Reviewerly/exHarmony huggingface dataset:
An Optimized Learning Model for Image Denoising using Modern Deep Learning Approaches

In the domain of image denoising, the existence of several forms of noise, such as Gaussian, impulse, salt, pepper, and speckle noise, presents considerable obstacles. A viable strategy for resolving this issue is optimized convolutional neural networks (O − CNN) which are receiving more and more attention. Numerous standard CNN techniques have been developed and applied to denoise images with different datasets being used to evaluate their performance. This paper seeks to offer a thorough overview of several CNN image-denoising methods with a meta-heuristic optimization approach known as adaptive wolf optimizer (AWO). The approaches are organized and looked at, and the most popular datasets for analyzing O − CNN image-denoising algorithms are reviewed. A selection of O − CNN-based image-denoising techniques was carefully examined and analyzed in this study. The goals and underlying principles of the O − CNN approach were elucidated. In addition to detailed explanations, certain O – CNN image-denoising techniques were represented graphically. With O − CNN, the suggested reviewing image denoising. The studies on image denoising using O − CNN from the past and present were chosen. Future research directions and potential obstacles were also explained.
Exploring Sentiment Detection in Healthcare: An Exhaustive Analysis

In our daily lives, sentiment analysis is essential. People usually make decisions after seeking advice from others. We used to rely on our judgments before the internet on the advice of our friends and relatives. But now, we have high-performance source sentiment analysis, which collects and analyses opinions from a variety of channels, including social media. To correctly represent emotion in a phrase, we must concentrate on idioms, sarcasm, negations, and context terms. A range of deep learning and machine learning techniques can be used to overcome the aforementioned issues. The aforementioned parameter will be used by the algorithm to identify the polarity of a statement. This study proposal covers the problems with sentiment analysis as well as its numerous levels. The review talks about the challenges and particularities of sentiment analysis. These have to do with issues like textual information, peculiarities of language, emotional vocabulary relevant to particular domains, and security and confidentiality of data. The report also emphasizes how sentiment analysis might promote public healthcare initiatives, boost pharmaceutical methods for making choices, as well as improve patient care.
Monitoring the Student's Entry and Exit Time in the Classroom

Monitoring the student's entry and exit time into a classroom is a crucial task. This system will be a solution for performing that crucial task. In this system, for face detection and face recognition, two pre-trained models were imported and utilized to create face embeddings for a collection of photographs kept in a file directory. The face embeddings and corresponding labels are then used to train an SVM classifier using the scikit-learn library's SVC class. To detect and recognize the students, the pre-trained face detection and recognition models were loaded using OpenCV's dnn module. Then the saved face embeddings and labels were also loaded using pickle, which was previously generated by a face recognition model. Face detection is performed using a Caffe model trained on the SSD framework, while face recognition is done using OpenFace. The system identifies the student's faces using embeddings that were generated by pre-processing the images and were saved in the face_embeddings.pickle file. It then matches these embeddings to the corresponding labels that were saved in the labels.pickle file. Then the input is captured from the webcam and the optical flow analysis is applied to detect motion in the video stream. After that, it initializes the input image with a blurred, grayscale image and sets the starting points for the motion tracking. It then loops over the captured frames, blurs and converts them to grayscale, and uses calcOpticalFlowPyrLK to calculate the new points based on the old ones. If the new points are within a certain range, it draws a line between them on the input image and displays it. If the points move outside a certain threshold, it triggers an event to log the entry or exit of a student from a classroom and appends these details into a text file accordingly. Finally, this text file is sent through email to a specified list of recipients (faculties) on a daily basis.
Smart Contract-Based Advanced Financial Security System: A Literature Review

The current research provides a literature evaluation of works from sources such as IEEE and Science Direct that explore testing and analysis methodologies and approaches for smart contracts. Smart contracts built on the blockchain have attracted a lot of attention from academics because of their unique properties. These include the immutability of transaction records, the automatic execution of contract instructions, and the decentralized building of trust. No one can deny blockchain's revolutionary potential across a number of sectors, but there are still technological hurdles to overcome before smart contracts gain widespread use. Solutions to these problems, as well as investigations into potential new uses for smart contracts outside of their present financial and logistics-related contexts, should guide future research efforts. In general, smart contracts built on blockchain technology have the power to revolutionize a number of sectors and open the door to a future that is more decentralized.
Exploring Language Models: A Comprehensive Survey and Analysis

