Reviewerly/exHarmony huggingface dataset:
Quantum Man-in-the-Middle Attacks on QKD Protocols: Proposal of a Novel Attack Strategy

Quantum Key Distribution (QKD) protocols theo- retically propose a high-security level based on the advantages of quantum computing. This paper analyze the security level of one of the most well-known QKD protocols, BB84, against the man-in-the-middle attack strategy. Specifically, we leverage quantum entanglement to enhance communication security by the injection of a malicious photon in the middle of transmission. In the security analysis, we measure the variation in mutual information between the sender-receiver and sender-attacker to assess the protocol's resilience against potential attacks.
Analyzing Breast Cancer Detection: A Scopus-driven Study on the Role of Machine Learning and Deep Learning

The common and occasionally fatal condition known as breast cancer affects millions of people worldwide. Early detection and precise diagnosis are essential for enhancing patient health, and their importance cannot be overstated. Machine learning (ML) and deep learning (DL) techniques obligate recently made substantial progress in the identification and diagnosis of breast cancer. This paper gives a thorough review of how ML and DL are used to detect breast cancer. The evaluation of yearly publication trends, citation analysis, prominent sources, and keyword distributions is done using data gathered from Scopus from 2008 through 2023 in order to give future academics insight into this field's research tendencies. The technique emphasizes the dynamic character of research by showing that more recent papers have shorter epochs of influence.
Cloud Cryptography: A Cutting-Edge Method to Protect Your Cloud

Cloud computing has revolutionized the way data is stored, processed, and accessed. However, it also introduces new security challenges, making the protection of sensitive data a top priority for cloud service providers and users. Cryptographic techniques play a crucial role in ensuring data security in cloud computing environments. This abstract presents a study that investigates various cryptographic techniques employed to safeguard data in the cloud. This research aims to analyze different cryptographic techniques used in cloud computing, focusing on their effectiveness, performance, and suitability for different cloud deployment models. The study encompasses both symmetric and asymmetric encryption algorithms. The comparative analysis includes popular symmetric key algorithms such as Advanced Encryption Standard (AES), Data Encryption Standard (DES), Triple DES, Twofish and Blowfish, as well as widely adopted asymmetric key algorithms such as RSA.
Deep Learning Techniques for Sentiment Analysis on Social Media Text

Social media enables individuals to express their opinions on a range of contacts with the outside world and topics that interest them. Many people share their thoughts or opinions through text, images, audio, and video. As a result, networking platforms cause a significant volume of unstructured data to be produced on the Internet. Sentiment analysis is a tool that can be used to quickly evaluate data in order to understand human psychology. When compared to current feature-based techniques, deep learning models for sentiment analysis offer detailed representation capabilities and improved performance. The automatic process of sentiment analysis determines the author's attitude and determines whether it is neutral, positive, or negative. Analysis of these emotions without consideration for voice and facial expressions is crucial and necessitates a supervisory technique for accurate emotion interpretation. Despite these difficulties, it is important to recognize human emotions as they increasingly communicate via social media platforms like Facebook, Twitter, etc. This article examines the difficulties researchers have had studying sentiment analysis in social media as well as other potential issues, and it introduces methods that have been tested through a series of experiments using real datasets and show significant advantages over other approaches currently in use for classifying text in social media.
A Study of CVSS v4.0: A CVE Scoring System

In the cyber world, almost all of the attacks that occur are due to the exploitation of some vulnerability of the system. Vulnerabilities have caused major harm to the growth and reputation of the organisations. Currently, MITRE assigns a unique number to each new vulnerability identified which is made available to the public via open-access databases. Further, each CVE is assigned a score known as the CVSS score based on its severity. CVSS is a commonly accepted industrial standard for scoring vulnerabilities published as CVEs. In this paper, an attempt is made to summarize the work done on CVSS. It is noticed that the researchers have proposed improvements to the CVSS scoring method and have analysed CVSS metrics. With fine-tuning, it can give better results in the prioritization process of faults and can be used as a severity measure in other systems as well.
Face and Object Detection Algorithms for People Counting Applications

Thisresearch work delves into the significance of people counting systems, emphasizing their role in furnishing valuable data for operational improvement, security enhancement, and resource optimization in various businesses and organizations. The focus is on a thorough examination of face detection and object detection methodologies rooted in computer vision and deep learning. Specifically, the study scrutinizes Multi-task Cascaded Convolutional Networks (MTCNN) and YuNet for face detection, along with the Histogram of Oriented Gradients (HOG) feature descriptor coupled with Support Vector Machine (SVM) and the real-time capabilities of You Only Look Once version 8 (YOLOv8) for object detection. Through empirical evaluation across diverse conditions and comparative analysis, this research aims to elucidate the strengths and limitations of these algorithms in the context of people counting tasks. The findings provide valuable insights to guide the selection of suitable approaches for specific use cases, contributing to the ongoing progress in the field of computer vision.
Phishing and Web Hacking: Examining the Vulnerabilities and Defenses in the Digital Age

