Reviewerly/exHarmony huggingface dataset:
Implementing Three Factors Authentication System (3FA) to Maintain Employee Attendance Record to Enhance Security

Today as we all know that maintaining of employee credential have been some major issues around the world. Finding it more difficult to keep track of employees. With this, the company, institution, or government entity does not know who has entered their work-spaces to steal important information from their systems or databases. This serves as a deep wound for any company that does not keep track of its employees. This has been a major issue within so many Africans country, most especially Liberia. where so many companies, institutions, and governmental entities losing huge amounts of data. The means by which they do not track employee records have been with the traditional sign-in method, which has to do with the pen and paper or book sign in and sign out method, which can easily be stolen or damaged. This is one of the methods that employees use to not sign in when they get to work in order to steal data without anyone knowing that they were at work that day. Coming up with a method that will enable strong security and secure maintenance of employees records with less consuming and a strong level of three-factor authentication (3FA) will enable all employees to sign in with their three-level credentials, which have to do with something you know (knowledge), something you have (possession), or something you are (Inherence).The trustworthiness of identification is increased because this data cannot be copied or hacked. Furthermore, Three (3) Factor Authentication attempts to demonstrate, from a broader perspective, why it is more important to review your security procedures in order to prevent unauthorized persons from gaining access to your systems and obtaining data and information that you have not provided them with. Our system is going to allow user signing in with their user credential on your system after entering your username and password which authenticate you into the system, you as an employee to be verified and attendance to be mark you will use your Face, recognizing you and comparing it to the data stored with in the database for verification, after than been verified and mark your attendance for the day. This method going to be used is less time consuming and low cost attached.
Digital Image Watermarking using DWT and Encryption with RGB Pixel Shuffling and PWP Techniques

Images are the most common word for every individual. Almost every area deals with image processing. Especially in security and military applications image processing is used. One the major stem of image processing is image watermarking and image cryptography. This research involves both watermarking and cryptography. The wavelets are a big family in which symlett wavelets are used. This paper deals with watermarking as well as encryption which uses Discrete Wavelet Transform (DWT), RGB (RED, GREEN, BLUE) pixel shuffling and Parallel Whirlpool Algorithm (PWP) techniques. A color image is processed where RGB pixels which are shuffled according to the procedure which does not affect the intensity, while retrieving back the original information. Techniques like De-noising, segmentation, smoothening and sharpening used for pre-processing the image. The different variants of DWT are considered to obtain proper watermarking. Watermarking with cryptography is the unique concept for proper security. The results were promising to obtain perfect security
Indian Sign Language Phrase Estimation Using PoseNet

Communication between people is very important to convey thoughts and ideas. The predominant mode of human communication is speech. But often people with communication and hearing impairments are not able to communicate properly with other people. The most popular form of communication for these individuals is sign language. Although there are at least 15 million deaf signers who use Indian Sign Language (ISL), there aren't enough technologies developed to interpret ISL and facilitate communication for signers who use it. This calls for the development of more resources for creating awareness and providing easy and accessible communication to deaf people. The aim of this paper is to aid in this initiative. In this work we use a webcam to capture the sign shown by the person, identify them using a pre-trained machine learning model PoseNet and classify them with ml5.neuralnetwork with an average accuracy of 92.85%.
Analysis on Quantum Cryptographic Approaches for Image Encryption

To secure and protect the photos from unfair tactics, the encryption process is used. the advent of the digital era has made it exceedingly easy to hack any data kept on the cloud. Encryption is critical to ensuring communication security, which is a crucial issue. There are numerous photo encryption methods that mostly rely on chaotic logistic maps; however, they have downsides such a small key space and low security. Current analysis introduced experimental results of quantum chaotic cryptosystem,3D-Chaotic map and quantum chaotic logistic map. This paper we measure image encryption parameters are Entropy, structural similarity index measure, combined average changing intensity and net pixel change rate which defines utilization of suitable image encryption approaches. For the cypher and the original image, we quantify the correlation coefficient value in both the horizontal and vertical positions with comparison of all three existing Approach-es performance. The scientific and practical economic results reveal that the quantum chaotic cryptosystem has outstanding performance and in future with utilization of advanced Approaches we can resist various cryptographic attacks.
Web Mining: Opportunities, Challenges, and Future Directions

