Reviewerly/exHarmony huggingface dataset:
SupRes: Facial Image Upscaling Using Sparse Denoising Autoencoder

Even in this era of digital images, still many images and media are hazy, pixelated, and blurry. This could be due to low-quality imaging sensors, poor image stabilization, or the image itself being old. This study proposes the Sparse Denoising Autoencoders (SDAEs) for upscaling blurry images. The performance of the proposed SDAEs is then compared with the deep learning architecture, Pix2Pix Generative Adversarial Networks (GANs) by primarily focusing on the facial images. The experimental results show that the SDAEs give slightly better results than GANs. Additionally, the SDAE architecture is computationally 30% efficient when compared to the Pix2Pix GAN.
Real-Time Translation of Sign Language for Speech Impaired

Sign language is a visual language that uses hand gestures, change of hand shape, and tracking information to express meaning, and is the main communication tool for people with hearing and language impairment. Given the barriers faced by speech-impaired individuals, this system introduced a tool that bridges communication gaps and supports better interactions. This work focuses on introducing a tool that should bridge the communication gap among speech impaired community. The work involves the development of a system that enables two-way conversation between people with speech disorders and noisy people. LSTM networks were studied and implemented for the classification of gesture data because of their ability to learn long-term dependencies. In real-time, the sign language gestures of speech-impaired individuals are fed to the system by the device's computer vision capabilities. These gestures are recognized using deep neural networks, while hand recognition is cracked with edge detection algorithms that interpret in both text and speech formats. The model is trained with the dataset that is collected using holistic key points from the video of the person which detects the pose, face and hand landmarks. These will convert speech to text and finally displays the relevant hand gestures. This model can predict with an accuracy of 90%, showing the feasibility of using LSTM-based neural networks for the purpose of sign language translation.
Implementation of Abnormal Event Detection using Automated Surveillance System

In military zones, banks, streets, hospitals, borders, airports, and schools, surveillance systems are widely employed Security personnel examine the footage from check- in rooms to look for unusual humanoid activity. This research study presents a new surveillance system that uses machine learning to detect human activity from a large number of cameras. The Internet of Things (IoT) technology is used to capture images from cameras in both inside and outside surroundings. One of the main issues with object detection that has a significant impact on performance is unevenness issues. This study addresses an issue that results from fault detection. In order to tackle complex problems, a detection approach that identifies strange patterns are implemented Particularly efficient techniques in this area include those based on autoencoders and the Generative Adversarial Network (GAN). Firstly, this study suggests a GAN-based detection model that uses an auto-encoder as the originator and two isolated discriminators for each usual and irregular input; next, two new loss functions, Patch loss and Anomaly adversarial loss are considered, and additional compound to train the model together. Using this technique, the proposed model can be effectively optimized This study assesses the performance of the proposed model against conservative standard datasets, such as the MNIST, Fashion MNIST, and CIFAR 10/100 records, as well as against a real-world industrial dataset and the defects in smartphone cases. Finally, experimental results show that the proposed approach surpasses cutting-edge techniques in conditions of the average area under the ROC curve, demonstrating the effectiveness of the proposed approach (AUROC).
Prognosis of Breast Cancer Cells using Image Processing

One hereditary disease, breast cancer, is brought on by mutations in the genes that govern our cells' functioning, especially their development and division. It is a key contributing factor in female fatalities. It is a malignancy that develops in the breast tissues (gland that produce milk) Among the entire world, breast cancer is thought to be the most frequent form of cancer in women. Mammography, machine learning, image fusion, CNNs (convolutional neural networks), and multispectral needle endoscopy were previously utilized to detect breast cancer but that methods cannot differentiate between benign, malignant, and normal types of cancer exactly, so neural networks is used to identify breast cancer in order to make a more accurate classification. Image processing, algorithm segmentation, feature extraction, etc. are some of the processes used. The methods previously used have some drawbacks, such as screening, faulty scanning, data gathering, and noise detection. A greater knowledge of the Cancer pattern can be obtained by using advanced technique.
Multiple Object Recognition from Smart Document Images using YOLOv5s

