), while also taking care to maintain each donor’s privacy during the testing, storage, and distribution phases. Blockchain platform acts as a leverage traceability withinside the blood donation deliver chain. The blood donation supply chain ecosystem uses blockchain, a distinctly green, decentralised, and peer-to-peer allocated technology, to provide continuous traceability, protection, immutability, and protection.
Scrap Management using E-com Online Machine Learning Iscrap Algorithm

The scrap recycler app is an online application where users can sell the scrap materials that are available in the house. Scrap materials are referred to as waste, unused products like broken items, plastics, waste metals, papers, glass etc. Scrap is defined as a material that has no economic value only the basic materials that are available in that scrap can be recovered and recycled.This application serves as a bridge to sell those waste scraps online. The main motto is to create an application that will be used to sell waste scraps and reduce the scraps that are available in the house and increase the recycling process and develop a sustainable environment. By using this application users can directly post the scrap materials online and vendors can be able to see the available scrap materials to be purchased and it is send to recycling stations. This application helps the vendors to easily make profit and it collects the scraps efficiently from the users, by this transportation costs can be managed. Only verified vendors can buy the scrap from the customers so that customer trust is developed and the vendors can mainly focus on customer satisfaction. This study provides a new perspective on selling those available scraps that are available in houses and small organizations using machine learning. The main objective of creating this algorithm is to reduce the usage of waste and reduce the land pollution. The algorithm is going to encounter challenges in sorting scraps and segregating waste, as well as disposing of waste. The machine learning is also used to detect the scraps so that the app will able to detect the scraps and indicate to the vendors. As a consequence, the algorithm is capable of resolving it. The software application is free and is available on the Android platform. The application is user-friendly with a basic structure and easily able to sell scraps online. This software application is developed using MYSQL, android studio, and kotlin.
Secure and Scalable Internet of Medical Things using Ensemble Lightweight Cryptographic Model

Internet of medical things (IoMT) enables the users to transmit and receive the data over secured communication medium. IoMT devices handle sensitive patient data and information, making them attractive targets for cybercriminals. Ensuring the security and privacy of this data is critical to maintaining patient trust and preventing unauthorized access and data breaches. In general, the traditional encryption models such as symmetric, asymmetric, hash functions, and hybrid that includes advanced encryption standard (AES), RSA, elliptic curve cryptography, secure hash algorithm, and message digest algorithm etc. are designed to render high level security for communicating and storing data. However, they were not optimized for the use in IoMT devices, which have limited processing power, memory, and battery life. This led to the development of lightweight encryption algorithms, which are specifically designed to provide strong security while minimizing the computational and power requirements of IoT devices. Therefore, this work focused on implementation of lightweight-medical image cryptography (LW-MIC) system using ensemble lightweight cryptographic (ELWC) protocols. Initially, the medical image data from users is converted into digital data. Then, ELWC operation applied on vector data, which implements play-fair, and Cha-Cha based encryption algorithms. So, secured image data of users are transmitted over IoMT environment. Finally, the ELWC decryption algorithms are used to restore the original image data at the receiver (doctor) side. The simulation results revealed that the proposed LW-MIC system resulted in superior image encryption, reduced time complexity performance as compared to state of art methods.
Ensuring Long-term Sustainability: A Study on Effectiveness of Watermark and Performance Evaluators in Video Watermarking

This study investigates the long-term sustainability of video watermarking techniques by examining the effectiveness of watermark and performance evaluators. With the rise of digital piracy, video watermarking has become an important tool to protect intellectual property and prevent unauthorized use of copyrighted content. However, the effectiveness of watermarking techniques can diminish over time due to various factors such as compression, cropping, and editing. This study focuses on evaluating the long-term sustainability of video watermarking by analyzing the performance of various watermarking algorithms and comparing them with existing performance evaluators. The study found that while some watermarking algorithms showed good short-term effectiveness, their long-term sustainability was limited. Additionally, the performance evaluators used in this study were found to be useful in measuring the effectiveness of video watermarking over extended periods of time. The results of this study can be used to guide the selection and implementation of video watermarking techniques to ensure their long-term sustainability.
A Comprehensive Study on the Application of Blockchain Technology’s Use in Healthcare

Blockchain technology has been increasingly adopted in various industries, including healthcare. This research study has conducted a systematic review of current blockchain applications in healthcare and assess their potential for improving healthcare outcomes. Moreover, this study also identifies the challenges and opportunities for blockchain in healthcare and discusses the solutions that need to be addressed for its widespread adoption.
Automatic Question Answer Generation using T5 and NLP

Automatic Question Answer Generation (QAG) systems have received significant attention in recent years due to their potential to improve the efficiency of various natural language processing tasks. A QAG system is proposed that utilizes a state-of-the-art language model to generate high-quality question-answer pairs. The system supports various types of questions, including Wh-questions, fill-in-the-blank, full-sentence questions, multiple-choice questions, and true-false questions. System is developed and tested on a diverse range of text-based datasets, and the results show that it can generate accurate and relevant questions and answers for a given piece of text. The QAG system has significant potential for use in educational, industrial, and research settings where quick and efficient comprehension of text is crucial. The system’s usability and effectiveness are demonstrated through experiments, and it is believed that it has the potential to be a valuable tool for a wide range of natural language processing tasks.
Few-Shot Learning with Feature Pairing and Mean Discrepancy

Few-Shot Learning (FSL) is a sub-area of machine learning which mainly deals with data where there is a scarcity of training supervised samples. Few shot learning (FSL) more closely resembles the human brain in comparing new concepts to others based on prior experience rather than identifying it exactly. FSL aims to generalize the model across the tasks (in meta learning) opposed to the classical supervised learning which generalizes across the data points. In general the FSL models may suffer from underfitting because of scarcity of supervised samples and at the same time it causes overfitting as it is likely to memorize task specific features of the training set. This work aims to reduce such problems and is presented as a metric based model ”Few Shot Learning with Feature Pairing and Mean Discrepancy” (FL-FPMD). As the title suggests, feature pairing is one among various data augmentations. It is observed that flip augmentation is more suitable in the context of pairing the features within the given task. Memorizing task specific features is reduced by incorporating the discrepancy of mean distributions of the query and the support embedding in the loss function. The training and the evaluation is performed at the miniImageNet dataset and the results indicate that the proposed model outperforms the state-of-the-art models of similar complexity.