With the objective of gleaning necessary knowledge and insights from the enormous amount of data accessible on the World Wide Web, web mining is a well-known research field. Web mining has developed into a crucial tool for companies, researchers, and organizations to find hidden patterns and linkages that might guide decision-making as the amount of web data grows exponentially. An in-depth analysis of its capacity to identify patterns, find fresh insights, and enhance decision-making is provided in this study. Additionally, providing insight into consumer behavior, industry trends, and business intelligence can assist companies and organizations in gaining a competitive advantage. Web mining faces many difficulties, including poor data quality, privacy issues, and ethical dilemmas. Additionally, the sheer volume of data available on the web presents a challenge in terms of processing, storage, and analysis. This paper discusses these challenges in detail and provides recommendations for overcoming them.
MRI Segmentation Using Deep Neural Network-based Unet Architecture for Brain Tumor

Image segmentation is a very basic task in medical image analysis, including brain (magnetic resonance imaging) MRI segmentation. Two popular deep neural network learning architectures for image segmentation are the autoencoder and U-Net. In this study, we compared the performance of these two architectures on brain MRI segmentation using a publicly available benchmark dataset. We trained both architectures using the same training and validation sets, and evaluated their segmentation performance on the test set using standard metrics like dice coefficient and mean absolute error. Our results show that the U-Net architecture outperformed the autoencoder in terms of segmentation accuracy and spatial resolution, especially in cases where the segmentation boundaries are complex or ambiguous. The U-Net architecture was able to capture fine-grained spatial information and produce accurate segmentation results, while the autoencoder tended to produce less accurate and more blurry segmentation results due to the compression and reconstruction process. These findings suggest that the U-Net architecture is more suitable for accurate and fine-grained segmentation tasks, while the autoencoder may be more suitable for tasks that require compression and reconstruction of the input image.
Enriching Transformer using Fourier Transform for Image Captioning

Transformers have an encoder-decoder architecture which is widely used as an image captioning model. Identifying the key elements and the underlying relationships between them is the challenge of image captioning. The introduction of the Fourier transform in this architecture is expected to perform better than other state-of-the-art models. Fourier transform is a signal processing tool that allows us to decompose a signal into its frequency components. In signal processing, an image is often treated as a two-dimensional signal, where the brightness of each pixel represents the amplitude of the signal at that location. With this intuition, an additional Fourier layer is added to the transformer architecture to enhance its performance. The Flickr dataset is used for training the proposed model in its initial phase. The model is tested against state-of-art-model using various evaluation metrics like BLEU-n, meteor, rouge scores, etc. During the evaluation, the suggested approach produced better outcomes.
Audio-Visual Deepfake Detection System Using Multimodal Deep Learning

With the rise of deepfake videos in today’s digital landscape, there is a pressing need to develop advanced detection and mitigation technologies. Deepfake videos, which use machine learning algorithms to manipulate and alter audiovisual content, can spread false information, create confusion, and even cause harm. To address this issue, our work leverages Deep Learning technology to process audio-visual data in real-time through a web interface, specifically a browser plugin. Our approach uses a multimodal neural network that is fed extracted audio and visual features from a video for deepfake prediction. The model achieves a maximum validation accuracy of 90 percent and is used to build an API that is responsive, and low-latency. Our end-to-end solution is implemented as a Chrome extension using JavaScript that communicates with the API. With this solution, we aim to contribute to the development of advanced deepfake detection and mitigation technologies that can help prevent the spread of false information and its potential consequences.
A Transition of Face Recognition to Mask Face Recognition Using Improvised Attention DL Model

With the emergence of the communicable fatal Covid-19 virus and increased level of air pollution, every individual is adapting to wear a facial mask now a days, due to which the existing Face Recognition Models are experiencing reduction in accuracy rate. The study in this paper is aimed to analyze the rate of drop in test accuracy for the task of face recognition from normal face recognition to the recognition of masked faces. Also, it targets to analyze the behavior of Convolution Block Attention mechanism with 10 different deep learning architectures as trunk branch. This paper discusses a novel idea to generate a sample space for training by various combinations of original face images, simulated masked faces and augmentation, from the original 2 datasets which improves the test accuracy of the models. The combination of InceptionV3+CBAM proves to be the best model with the peak accuracy of 88.78% & 88.62% and least model’s inference time of 512 & 923 milliseconds on Yale and Casia subset datset respectively among all other 9 implemented attention models. Moreover the proposed implementation of InceptionV3+CBAM also succeeds to achieve better accuracy as compared to existing models by using proposed set of dataset (Original face Images+ Simulated Mask faces+Augmentation) type for training.
Screening of Baggage X-ray Images Using Convolutional Neural Networks

