Reviewerly/exHarmony huggingface dataset:
Survey of Auto Recon SQL Mapping

The proposed system combines SQLmap, Burp Suite, Nikto, Nmap, and Hydra into a single automation tool for web application security testing. It automates the entire testing process, from reconnaissance to vulnerability exploitation and reporting. The tool detects SQL injection, XSS, directory traversal, and other common web-based attacks. It has a user-friendly interface, customizable scan parameters, interactive dashboards, and collaboration features. It supports popular web frameworks like Django and Flask, multiple SQL databases, and cloud-based scanning. Machine learning algorithms enhance vulnerability detection and classification, improving accuracy and comprehensiveness. The system aims to improve efficiency and accessibility in web application security testing, providing a comprehensive tool for automated vulnerability detection and Reporting.
Comparison of Regression Techniques and Integration Models Using Machine Learning

Regression problems help extract the law from massive amounts of data to predict. Hence, regression algorithms have been a hot topic in machine learning research in recent years. In actual problems related to regression have complex internal and external factors, and different machine learning algorithms affect scalability and predictive performance. This research uses BP neural network, extreme learning machine, and support vector machine to examine the applicability of machine learning methods in regression problems. Comparing the single model and integrated model regression results of each machine learning algorithm shows its pros and cons. Finally, simulated experiments using four data sets show how well each machine learning method predicts regression. Research on machine learning algorithms and integration models appears to be viable and logical.
Enhancing Image Quality through Deep Learning-based Super-Resolution Techniques

Significant advancements in super-resolution have been accomplished over the past two decades, particularly using deep learning techniques. Image super-resolution is a key image processing technique for improving an image's resolution in the field of computer vision. In addition to providing a thorough overview of recent developments in single-image super-resolution from the viewpoint of deep learning, this article aims to educate readers about the original conventional approaches for image super-resolution. In this article, we examine significant deep learning-based SR techniques and divide them into two groups based on the contributions they make to the definition of key optimisation targets for deep SISR and the assessment of relevant neural network topologies for SISR. Finally, we wrap up our study with some present issues and potential developments in deep learning-based SISR.
Demand Prediction Of Private Cars With Parallel Multi-Task Learning Model Using Ridge Linear Classification In Comparison With K-Nearest Neighbour Algorithm For Better Accuracy

The research work intends to enhance the precision of forecasting private car demand by applying Ridge Linear classifier algorithm and comparing its accuracy with K-Nearest Neighbour (KNN) algorithm. Materials and methods: The UCI repository database were used to download the experimental data. The total number of iterations for each group is 10 iterations with total iterations of 20 calculated by performing an independent sample t-test with a Gpower of 0.8 along with alpha quality and beta qualities of 0.05 and 0.2 respectively also the Confidence Interval (CI) of 95%. The K-Nearest Neighbors method (KNN) and the Ridge Linear classifier models were used to predict the demand for private automobiles. Results: As compared to the Ridge Linear classifier model, which has more accuracy rate of 71.36 percent, when compared to the K-Nearest Neighbours method (KNN) classifier has a 70.90 percent. The significance value was analyzed using the independent sample t- Test analysis where p<0.05 and this work showed a significance value of p=0.19. There is no significant difference between the two groups. Conclusion: The accuracy of Ridge Linear Model is 71.36% is higher compared with K-Nearest Neighbors algorithm that has an accuracy rate of 70.90% in analysis of to predict the demand of private Cars with improved accuracy rate.
Automated Diagnosis and Detection of Blood Cancer Using Deep Learning-Based Approaches: A Recent Study and Challenges

Blood cancer, also referred to as haematological malignancy, is a collection of cancers that affect the blood, bone marrow, and lymphatic systems. Early and accurate blood cancer detection is essential for effective treatment and enhanced patient outcomes. Deep learning algorithms have emerged as potent instruments for medical image analysis and disease detection in recent years. This paper intends to provide a comprehensive overview of the application of deep learning techniques to the detection of blood cancer. The paper begins by describing the various forms of blood cancer and the difficulties involved in their detection. The merits and weaknesses of various deep learning architectures and frameworks used in blood cancer research are described. The review then focuses on the datasets and imaging modalities that are commonly used for blood cancer detection. It discusses the pre-processing techniques used to improve image quality and reduce noise, as well as the data augmentation techniques used to increase the robustness of deep learning models in the proposed system architecture. The paper concludes with a discussion of prospective future directions and developments in the field. It highlights the significance of developing robust and interpretable deep learning models, incorporating multi-modal data for enhanced accuracy, and integrating clinical and genomic data to facilitate personalised treatment strategies.
Hand Sign &amp; Gesture Recognition System

