Reviewerly/exHarmony huggingface dataset:
An Enhanced Data Confidentiality in Online Social Networks Using Quantum Key Management with a Blockchain Approach

The main aim of this paper is to propose malicious detection and prevention by providing the data confidentiality framework using deep learning models of Long Short-Term Memory (LSTM) combined with a Convolutional Neural Network (CNN) for online social networks with a blockchain approach. Basically, this paper started with an introduction to deep learning and blockchain technology, followed by a literature survey on malicious attack detection, prevention, and data confidentiality in online social networks. Further, an analysis of the blockchain-based data confidentiality layers is presented, followed by the proposed malicious prevention framework using deep learning for decentralized online social networks (dOSN). Then, the comparative role of the LSTM-CNN deep learning prevention system with the existing system. The accuracy percentage for earlier sentiment analysis techniques utilizing the same data was just 64.47% compared to the more famous behavior analysis. This study increased accuracy to 96.63%, so the prospects are quite good, also compared and proved the Quantum Key Management (QKM) algorithm is more lightweight than classical cryptography algorithms in execution speed in the field of malicious prevention and protection systems is discussed, followed by the results and discussions, and lastly, the paper is concluded.
Abstractive Multi-Document Summarization: Exploiting Maximal Marginal Relevance and Pretrained Models

The increasing demand for effective multi-document summarization (MDS) due to the rapid expansion of information availability has triggered this investigation. Focused on the fusion of Maximal Marginal Relevance (MMR) with pretrained models, we circumvented the often-cumbersome fine-tuning process, and introduced a novel methodology. We applied this to various text datasets like Multi-News and WCEP, and it resulted in significant improvement of ROUGE scores, illustrating promising results. For example, the ROUGE-1 score of our method on the Multi-News dataset was 46.23, and the ROUGE-1 score on the WCEP dataset was 30.74.In this discussion, the research implications, including better n-gram optimization and model selection strategies, were brought forward. This study had substantial implications for natural language processing, propelling us toward more advanced text summarization applications, presenting potential avenues for future research, such as refining the role of n-grams in summaries and optimizing model selection processes.
Helmet Detection and License Plate Extraction for Two-Wheeler Offenders

Helmet Detection and License Plate Extraction for Two-Wheeler Offenders is a critical issue in many countries, particularly in developing countries where two-wheeler riders make up a significant portion of the population. In the case of an accident, not wearing a helmet while operating a motorbike or scooter might cause serious brain injuries or even death [1]. To address this issue, several technologies and systems have been developed to encourage two-wheeler riders to wear helmets [2]. Using YOLO v8 on Google Colab, this project aims to create a system for identifying two-wheeler helmet offenders and extracting license plate data. The system accurately recognizes and extracts relevant data from images and videos using object-detection algorithms. The system was evaluated for both helmet identification and license plate extraction using a variety of measures, including accuracy, precision, and recall. The system's success in policing traffic regulations and improving road safety demonstrates its future work could focus on expanding the dataset to further improve the system's accuracy and robustness.
Detection of Compromised Accounts using Machine Learning Based Boosting Algorithms- AdaBoost, XGBoost, and CatBoost

In today’s digital era, Online Social Media (OSM) like Twitter, Facebook, and Instagram are used to share information. Everyone is trusted on these social network platforms for exchanging information such as personal, professional, financial, etc. Due to this implicit faith in these social platforms, users broadcast their personal information on them. By taking advantage of the same, unauthorized users use the account of legitimate users for performing malicious activities. They gain valuable information and legitimate accounts are trapped in malicious hands. These types of accounts are known as Compromised Accounts. In order to create a safe and secure social media environment, it is, therefore, essential to identify these compromised accounts. In this paper, for the detection of Compromised Accounts, we have applied machine learning-based three boosting algorithms- AdaBoost, XGBoost, and CatBoost. These algorithms are compared with the state-of-art Compromised Account Detection using the Authorship Verification (CAD-AV) approach over the real-world Twitter dataset. The results have shown that all three algorithms- AdaBoost, XGBoost, and CatBoost have performed better in comparison to CAD-AV. Among them, CatBoost has performed the best (Accuracy: 94%) in the detection of compromised accounts.
Sign Language Recognition Using CNN