For security screening of X-ray baggage, we address the problem as image classification task by the application of trained deep convolutional neural networks (CNN). Large quantities of training data are typically needed when using a deep multi-layer CNN technique to build a complete framework that obtains features and performs screening. To solve this problem, we use a transfer learning methodology that allows a pre-trained CNNs to be particularly tuned later for achieving the classification of baggage. In our study, for the classical threat and non-threat image classification, we experimented the classification task by a newly designed lighter CNN network without pre-training and compared the classification performance of pre-trained neural networks with SVM classifier using the features extracted from various layers of CNNs. Pre-trained networks achieve 99% classification accuracy and precision and exceeds the performance of CNN network without prior training. Further, the classification task by a newly designed lighter CNN network without pre-training achieves 96% accuracy, 95% precision, 5% false positive rate with SVM classifier.
The Cognitive Load Based on Language Big Data and the Construction of Intelligent Classroom for College Oral English Mixed Teaching

Oral English course is one of the most important courses for English majors. It is also a practical and practical course. However, the English education at the university stage pays more attention to the written test results, so the students' ability to write questions is more prominent, and their oral expression ability is relatively weak. It is an effective teaching method to construct an intelligent classroom in the mixed teaching of college oral English. It can be used as a method to improve students' oral ability and also as a tool to learn new languages. The main idea of this method is that students must communicate through speech, writing or listening, which will help them improve their communication skills and make them more confident in their oral ability.
WorkoutNet: A Deep Learning Model for the Recognition of Workout Actions from Still Images

Workouts or Exercises are some repetitive physical activities essential for maintaining good health. Workouts make us relax and happy, give us more energy, lower the risk of chronic disease, and rejuvenate our bodies and minds. Proper technique and posture are essential to get the most out of any workout; the same workout may lead to injury if performed incorrectly. This paper presents a method to identify a person’s workout action from realistic still images. In contrast to video-based action recognition, the challenge is more difficult for still photographs because the Spatio-temporal information is not present in images. This study proposes a deep learning-based model named WorkoutNet to recognize workout actions. The method achieved remarkable results on the Workout Action Image dataset, consisting of 2796 images prepared by us and on the publicly available Exercise image dataset. The suggested approach predicts different workout actions and classifies them into ten categories with high accuracy. The proposed WorkoutNet model attains 92.75% validation accuracy in classifying the workout action images, succeeded by the XceptionNet, VGG-19, DenseNet121, InceptionV3, MobileNet and VGG-16. Further, the classification performance has been evaluated using the F1-Measure and Confusion Matrix. The suggested model can be utilized in real-life to identify workout action images and assist humans doing workouts without a trainer or guide in understanding the right way.
Blockchain-Enabled Healthcare Records Management: A Survey of Implementation Strategies

Electronic Health Records (EHR) is a technology that enables users to monitor their medical data. Over several healthcare organizations, it maintains computerized records. Enterprise-wide data exchanges and other networking exchanges and technologies are used to exchange records. People today demand fast access to their medical records. The privacy and security of patient medical records are a concern because the health industry has rapid access to data. Thus, a blockchain-based solution can help to solve this problem. A blockchain network is used in healthcare to store and distribute health information amongst hospitals, diagnostic laboratories, pharmaceutical firms, and physicians. This study uses Blockchain Technology (BT) to analyze electronic healthcare data management. Insightful analysis of patient data is made possible by such a technology, which medical institutions can utilize. After an analysis of the survey of the relevant literature, a comparison of the many earlier studies is presented. Encryption, the keyword (KW) search, server and user verifiability, storage integrity, and dynamic updates are all aspects that have been compared. After examining. the author Tan et al., (2022) agrees with all the key features such as server-side verifiability, encryption, KW search, user-side verifiability, storage integrity, and dynamic updating
Analysis and Early Diagnosing Tool for Learning Disability using Machine Learning Models