The interaction between phishing, web attacks, and vulnerability assessment in the context of cyber security is examined in this research article. The study examines the methods employed in phishing assaults and diverse web attacks through an analysis of numerous case studies, empirical investigations, and surveys. The significance of vulnerability assessment in identifying and reducing these dangers is also emphasized. The paper highlights the effectiveness of machine learning and deep learning techniques in tackling different types of web attacks, showcasing the potential of these methods for improving cybersecurity defenses. The paper concludes with implications for future research and developments in the field, including the necessity of better detection and prevention mechanisms, education and training for cyber hygiene, and the possibility of using artificial intelligence and machine learning to improve vulnerability assessment and threat detection.
A Small Comparative Study of Machine Learning Algorithms in the Detection of Fake Reviews of Amazon Products

Nowadays the popularity of online shopping has increased, leading to a significant rise in the number of product reviews on platforms like Amazon. However, this increase has also attracted a growing number of fake reviews, which are designed to deceive consumers and manipulate product ratings. Detecting and filtering out these fake reviews is crucial for maintaining the integrity and reliability of online review systems. We proposed a methodology to identify fake reviews on Amazon using sentiment analysis, support vector machine algorithm and logistic regression algorithm.The objective is to show how well the suggested approaches work in separating fraudulent reviews from real Amazon reviews.
Two-way Speech to Sign Language Converter application using Python, OpenCV and NLP

Language is one of the cornerstones of modern society. It has a very important role as it is responsible for conveying and sharing information with others. The people who are challenged with hearing and speaking disabilities are unable to communicate well with others. To aid them in this scenario, we have developed a program which can translate Sign Language to speech and vice versa. The models for the program were created with the help of public ISL datasets containing sign dictionaries. Speech to sign language modules used speech to text and NLP principles to generate signs for the provided sentence. Random Forest classifier was used for sign recognition.
Sentimental Technique Implementation on Textual Data

The increasing amount of real-time or near-real-time data in the big data age provides organizations with opportunities to make data-driven decisions. However, social media data, such as that generated by Twitter, is typically unstructured and difficult to manage. This paper proposes an efficient text data pre-processing approach and develops an algorithm for training decision tree, random forest, SVM, and logistic regression classifiers on a dataset. The algorithm weights the sentiment score based on the hashtag and cleaned text weights, and the most efficient data pre-processing strategy is identified using a novel technology called the hash vectorizer. The effectiveness of the algorithm is tested against manually sorted sentiments and sentiments created before text data pre-processing, and the resultsshow its accuracy is comparable to human-annotated feelings. This research contributes to the existing body of knowledge by providing a precise method for cleaning social media data and a new approach to sentiment analysis.
An Innovative Method of Temperament Identification Using Text Messages and Social Media History with Gated Recurrent Unit Algorithm in comparison with Support Vector Machine Algorithm for Improved Accuracy

The research aims to boost the accuracy of Innovative Temperament identification using social media and text messages. The proposed GRU algorithm was compared with SVM algorithm, a widely used algorithm in text classification. Materials and Methods: In order to anticipate an individual's nature by assessing their thoughts and intentions at an increased rate, the Gated Recurrent Unit algorithm and the Support Vector Machine (SVM) with a total of 20 iterations (N equals to 10 each) are utilized for the sample size of 3062. GRU and SVM are utilized for temperament analysis, with different training and testing splits. Kaggle website is used for retrieving the datasets from the csv file repositories which serves as the origin of the data used in the study. The statistical calculations are made with a G-power of 0.80 and alpha quality, beta qualities are 0.05, 0.2 respectively with the confidence interval consisting of 95%. Result: The results indicate that the GRU algorithm with 93.40% accuracy and 6.59% loss outperformed the SVM algorithm with accuracy 88.59% and 11.40% loss. The independent sample t- Test analysis with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p} &lt; 0.05$</tex> was employed to analyze the significance value and retrieved a value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}=0.052$</tex> . i.e. there is no significant difference between the two groups. This research contributes to the development of methods for the Innovative Temperament identification using social media and text messages. Conclusion: The comparison of GRU is performed with the SVM algorithm and observed that the accuracy value of the Gated Recurrent Unit is 93.40% when compared to the accuracy value of the Support Vector Machine with 88.59%.
Enhanced Metaheuristic Based Hyper-Parameters Tuning for Learning Models