The domain of natural language processing (NLP) has witnessed significant advancements with the arrival of large-scale pre-trained language models to revolutionize NLP research and achieve state-of-the-art performance across various tasks. They have propelled the development of sophisticated NLP applications and deepened our understanding of artificial intelligence. However, the growing size and complexity of these models have given rise to new challenges and limitations. Concerns related to model bias, interpretability, data privacy, and environmental impact have become prominent. This paper explores the impact and potential of large language models in NLP, highlighting the advancements made and the challenges that need to be addressed.
Exploring Extended Reality (XR) in the Workplace - Applications, Challenges: Survey

In ordinary work life, extended reality (XR), which here collectively refers to virtual, augmented, and mixed (VR, AR, MR) reality, is becoming more prevalent. This essay offers a thorough analysis of academic works on XR that show modifications to the way labour is actually organized. We examine XR research's theoretical and methodological foundations as well as its application areas. The PRISMA statement was followed during the evaluation procedure. The primary application domains of XR were design, remote collaboration, and training. By modifying the perception of space, XR made it possible to overcome constraints imposed by time and space, safety, and resources. It is still rather uncommon to do research on XR applications in real-world workplace contexts, and it mostly focuses on three areas: teamwork, knowledge transfer evaluation, and working procedures. The most prevalent application of XR was virtual reality, albeit the hardware used varies depending on the situation. Collaboration, work habits, and knowledge transfer evaluation were the four XR research areas we defined, which roughly mirrored the application domains. Only a few recent studies used unique methods of data collection, such as videotaping participants' movement in virtual reality, in the articles we evaluated. We could not identify any XR-specific procedures in the articles we reviewed. For the time being, XR still has a lot of potential rather than definite general advantages in the workplace.
Understanding the Effects of Model Optimization Methods in Enhanced CNNs

In-depth examination of model optimization techniques and their effects on enhanced convolutional neural networks (CNNs) is done in this paper. The performance of Convolutional Neural Networks, which are the foundation of many computer vision applications, is greatly influenced by the optimization strategies used during training. The goal of this study is to have a thorough understanding of how different model optimization techniques affect CNN performance. In the context of Enhanced CNNs, the research assesses and contrasts well-known model optimization methods, such as Stochastic Gradient Descent (SGD), Adam, RMSprop, and others. This study's findings can help practitioners and academics make well-informed decisions regarding the best optimization approaches to optimize the effectiveness and efficiency of Enhanced CNNs in a variety of practical applications.
Sentiment Analysis of Tamil Tweets Using Deep Convolution Neural Networks

The article explores sentiment analysis, a widely researched topic in Natural Language Processing, primarily influenced by the expansion of social networks and e-commerce platforms. Traditionally, customer reviews are assessed using five different ratings. Nevertheless, the study aims to streamline the reviews of Tamil Movies from five categories to three: Good, Neutral, and Bad. To accomplish this, the paper introduces a Bidirectional LSTM model for sentiment analysis, capitalizing on deep learning methods to accurately categorize reviewers' sentiments and feelings. Thus, we have introduced a novel model named Combination of BI-LSTM and DCNN, which handles the classification of Tamil Tweets from Tweeter. The proposed model was trained on an extensive dataset of reviews, particularly for Tweeter Tweets. To optimize the data for our model, we performed various data pre-processing techniques, notably converting the dataset from an imbalanced state to a balanced one. As a result, our model demonstrated a substantial increase in accuracy (82.7%) compared to earlier approaches.
An Enhanced Triple Prime Encryption Approach for Image Encryption in LSB Steganography

The internet is developing rapidly, which leads to a sharp increase in security levels. Compression of data is used to save memory securely. At the same time, the protection of data is very important. The effective compression method gives efficient and easy-to-connect data. To protect data securely cryptographic and steganographic algorithms play a crucial role in the modern world. There are a lot of algorithms in cryptography to protect the data. In this study, three different ways to protect the data were proposed. Image scrambling is also an efficient way to protect the data. Discrete Cosine Transform (DCT) and Arnold’s cat map techniques were used to compress and encrypt the image. Multiple encryptions were used to encrypt the plain text. The first level is an alphanumeric scrambling sequence and in the second level, a new algorithm named Triple Prime Encryption (TPE) gives good results in security lastly one of the public key cryptography algorithms RSA was used to encrypt the text. The combination of Lempel Ziv Welch (LZW) and Huffman coding was used to compress the lossless data which provides an efficient security level and high compression ratio. Finally, the encrypted data is encoded into a scrambled image. To analyze the performance of this compression some parameters were performed which is compression ratio, compression time, savings percentage, Bits per pixel, and histogram analysis. To analyze the performance of stego images Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were performed. Experimental results show the best accuracy of the performance of the analysis.
Enhancing Idiom Classification on Text-based Conversational Data: Evaluating Transformer Model Performance through k-fold Cross-Validation