The learning deficit is one particular type of neurological disorder that can affect a kid's mental abilities, word identification, ability to write and read, as well as their capacity for problem-solving. These disabilities are known as Particular Learning Disabilities because they primarily influence individuals' academic performance, particularly reading (dyslexia), writing (dysgraphia), and trouble with mathematical (dyscalculia) (SLD). These pupils must be discovered at an early stage so that, with the right assistance, they can gain sufficient experience with a particular task and hone their disability-related skills. The testing scale tool has been suggested for use in diagnosing and identifying SLD. The suggested tool enables the student who may have SLD to participate in the quiz. Depending on the type of test, some individual questions are repeated. The machine learning algorithms CNN and Random Forest receives the test results as input after the test is over. The algorithms predict children with learning impairments based on student grades and the amount of time spent by the kids. The suggested tool is used to create a user-friendly, integrated system for diagnosing reading, writing, and math impairments. It also suggests to parents and instructors the best methods and instructional activities.
Siamese Network Based Anomaly Detection Framework for Robust Federated Learning

Federated learning is a decentralized approach to machine learning that has increased in popularity in recent years. It enables several participants to train a common model without revealing their data. This strategy is, however, susceptible to attacks from malicious clients who can launch targeted model poisoning attacks and reduce learning performance by delivering false model updates to the server. It is necessary to identify and eliminate such fraudulent updates and the attackers behind them to preserve the robustness and security of the shared model. In this paper, a novel Siamese network-based architecture for robust federated learning is proposed. which can identify and eliminate harmful updates. Our method is assessed and compared with other approaches for adversarial detection in image classification tasks in a federated setting, using a CNN model. Experimental findings demonstrate that the system offers reliable federated learning that is resistant to both targeted model poisoning and untargeted Byzantine attacks. Overall, the study advances the development of secure federated learning systems, by suggesting a novel method for identifying and deleting fraudulent updates in federated learning,
Transliteration from Hindi to English Using Image Processing

Transliteration is the process of changing a word or a text from one script to another while keeping the phonetic and orthographic features of the source language. Transliteration is an important part of natural language processing, especially for communicating with people who speak different languages. This paper focuses on the transliteration of the Hindi language into the English language, as Hindi is the official language of India and there is a huge amount of data in Hindi that must be translated into English for regional and global use. It is essential to transliterate Hindi into English so that speakers of other languages can comprehend and communicate in Hindi. This paper describes a hybrid approach for transliteration from Hindi to English that includes image processing and a model trained with attention, as well as its applications in diverse fields. The system is evaluated using a dataset of Hindi text images, and the outcomes show that the proposed method improves existing transliteration systems in both, accuracy and speed.
Revolutionizing Cloud Infrastructure Management: Streamlined Provisioning and Monitoring with Automated Tools and User-Friendly Frontend Interface

Cloud infrastructure provisioning has become a crucial aspect of the software development lifecycle, as companies shift from on-premise server management to cloud-based services. This transition allows organizations to focus on their core business rather than worrying about server and hardware management. The DevOps culture has further enhanced this approach by utilizing automated processes for delivering applications across various environments. Continuous Integration (CI) and Continuous Delivery (CD) have revolutionized application development and release management, facilitating seamless delivery with integrated feedback. To automate various tasks in the CI/CD framework, the use of GitHub Workflow has been proposed. It allows defining workflows as a series of jobs and triggering them automatically based on specific events, thus enhancing the efficiency and reliability of the development process. Additionally, its pre-built and custom actions provide added flexibility and functionality, making it a valuable tool for modern software development teams. This paper demonstrates the feasibility of designing an effective framework that achieves continuous integration, testing, and delivery of cloud resource provisioning. By employing a build pipeline concept, the framework can automate various tasks such as source code compilation, code analysis, test execution, packaging, infrastructure provisioning, deployment, and notifications. This brings an idea to develop a simple software solution that will use GitHub Actions in the CI/CD pipeline, speed up the process of provisioning cloud infrastructure through a simple frontend and track the resource utilization of provisioned resources through a dashboard, so delivery of cloud infrastructure will happen quickly.
Black and White Image Colorization Using Convolutional Neural Networks

Colorization of black and white images has become increasingly important with the advent of high-quality and high-resolution pictures in modern-day life. This project proposes a method for converting black and white pictures to color pictures. This project is mainly designed for coloring black and white images using the Deep learning Algorithms like CNN (Convolutional Neural Networks. This proposed method produced great results with a very little mean square error of 0.3174.