Multiple object recognition from various ID proofs will give more security as well as authentication of the data. In general, various ID proofs showcase objects in various ways, so manually identifying common objects is a time-consuming process. Some of the existing work like YOLOv3, YOLOv5, CNN, and Faster RCNN, all concentrate on one single object detection from the same dataset category, while the proposed work concentrates on heterogeneous datasets with the YOLOv5s model. The proposed work will automate the multiple object detection and recognition from various government id proofs as well as overcoming overlapped object recognition. The proposed model includes deep YOLOv5, which contains 19 convolution layers and 5 pooling layers. The proposed model detects the object from 1000 manually collected various government ID proofs like driving licence, PAN card, Aadhaar card, and voters' ID. The model is trained with 750 datasets and tested with 250 datasets, and finally validated with 50 datasets. The model clearly detects and recognize the name, unique identification number, date of birth, and photograph with 94.6% accuracy. Also, the model recognizes overlapping signatures with better accuracy.
Hand Gesture Controlled Car using Bluetooth Modules and Accelerometer Sensor

This research work demonstrates the usefulness of an accelerometer sensor and how it can be used to move an object using hand gestures. The device uses two Arduino UNO models, one as the transmitter and other as the receiver. The functioning of the device is based on the serial data transmitted and received through the Bluetooth modules. The data to be transmitted and received is produced by the accelerometer sensor. Based on the data received, the motors run to produce the desired movements. The device can communicate within a range of ten meters. Such a device can be used as a gesture-controlled wheelchair for handicapped people. The code for functioning is uploaded to the Arduino UNO by Arduino IDE software. The major challenge that was faced during the preparation of the model was the connection of the Bluetooth modules. Each module sends and receives data at different baud rates. If the baud rates were not same, communication will not be established. One module serially transmits the data and another module serially receives it. Different prototypes are already available with slight variations. Instead of Bluetooth modules, other modules are available to establish wireless communication. Examples are: nRF modules, RF pair, Lora WAN. As Arduino UNO is larger and consumes more space, small boards such as Arduino Nano or Arduino Lilypad has also been implemented in the available models.
Applying Deep Learning Methods on Spam Review Detection

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. There are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. These fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. The issue of spam review finding has been solved by the introduction of prominent deep literacy methods. The focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. This initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector Machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency.
Identification of Voting Patterns using Clustering Methodology

The act of separating a population or set of data points into a few groups or clusters so that data points in the same group are more like each other and distinct from data points in other groups is known as clustering. The purpose of this study is to categorize the respondents to identify groups with similar attitudes about science and technology and analyze their views. The difficulties of cluster analysis, determination of distance measure, number of clusters, and database structure have all been noted as possible issues with cluster analysis. To explore the respondents' grouping tendencies, several clustering approaches such as K-means, Hierarchical clustering, and so on are utilised The Hierarchical Clustering methodology itself may provide the analyst with the ideal number of clusters; human participation is not necessary. Dendrograms provide in clear imagery that is useful and simple to comprehend The centroids are computed by the K-means clustering method, which then iterates until it finds the ideal centroid It presumes that there are already known quantities of clusters. The flat clustering algorithm is another name for it. Since the data is binary, the clustering methods may be used to group the respondents. The clustering methods will be applied to the survey data by tracking the resultant decisions. Currently, all clustering algorithms have been used and it has been discovered that the data contains three or four clusters, each of which specifies a voting pattern that may be of interest.
A Extensive Survey on Sign Language Recognition Methods

The human community depends heavily on communication, and there are many different natural languages. Sign language is another language unique to the deaf and dumb human population. Due to the fact that most people cannot comprehend sign language and there is always a chance of misinterpretation, the sign language community has many problems. In this situation, technical assistance is offered in the form of a translation system that can comprehend the signs and translate them into the form of text or speech, making it easier for the sign language community to communicate. The advancement is being made possible through various sign language repositories, algorithms that assist in translating natural language into sign language, and numerous methods for gesture recognition. This work offers a thorough overview of all the datasets, algorithms, and new techniques to provide a better starting point for future researchers.
A Composite Technique for Creating Contemporary MRS using Association Rule Mining &amp; CF