Hyperparameter tuning is a crucial step in the process of building accurate machine learning models. Finding the optimal combination of hyperparameters can be challenging, especially in complex models with large hyperparameter spaces. Genetic algorithms (GAs) have become a popular approach to address this challenge by efficiently exploring the hyperparameter space and selecting the best combination. In the paper, different machine learning models are used along with the genetic search algorithms for the prediction of multi-diseases. The purpose of using the genetic algorithm is to optimize the learning models hyper-parameters. Based upon the evaluations we come to know which models performed well after hyper-parameter optimization using meta heuristic optimization.
Blockchain Application in Healthcare Domain: Industry 5.0 Perspective

Blockchain is an advanced digital technology that has been increasingly used during the past decade to store sensitive information in all small and large businesses that can benefit from incorporating blockchain to increase confidentiality, integrity, security, privacy, and data openness. Modern manufacturing techniques that enable manufacturers to hit their target more quickly are combined to form Industry 5.0. Various manufacturing verticals like smart healthcare, manufacturing plant, smart education, cloud/fog/Edge computing, and supply chain Management (SCM) ecosystems are anticipated to benefit from Industry 5.0. However, trustworthy safe data transfer is necessary to synergistically balance and secure the industrial perimeter since data is shared between extended networks and heterogeneous networks in many authorized sectors. Due to its intrinsic ability to provide immutability, transparency, chronology, and traceability of data, and trust in industrial systems, blockchain (BC) is a preferred choice as security and privacy are key concerns for Industry 5.0 ecosystems. In this article, keeping in mind the need of existing industry, a literature review of some limited different types of security parameters has been done and the journey of the industry has been shown with the healthcare, and blockchain revolution and also discusses the various verticals of Industry 5.0. A comparative study of the blockchain revolution has been done in this article and explains the blockchain's role as a better security facilitator in Industry 5.0 is presented in this article.
GeoCamp Online Reviewing Application Using NodeJS and MongoDB

Today's consumers frequently use online search to reduce the perceived risk involved with relying on unfamiliar businesses and services. Consumers may turn to online reviews in particular (i.e., reviews written by other customers of a product), and many are likely to believe online reviews more than suggestions from friends and family.
Synthetic Pathology Generation with Near-Pair Cyclic GANs for Object Detectors

In machine learning for medical imaging, object detectors that localize pathology require a large volume of labeled images. This type of data is often expensive and time-consuming to create. To avoid this challenge, we present a method to support distant supervision of object detectors using synthetic pathology-present labeled images. This method seeks to generate diverse focal pathology and insert this pathology into healthy images. This method is novel in its use of pathology-present regions and similar pathology-absent regions of the same image to serve as "near-pair" cases for the training of a cyclic generative adversarial network (cycleGAN). After training, pathology-absent regions of the image serve as inputs to the trained conditional generator to create synthetic pathology-present regions with exact knowledge of location (labels) in the image. We train and test the method with 2800 fracture-present image patches from 1109 unique pediatric chest radiographs. The trained generator was then used to create fracture-present patches, that were reinserted into radiographs using Poisson inpainting. Visual inspection of the generated patches revealed realistic fracture patterns. In a blinded observer study, three pediatric radiologists were presented with 50 side-by-side images of a real and synthetic radiograph and asked to identify the real case. Radiologists achieved an accuracy of 67±17% for the selection of the real vs. synthetic case, where 50% accuracy would reflect equivocal distinction between the two. In 70% of cases, at least one radiologist did not correctly identify the real case, and radiologists unanimously agreed in only 33% of cases. These results suggest that the proposed method creates visually realistic pathology. Our methodology is potentially generalizable to other imaging applications with focal pathology such as tumor-present PET imaging.
Segmentation Based on 1-Shot Learning

Low-risk learning approaches for classifying images encourage learning via little data. In order to facilitate packed semantics segmentation of pictures, we expand those methods. We particular instruct a neural network which generates factors for the completely Convolutional The network (FCN) from just a few sets of analyzed with annotations. From an experiment picture to known as newly introduced semantics the classroom, we execute packed sensor-level prediction using that FCN. Our system is a minimum of three times quicker and exhibits an approximate mean IoU enhancement of 25% over the top-performing baseline techniques for person-shot division on courses that have not yet been observed within the PASCAL volatile organic compounds 2012 information set.
Enhancing Sentiment Analysis In Text Of Social Media Texts Using Hybrid Deep Learning Model And Natural Language Processing