This study focuses on enhancing the classification of idioms within conversational data by leveraging transformer models. Idioms pose a challenges in natural language understanding due to their figurative nature and context-dependent meaning. Transformer models, such as BERT and T5, which are well-known for their ability to capture complex linguistic patterns and have been extensively explored for this task. The study incorporates embedding methods with these models and observes the results: a rigorous evaluation methodology, k-fold cross-validation, assesses of their performance. The dataset is divided into multiple folds, allowing for comprehensive training and evaluation of the models. This evaluation approach provides reliable and robust measures of the transformer models' effectiveness in accurately classifying idioms in conversational contexts. The findings from this study contribute to advancing the understanding of idiomatic expressions and demonstrate the potential of transformer models for improving idiom classification in conversational data. The transformer model BERT with ELMo embedding and the T5 model achieved better performance than the BERT base model.
Credit Card Fraud Detection Using Machine Learning Techniques

Due to the advancement of online transactions also widespread usage of credit cards for electronic payments, credit card fraud had major concerns in the financial sectors. Efficient and effective fraud detection techniques are urgently needed to protect financial institutions and consumers against evolving and sophisticated fraudulent activity. This study analyse the methods and developments used to find credit card frauds. With the objective to detect credit card fraud using an extremely unbalanced dataset, this research gives a thorough evaluation of 4 widely known machine learning algorithms: Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost. To assess how well the algorithms detect fraudulent transactions, the area under the precision-recall curve (AUPRC) and Area Under the Receiver Operating Characteristic (AUC-ROC) was estimated.
A Unified Process on Opportunities for Federated Learning in Big Data with IoT

Big Data (BD) has developed dramatically in recent years to accommodate a vast amount of data retrieved by novel services and as well as a wide number of applications of Internet-of-Things (IoT). Big data latent can allow storage, processing, and training to a central cloud to be fulfilled through as well as a wide number of applications, which involve transferring data from diverse sources to a centralized cloud for storing, handing out, and training. However, because the information could include sensitive information, this traditional technique raises serious privacy concerns. Federated Learning (FL) appears to stay a potential process strategy for overcoming this difficulty. However, there is a gap in the literature because there has yet to be a full inspection of FL for big-data amenities and presentations. Given an evaluation on the convention of FL for large data facilities and requests in this paper, to give universal booklovers motives for using FL for BD. Go on to the usage of FL process in critical huge data storing look at FL's potential in BD applications. View several major FL-big data initiatives and explore the topic's key issues, as well as some promising solutions and prospects.
Implementing Digital Signature with RSA algorithm to enhance the data protection in cloud

India which is an agriculture dependent country must pave its way for promoting development of intelligent and smart agriculture system. The emergence of Internet of Things(IoT) technologies has changed various traditional applications to smart home, smart city, smart health, and smart agriculture. Even though Wireless Sensor Networks (WSNs) have seen rapid bloom, securing data in the network is still a challenge. The main objective of a smart agriculture system is to ensure the security of the data that is transmitted over the network and the one which is stored in cloud. A Wearable Environmental Monitoring System (WEMS) is designed with the aim of preventing loss of data and providing secure data communications. IoT data analytics with cryptographic algorithms will increase the protection of cloud data. In this paper, a combination of public key or asymmetric cipher algorithms namely Rivest, Shamir and Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA) is proposed.
New Enhanced Hybrid Cryptographic Algorithms in Cloud Network

Cloud networks have become increasingly popular due to their scalability and flexibility, but they also face new security challenges. To address these challenges, researchers have proposed many cryptographic algorithms that can secure cloud networks from attacks. In this abstract, we provide an improved hybrid cryptographic algorithm that combines symmetric and asymmetric encryption methods to offer a high level of security as well as efficacy in cloud networks. The suggested algorithm uses the Rivest-Shamir-Adleman (RSA) algorithm for asymmetric encryption and the Advanced Encryption Standard (AES) algorithm along with blowfish algorithm for symmetric encryption. When these three encryption methods are used together, data and keys can be transmitted between cloud servers and clients in a secure manner. The enhanced hybrid cryptographic approach also includes a key management system to ensure the security of the keys used. We assess the suggested algorithm's performance and contrast it with the current cryptographic algorithms. The results show that our algorithm provides better security and efficiency than existing algorithms, making it a promising solution for securing cloud networks.
Prediction and Analysis of Next Website Request by Using Fuzzy Approach