The vast amount of information on the internet has made information available, but it has also made it difficult for users to choose the information that is necessary or interesting to them. To address this issue, recommender systems (RS) were developed to find relevant information using information filtering. Using RS, users may find the appropriate data from a vast collection. There are several types of RS, but those developed using collaborative filtering techniques have proven to be the most effective for a variety of issues. One of the most popular RS accessible is called the Movie Recommendation System (MRS). In this paper, suggestions will be made based on the shared features of user items. Both user objects and item objects are frequent in the movie recommendation system. In order to provide stronger suggestions, this paper integrates the collaborative filtering technique with association rule mining. By integrating collaborative filtering with association rule mining, a hybrid strategy that takes use of both techniques' advantages can boost the recommendation system's performance. Consequently, the recommendations that were generated can be regarded as strong recommendations. Collaborative filtering uses the past behavior of users to make recommendations, while association rule mining looks for patterns in the data to identify items that are frequently bought together. Combining these two approaches can help overcome the limitations of each individual method, such as the need for a large amount of data for collaborative filtering or the lack of personalization in association rule mining. This paper combines data mining and conventional filtering techniques to provide movie recommendation suggestions.
Exploring the Applications, Challenges, and Issues of Sentiment Analysis

Everyone in this rapid evolution generation makes use of online resources. There is a huge amount of textual content available online from several sources, including social media apps, e-commerce sites, movie recommendation system, etc. Everyone in the modern society provides a variety of reviews for a specific product or item. An effective and smart technique is required that can analyze and provide the polarity of this textual data. since it is impossible to manually review such a vast volume of user-generated data. For this user-generated data, automatic sentiment classification is possible using a variety of tools and methods. Opinion mining and sentiment classification benefit from the dependable and accuracy of machine learning. Moreover, the sentiment analysis and evaluation process are fraught with difficulties. These complexities make it difficult to interpret sentiments accurately and figure out the right sentiment polarity. This article describes an overview on sentiment analysis(SA) applications, challenges, and issues encountered by researchers, with the goal of providing a systematic review on sentiment analysis and related areas for future research.
Optimising Face Recognition System using Contrastive Learning and Contrastive Loss

The current issue with facial recognition systems is that they can be influenced by poor illumination or inadequate image quality. Because camera angles are obstructed, the data may not match the person's nodal point. There are many more difficulties, such as variations in facial appearances such as illumination, pose, expression, and aging. To tackle this difficulty, this study has developed a new strategy by utilizing contrastive learning for face recognition system. Contrastive learning is a Machine Learning (ML) approach that integrates information from two faces to understand the unique qualities of an individual's face. Contrastive learning improves the system's vision by generating two vectors, positive and negative, by varying the image contrast. The model is then trained to learn about images and recognize them more efficiently and effectively by establishing those two vectors.
Moodle Data Analysis For Effective Online Teaching And Learning

This paper presents the results of an exploratory study that analyzes data from 500 students in MOODLE to understand the students' activities and how they relate to their course performance. The frequencies of the student's activities and the transitions between these activities are analyzed. This article analyzes the student's participation in Moodle activity via python code and their performance based on the marks scored in the quiz activity, which was given on Moodle platform. The analysis of students' performance of each question in that quiz. Also, the analysis of the easiness and difficulty of those questions asked. This study performs the facilitation value and discrimination index to find the easiness and difficulty of all questions, taking a percentage of questions which was easy, moderate, and hard. This is useful for the teaching staff to find a way to improve student performance and motivate them to score good marks.
Peer to Peer Credentials Sharing using RSA and Session Token Techniques

With the increase of different online platforms, the information stored also increases, and sharing the data across users sometimes cannot be done without sharing the account. This article proposes 2 strategies for securely sharing credentials with different users, considering various security aspects and maintaining user privacy. And also a comparison of the strategies for using the right one for our use and providing a sharing mechanism where users will be able to give access to a particular account to another person without actually sharing credentials or on a fixed period. Also, how the methods differ from the existing authentication mechanism used in online web applications by taking different measuring factors like Memory, Computation, and Latency. This can be achieved based on the concepts of session details saving of the authentication process in conjunction with the cryptography encryption algorithm to transmit data across the internet.
Spam Email Filtering using Machine Learning Algorithm