Sentiment analysis (SA) is a mechanized strategy for finding and understanding the feelings depicted in text. Over the most recent decade, SA has altogether expanded in ubiquity in the Natural Language Processing (NLP) people group. One emotion that has a negative effect on people's daily life is depression. Every year, more people around the world report having long-lasting feelings. Finding persons with depression as early as possible is one of the most difficult difficulties. Researchers are analyzing text content posted on social media using Natural Language Processing (NLP) techniques, which helps to develop methods for Depression detection. This study examines various earlier investigations that employed learning strategies to recognize depression. The current approaches have issues with better model representation that make it difficult to accurately identify depression from literature. Fast message in the ongoing undertaking to address an answer for these issues, Fast text Convolution Neural Network with Long Momentary Memory (FCL), an original hybrid deep learning brain network plan with improved message portrayals, is made. Real-world datasets that were used in the literature were used to implement the current study. The suggested method achieves higher accuracy in detecting depression than the state-of-the-art.
Computer-Aided Diagnosis for Breast Cancer Classification Using Deep Learning

In today's world, breast Cancer is one of the most deadly diseases among women, although early detection dramatically improves survival rate. CNN Models are the category of DL architecture that was developed to improve accuracy in breast cancer categorization. Compared to traditional methods, CNN has demonstrated better classification efficiency and tumor detection in medical imaging. Based on deep CNN architectures This research provides a new breast cancer classification system. This research provides a new breast cancer classification system based on deep CNN architectures. To develop a technique for early and accurate breast cancer detection in order to avoid wasteful therapy (error 1) due to False Positive and late treatment (error 2) owing to False Negative is the main aim of this research methodology. The primary goal of this work is to prevent error 2, for which we employ Resnet-152.
Getting comfortable with CIE charts for color grading

A common task in color grading in post is to grade to one gamut while constraining to another. Example, grade to ITU-R BT.2020 but constrain to DCI-P3. If colorists can quantitatively determine the excursions of colors from the gamut of interest, they can then either remap the colors or allow them to clip at the gamut boundary. Currently, this work is handled using a combination of legacy tools and monitoring with reference monitors. This can be time consuming and prone to errors. — The CIE chromaticity chart provides a 2D view of chromaticity content of the image. Traditionally, the CIE chart has been a mystery relegated to academic textbooks and used by those with deep knowledge of color science. This paper presents innovations based on deriving useful data from the CIE chart that helps colorists quickly determine how far off colors are from the gamut of interest. — The Gamut Excursion Measurement (GEM) effectively unrolls the CIE chart and presents a quantitative snapshot of the gamut excursions outside a gamut of interest as a 2D linear chart over the spectrum of colors, thus helping to quickly identify regions of interest to focus on. The excursions can then be visualized (using the GEM data) with a multi-level false color heat map. Luminance Qualified CIE is a method of constraining the CIE chart against luminance, allowing users to study colors within specific luminance ranges of interest. Combinations of the above tools provide fast, effective techniques for color grading.
Research on the evaluation of English teaching quality based on grey relational analysis

Online English teaching app is a platform that can make students more flexible and effective in mastering English skills. At the same time, teachers can be more targeted and motivated in the classroom, making the guidance more targeted. This paper sorts out the basic theory of teaching quality evaluation and introduces the advantages and disadvantages of the more commonly used evaluation methods such as fuzzy comprehensive evaluation, AHP and BP neural network evaluation. In this paper, the relevant information of three random teachers in the English online teaching platform was obtained by means of a questionnaire survey, and then the teaching quality of the three teachers on the English Teaching Platform (EOTP) was simulated and analyzed in MATLAB, and the simulation results were compared with the teaching quality of the three teachers. The results of fuzzy comprehensive evaluation method and BP neural network evaluation method are compared and analyzed. The analysis results show that the teaching evaluation model based on grey relational analysis has certain rationality and feasibility.
Research and exploration of digital watermarking technology based on BP neural network

Digital watermarking is a technique to embed specific digital signals into digital products to protect copyright integrity, which is an effective way to protect information security. Neural network technology is a mathematical model generation technology that mimics the structural function of biological neural networks and has good self-learning and merit-seeking capabilities. It is feasible to add neural network to digital watermarking technology for copyright protection research. In this paper, we select and improve a generative adversarial network from a classical neural network to generate adversarial samples required for subsequent research using watermarked datasets, where the improvements include combining high-level loss and low-level feature loss and using adversarial labels to facilitate adversarial classification. The improved generated adversarial samples have the advantage of less alteration to the original image. In this paper, new digital mosaic techniques combined with neural networks are explored in depth rain for new big data directions, which are informative for new areas of research.