Due to increasing amount of internet usage, mining useful information and knowledge from the proxy server log is evolving into a significant research area. Web usage mining is the method of extracting interesting patterns from Web usage log file. The browsing frequency of a user on each web site is used to analyze the behaviour. Earlier, large number of machine learning crisp methods were available for mining interesting patterns from proxy log transactions file. Earlier techniques depended on whether the website is present in the session or not. But proxy log transaction file contains numerical data. To deal with numerical data fuzzy data mining method is used for extraction of interesting patterns from numerical proxy log transaction file. In this paper, fuzzy frequent mining method is used to detect website browsing behavior from proxy log transactions file. The interesting patterns are mined out which exhibit the browsing behaviour and used to provide appropriate prediction. Web usage mining is a subfield of data mining that uses various data mining techniques to produce association rules. Data mining techniques are used to generate association rules from transaction data. Most of the time transactions are bool transactions, whereas Web usage data consists of quantitative values. To handle these real-world quantitative data, we used fuzzy data mining algorithm for extraction of association rules from quantitative Web log file. To generate fuzzy association rules first we designed a membership function. This membership function was used to transform numerical values into fuzzy terms. Experiments were carried out on different minimum support and minimum confidence. The experimental outcomes indicate that a shared pattern observer exists between the training and testing datasets, with different minimum support and minimum confidence levels.
Automated Attendance Management System: Leveraging Computer Vision for Efficient Tracking and Monitoring

Face recognition is a bio-metric technology that has been widely adopted for security, authentication, and identification. In recent years, face recognition systems have also been used for attendance management in educational institutions and offices. This paper presents a face recognition-based attendance management system that is designed to replace the time-consuming and error-prone manual attendance process. The system comprises four key phases: database creation, face detection, face recognition, and attendance updating. The initial phase involves the creation of a comprehensive database of images of the students in the class. Subsequently, the system employs the HAAR-Cascade classifier for face detection and the Local Binary Pattern Histogram algorithm for face recognition. The system accurately detects and recognizes faces in real time by analyzing the live video stream from the classroom. Finally, at the conclusion of each session, the attendance records are compiled and stored in a CSV file. The presented system has been validated on a dataset of 100 students. The results show that the system can achieve an accuracy of over 90%. The system is also able to handle variations in lighting and facial expressions. The implementation of this face recognition-based attendance management system can enhance operational efficiency, reduce administrative burdens, and ensure more accurate attendance tracking.
Augmented Reality for Equipment and Troubleshooting Application

Augmented Reality (AR) is a game-changing tool in education, effortlessly merging digital information into the actual world and altering the learning experience. AR increases student engagement by superimposing virtual content on real-world environments, making learning more interactive and engaging. It has the potential to significantly increase retention rates and overall learning results. However, implementing AR in education is not without difficulties. Complex subjects necessitate a thorough grasp in order to effectively use AR as a learning aid. Traditional teaching methods frequently fail to captivate today’s technologically aware students. Education may become more dynamic and successful by incorporating technology, notably augmented reality (AR), into the learning process. Furthermore, the COVID-19 pandemic has underlined the importance of adaptable learning methods. AR technology creates an immersive and engaging learning environment, bridging the gap between real and virtual classrooms. It allows students to access instructional content remotely, providing continuous learning and mitigating the effects of disrupted traditional learning environments. A study project was launched to investigate the full potential of augmented reality in teaching. The project’s goal was to design, develop, and test an application called Augmented Reality for Equipment and Troubleshooting. The project generated AR experiences with Unity and Vuforia software. Student testing was carried out by distributing the application and collecting useful input, which allowed for modification and optimisation. The data analysis showed a strong positive impact, with 80% of participating students having a favourable user experience with the programme, indicating the study project’s potential to successfully incorporate AR technology in education. It may develop an immersive, dynamic, and flexible learning environment that suits the needs of modern learners by exploiting AR’s capabilities, thereby revolutionising the educational landscape and empowering students to attain academic achievement in preparation for future difficulties.