Hand gestures serve as a form of non-verbal communication utilized in sign language, primarily employed by individuals who are deaf or have hearing impairments to communicate with those who do not share the same condition. While numerous sign language generators exist, none of them offer customization options or cost-effectiveness for end users. This proposed work outlines a prototype system named the Hand Sign and Gesture Recognition System Software. The system aims to automatically interpret sign language, facilitating smoother communication between individuals who are hearing and those who are deaf. Particularly, the system can be implemented on popular video-sharing platforms like YouTube, which currently lack an auto-text creation feature for gestures and sign language. The system utilizes a real-time hand gesture recognition mechanism that employs shape-based features such as orientation, centroid of the center of mass, finger state, and thumb position relative to extended or folded fingers to identify gestures. A convolutional neural network (CNN) supervises the entire feature extraction process. Each frame of the video is captured, allowing the identification and isolation of hands for processing by the CNN. The system achieved a remarkable test accuracy of 99.89% by employing CNNs for both training and testing. An advantage of this approach is its independence from external hardware or devices, as it operates solely through the software implementation.
Comparative Analysis of Recurrent Neural Network over Convolutional Neural Network in Predicting R2l Attack in IoT Devices with Improved Accuracy and F1 Score

The main target of this study is to more accurately and efficiently predict the R2L attack on IoT devices using Novel Recurrent Neural Network than Convolutional Neural Network. Materials and Methods: The number of IoT devices that can interact with one another has increased along with the capacity of the internet. This change makes the traditional methods and antiquated data processing techniques for identifying attacks, which are useless. It is challenging to spot attacks on IoT devices and recognise fraudulent activity in its early stages due to the growth in network traffic volume. The study utilized GPower software to calculate sample size requirements. The total number of iterations required for the study was determined using the clincalc tool, which indicated a value of N=20 (10 iterations for each group). For a sample size of 5479 and (ɑ=0.05; power=0.85), each group performed 10 iterations, resulting in a total of 20 iterations for both groups combined. Result: RNN (90.3140%) recognises objects and improves the accuracy over CNN (89.9210%) with significance value P=0.757 (p>0.05), which is statistically insignificant. The F1 score of RNN and CNN are 0.90 and 0.89 respectively. Conclusion: When compared to Convolutional Neural Network (CNN), Novel Recurrent Neural Network (RNN) has more accuracy.
A Comparative Analysis of Deepfake Detection Techniques: A Review

Every person in this new generation has access to all of humanity's wisdom. Many technological possibilities are available. Nonetheless, we misuse this benefit by using deepfake for face swap. The deep fake method of machine learning, which is popular on social media, superimposes one person's face over another person's face. Deep learning, which has allowed researchers to produce deepfake images and videos considerably more rapidly and affordably, is the main ingredient in deepfakes. Even though the word “deepfakes” has a terrible image, more people are using the technology privately and practically. Although it is still fairly new, recent technological advancements have made it more difficult to tell the difference between deep fakes and synthetic images. Deepfake technology is evolving, and there is rising unease. The analysis of deep fakes has already been done by a number of researchers utilizing techniques like GAN, Convolution Neural Network with or without LSTM, Support Vector Machine, and Machine Learning. This paper analyzes the many techniques that have been used by researchers to identify Deepfake videos and rates the performance of multiple Deepfake video detection systems. According to our analysis, integrating CNN and the LSTM together produces superior outcomes and accuracy, which could also be improved by applying the concept of image enhancement.
Comparison of Random Forest with K-Nearest Neighbors to Detect Fake News with Improved Accuracy

Aim: To develop an automated, reliable and effective system that detects fake news articles, with the goal of reducing the spread of misinformation and promoting the dissemination of accurate and trustworthy information because the rapid spread of fake innovative news has dturn out to be a foremost concern in modern years. Materials and Methods: The effectiveness of two methods Random Forest and K Nearest Neighbor (KNN) are compared in predicting fake news. The evaluation was carried out using a Github dataset of 2000 newscast informations labeled as either counterfeit or unaffected, with performance metrics such as accuracy used to compare the two algorithms. The model size of the group is 10. Results and Discussions: The result shows that KNN outperformed Random Forest (RF) in terms of all the performance metrics, suggesting that KNN is a more effective method for detecting fake news. The significance value for this study is p=0.001 which is less than 0.05. Hence, there is a statistically significant difference between the two groups. Conclusion: The results suggest that KNN with 81.20% accuracy is a more effective algorithm for fake news detection. This study provides valuable insights into the effectiveness of Machine Learning (ML) algorithms in detecting fake-news.
Predicting the Accuracy of Fractionation of Patron's Activities in Online Social Networks Using Novel K-Means Clustering Algorithm Comparing with Agglomerative Hierarchical Clustering Algorithm