Email is one of the most used modes of communication by many industries and IT sectors. Even common people used to communicate through email about business related in-formation over the internet As technology grows, the threat to the individual has also been increased. In the Email system, the threat takes the form of spam email. There are several existing spam filtering methods currently in use including knowledge-based techniques, learning-based techniques, clustering methods, and so on. The proposed work provides an overview of several existing methods that use Machine learning techniques such as Naive Bayes, Support Vector Machine, Random Forest, Neural Network and formulated new model with improved accuracy. However, in this work, the discussion and consolidated analysis has been done by comparing several email spam filtering techniques.
Statistical Analysis of Design Aspects on Various Graph Embedding Learning Classifiers

Graphs representation learning is an emerging research topic. Graph representation learning generates vectors that capture immense graphs' structure and properties. The quality of graph representation vectors affects machine learning downward tasks like node categorization, edge estimation and anomaly detection. For efficient graph representation vectorization, both traditional graph embedding techniques and methods based on graph neural networks (GNN) have been described. These approaches work on “static & dynamic graphs”. In contrast to dynamic graphs which constantly adds new nodes and edges, static graphs consist of a single unchanging structure. This paper reviews traditional and GNN-based embedding approaches for both static and dynamic graphs. This paper summarizes GNN challenges and proposes a framework. Finally, real world applications of these techniques are explored.
Secured Data Cluster Distribution and Propagation using IBBE and Attribute-based CPRE

Cloud services are one of the key factors in many real time applications. It involves huge amounts of data transfer and thereby data privacy and security are the major concern in cloud computing applications. To address this issue, Secured Data Cluster Distribution and Propagation using IBBE and Attribute-Based CPRE (SDCDP), a model has been proposed in this manuscript where the data is being shared with a lot of users in groups with proprietary distribution data in which the owners of the data can simultaneously transmit the data that is being encrypted to a recipient batch by specifying the recipient's identity like a secure and easy way. The encryption technique used is a proxy kind of encryption which is a conditional based on merit and release time to ensure that only data distributors have access to the data access policy and distribute to other groups after release by providing a rewrite key to the cloud server. Data owners will have complete and timed-release controls used over distributed cipher texts, which are generated by the encryption conditions associated with the attributes and release time. The integrity and adequacy of proposed approach was demonstrated through theoretical analysis and experimental results to test this exhibit at each stage. Theoretical analysis and experimental results to assess the exhibition at each stage to show the adequacy of the proposed plan. To maintain the guarantee of the private resource batch transfer, Identity-Based Broadcast Encryption (IBBE) process will be used to perform data transmission. The owners of the data can publish their data that is encrypted for batch when the particular time arrives and the key that is needed that is given to the person will get an email or number that is different from others and name for a particular user. Therefore, there is a need to prove that the owner of the resource will transfer resources with other batch people in an easy and secure way.
Investigation on Privacy Hazards in Social Area Network

The Social Area networking Platform (SANP) is a rapidly developing technology that allows users to meet friends, family, and others, who share their interests and express their personal feelings. The potential privacy threats include, Paedophilia, identity theft, spoofing, data breaches, the Koobface worm, phishing, and evil twin attacks. These attacks are major threat to user privacy information. Many people are not aware of the risks of impersonation. To avoid this, it is necessary to understand social network privacy concerns. This scenario includes a study of social network privacy concerns, as well as hazards and risks from recent events. This study significantly contributes to the classification of dangers and categorization of frauds relevant to social networks. Following the completion of a comprehensive research study of privacy concerns in SANP, optimized techniques were identified and performance for privacy threats was also evaluated. The proposed system has used Euclidean distance to evaluate the dataset based on privacy threats. It will be useful in establishing trustworthiness on social media.
Hand Detection and Gesture Recognition in Complex Backgrounds

In this paper, a Convolutional Neural Networks (CNN) based hand detection model that, on a major note, focuses and segments only the hands from any complex background using the Open-CV libraries for real-time computer vision, is proposed. Based on the features extracted from the region of interest, the VGG16 CNN Architecture classifies and predicts the gestures, based on the trained data. The system is trained by using binary images, so that the background is eliminated and classification is done only on the edges. This approach increases the performance of the system with respect to time. The major step involved in the proposed system is Background Elimination, which is carried out using a series of Open-CV methods and functions. Hand Detection Systems find applications in various domains ranging from Sign-Language Detection to Human-Computer Interaction.