Aim: This research compares a novel K-means clustering algorithm with an agglomerative hierarchical clustering algorithm for its ability to predict user behaviour in online social networks with a high degree of accuracy. Materials and methods: The accuracy percentage was predicted using a 95% confidence interval, a G power (value = 0.8), and an evaluation of the Novel K-Means clustering method with a sample size =10 and the Agglomerative hierarchical clustering algorithm with a sample size =10. Results: The findings shows that the Novel K-Means clustering method Agglomerative hierarchical clustering algorithm (78.81%) in terms of accuracy. Two Algorithms were found to be statistically significant, with a p value of 0.001 (p < 0.05). Conclusion: It was demonstrated that the Agglomerative hierarchical clustering approach performed inferior to the Novel K-Means clustering strategy.
Prediction of the Variation in Price of Bitcoin Using Machine Learning

This paper presents an approach for predicting the price of Bitcoin using machine learning techniques. We used historical data of Bitcoin prices and extracted relevant features such as trading volume, social media sentiment, and market capitalization to train and test the models. Numerous machine learning algorithms, such as random forest, gradient boosting, and neural networks, were tested by us and evaluated their performance using metrics such as mean squared error and accuracy. Our results show that machine learning models can effectively predict the price of Bitcoin with a reasonable degree of accuracy. We also discuss the limitations of our approach and suggest future research directions to improve the performance of Bitcoin price prediction models. Our findings suggest that machine learning can be a useful tool for investors and traders in making informed decisions about Bitcoin investments.
Design and Implementation of Block Chain with Cybersecurity Scheme for Fog Based Internet of Things

This study proposes an IoT gadget information trade system that is haze based and utilizes a block chain approach. A typical worldview for distributed computing is haze calculation, which spots handling and storage spaces between the customers and the cloud climate. Here, a solid method is utilized by the IoT gadgets to trade information. The Internet of Things (IoT) gadgets and frameworks, for instance in a haze climate, are one of the significant information sources. The multifaceted design and interconnectedness of such IoT and haze conditions, nonetheless, can prompt security shortcomings (for example, because of execution missteps or flaws in the hidden gadgets or frameworks), which can be utilized to sabotage the legitimacy of the information. Because of the trustless idea of block chain security, its utilization in a large number of businesses is extending rapidly. Thus, we give a protected Block chain-based conspire in this paper to guarantee the veracity of hubs and information and the security of information transmission in the mist climate.
Text Emotion Detection Using Machine Learning Algorithms

The ability to identify emotions in text is essential for a range of applications, including sentiment analysis, customer feedback analysis, and monitoring mental health. In recent years, machine learning algorithms have become more widely utilized to automatically classify text into distinct emotion categories. Support vector classifier, linear support vector classifier, decision tree classifier, and random forest classification machine learning approaches are used for text emotion recognition model that is provided in this study. The proposed model demonstrates the effectiveness of machine learning in classifying text emotions and has the potential to be applied in a variety of real-world scenarios.
NUTRIENT`S POCKET GAMES THE EFFECTIVENESS OF INTERACTIVE MEDIA IN UNDERSTANDING CHILD NUTRITION IN KEDUNGPEDARINGAN II KEPANJEN ELEMENTARY SCHOOL STUDENTS

Child nutrition is important for children's physical and cognitive development. However,&#x0D; many children do not have a good understanding of child nutrition. Interactive media has the potential&#x0D; to be an effective tool to teach children about child nutrition. This study aims to determine the&#x0D; effectiveness of interactive media (Game Saku Gizi) in improving students' understanding of child&#x0D; nutrition. This study used a pre-experimental research design with a one-group pretest-posttest design.&#x0D; The participants of this study were a group of grade 1 to grade 5 elementary school students at SDN&#x0D; Kedungpedaringan II Kepanjen, Indonesia. The results showed that there was a significant difference&#x0D; in students' understanding of child nutrition between the pretest and posttest. This means that the useof interactive media (Game Saku Gizi) can improve students' understanding of child nutrition. Thefindings of this study have important implications for the use of interactive media in education.Interactive media can be used to develop educational games and simulations that can help students to learn about various topics, including child nutrition. However, it is important to note that interactivemedia is not a magic bullet. It is important to use interactive media along with other teaching methods,such as lectures and discussions. In addition, it is also important to choose interactive media that isappropriate for the age and learning level of the students. Based on the results of the study, it can beconcluded that the Nutritional Pocket Game can improve the nutritional knowledge of students of&#x0D;
A vector compression coding algorithm based on SIMD(VCABSIMD)

In the vector operation system, each data operation will only increase or decrease the same number for each component of the vector, that is to say, the vector is regarded as a data object on the whole, and each operation is carried out on the whole. For example, translation, scaling and other operations in computer graphics. Such computing systems are currently widely used in graphics, machine learning, mathematical modeling and other fields. When the data in the vector computing system is homomorphic encrypted and uploaded to the cloud server, the problem of low vector coding efficiency and polynomial coefficient expansion caused by a large number of polynomial operations is generated. The existing SIMD coding algorithm encodes an n-dimensional vector integer into a polynomial. The number of calculations increases exponentially with the increase of n, and the coding efficiency of the vector is very low. For typical FHE schemes such as BGV, BFV and CKKS, the main performance bottleneck comes from a large number of polynomial algorithms. Specifically, encrypted data is typically composed of a pair of polynomials with coefficients of hundreds or thousands of bits, requiring expensive multiword arithmetic. In addition, large polynomial lengths increase the computational complexity. Therefore, in this paper, a vector compression coding algorithm based on SIMD(VCABSIMD) is proposed to solve the performance problem when the data in the vector computing system is homomorphically encrypted and uploaded to the cloud server for calculation. The vector coding efficiency and polynomial calculation are deeply optimized.
Life Cycle Investigation of Educational Systems in the Context of Civilizational Development of the Planetary World

The article analyzes the dependence of the phenomenon of education on the type of civilization in space of which the life of the world community takes place. It is based on the interaction of social institutions of the market, science and education. It “proceeds” on the surface by the division of social labor, which generates a specific form of social system of education in dependence on the social division of labor in specific socio-economic conditions. To reproduce effectively its structural and functional parameters, the author suggests resorting to the paradigm of the «life cycle of educational systems in the context of a civilizational approach» based on the use of cognitive analysis. Its advantage is only that the concept «grasps» the phenomenon of education as a whole. This means that the phenomenon of civilization education is studied as a social institution and social system of the industrial society. To analyze the dynamics of knowledge change, two methodological conditions must be used: to apply the civilizational approach and the paradigm of the life cycle of social systems, since this phenomenon has a long-lasting dimension in space and time; the meaning of educational content at each stage of industrial civilization life is distinguished: natural knowledge prevailed at the stage of its origin; at the stage of growth - technical knowledge that corresponded to the essence of civilization itself; at the stage of maturity - scientific knowledge turned into ideologues of social development, which led to the emergence of technocratism and “drove” the planetary community into a dead end; at the stage of decline - technological knowledge led to the loss of mainstream guide of social development and began the catastrophic destruction of the social organism of the planet, which entails a change in the type of civilizational development; the solution is seen in the rise of the planetary community into the space of informational civilization, the transition of education to mastering methodological knowledge corresponds to it.
Exploring Workflows for Real-Time HDR-SDR Conversion

Due to the growing demand for high quality imaging in the movie industry as well as in broadcasting environments, research on high dynamic range (HDR) technologies is enhanced more and more. The incentive for the great interest in HDR content is particularly based on properties such as natural reproducibility of a scene containing the approximate dynamic range of real-world brightness distribution, a broader color spectrum and a more intense spaital depth in the image. Especially in the broadcast-related scope there is still potential to further develop and improve HDR technology. As not every end user has an HDR-compatible device as of now, methods are needed to map high dynamic range content to standard dynamic range (SDR) including transfer function and color gamut. Considering usually demanded real-time HDR-SDR conversion workflows, conventional mappers often produce an unaesthetic and unnatural look due to color hue shifts, flat brightness distribution or unnatural looking skin tones, which causes loss of the creative intent of images especially but not only in context of intense lighting effects. — On that account, the research team has set themselves the goal of analyzing existing mapping algorithms for a real-time broadcast use case in a show context. To properly test and apply the used algorithms on HDR video footage, a new dataset of HDR show content is produced. The insights of this comparison are depicted and explained.