Sign Language is widely used by deaf and dumb people for their daily communication. One of the most established and well-known sign languages used worldwide is American Sign Language. It is extremely challenging for someone who does not know sign language to understand sign language and communicate with the hearing-impaired people. To overcome this drawback, we have developed a CNN model. We have trained and tested it for the 26 letters of the alphabet. The model produced training accuracy of 97.48%, testing accuracy of 98.44% and validation accuracy of 96.58%. The model performed very well in predicting the sign gestures with plain background and appropriate light conditions. We converted the model into an efficient artificially intelligent application that helps us to understand the sign language very easily. Initially the application captures the video of the hearing-impaired person doing the sign gestures, after that the video is converted into frames followed by converting it to gray scale image and applying gaussian blur filter. The output image of Gaussian blur filter is fed to CNN model. The model converts the sign gestures into letters, words and subsequently to sentences and displays them in the application. We also implemented a feature to convert the predicted sentences to different languages and we also have voiceover for the predicted text.
Detection And Tracking of Multiple Pedestrians Using Deep Learning

Recent advancements in deep learning and computer vision have benefitted security systems. Recent object tracking algorithms, in example, have incorporated deep learning in a variety of methods to enhance tracking performance. Object tracking and security systems continue to provide a number of difficulties. This work will first identify the several challenges related to the object tracking for an autonomous system and further the object tracker system will be evaluated in a simulated environment for multiple objects like pedestrian, moving or stationary object. This research paper is all about designing the tracking algorithm for multiple pedestrians. This research employed YOLO V3 with a Deep Sort architecture. In order to get heightened accuracy different kind of scenario is used for preparing the test data. As a result, precision is 93 %, Recall is 98% and accuracy is 96%.
A CNN-based Prediction Model for Age, Gender, and Ethnicity Using Facial Images

A computational model can examine multiple critical demographic factors at once, including age, gender, and ethnicity. This is useful when cognitive robots need to execute multiple computer vision tasks at once and GPU and memory resources are limited. Multi-task learning allows a solitary model to proficiently handle multiple tasks simultaneously to do several classification and regression tasks by mastering single low-level representation in an effective method for doing this. An assumed relationship between age, gender, and ethnicity is taken care of using the combined estimation approach. In the current study, gender, age, ethnicity, and mood are predicted from facial photographs in real-time user profiling using a multi-task convolutional neural network. Three different architectures were looked at to see which one struck the optimum balance between accuracy and speed. The multitasking CNNs outperformed equivalent single-task CNNs in accuracy, execution speed, and memory use. This approach is particularly suitable for overcoming the processing resource constraints particular to such applications, and it can be used in real-world situations like embedded systems and smart cameras.
A Novel Approach Towards Photorealistic Image Stylization

Photorealistic image stylization — a technique which involves transferring the artistic style from one image onto another, while simultaneously preserving the inherent content of the target image. Numerous methods for achieving photorealistic image stylization have been developed, however they produce stylizations that are spatially inconsistent with noticeable artifacts and distortion in output images which are not present in captured photographs. We propose novel advancements to address these problems and enhance photorealism to improve the existing methods by incorporating both content and style loss in the optimization process. We propose two key enhancements: activation function enhancement and learning rate optimization. In the activation function enhancement,the conventional ReLU activation function is replaced with the Exponential Linear Unit (ELU) activation function. Furthermore, we conduct a comprehensive learning rate optimization study to customize the learning rate for our method. Both enhancements lead to improved convergence speed and solution quality, resulting in superior photorealistic images. The dataset used in this research paper is the Deep Photo Style Transfer(DPST) dataset which contains a collection of content and style images that can be used to train neural networks to perform photo style transfer and the evaluation of this model was done using several metrics, including perceptual quality, visual similarity, and structural similarity. Through rigorous evaluation and comparison with existing methods, our enhanced approach achieves remarkable and visually appealing stylization results that are more preferred by human subjects. Our novel approach achieves a higher SSIM score (the higher the better) as discussed in Table 1 than other existing methods. It also opens up new possibilities for integrating desired artistic styles into images while preserving their original content.
New Histogram-Based User and Item Profiles for Recommendation Systems

A recommendation system is used as a tool for data filtering that works on the principle of discovering patterns in data on customer behaviour to suggest the most relevant items to a user. Based on the existing trend- content based, collaborative filtering, hybrid model are the most popular techniques that are used for recommendation systems. By using Symbolic Data Analysis (SDA) [9], we extended the approaches of the content-based and collaborative filtering techniques in our work. Standard data analysis has been enhanced by SDA, that takes symbolic tables as input and results in symbolic objects as output. The symbolic data is highly used to aggregate and reduce the loss of data. The similarity scores will be generated for each technique and the NDCG metrics is used to evaluate the performance of these models. Finally, we have observed that SDA is significantly helpful for understanding and modelling of the histogram data in optimizing the performance and generating the recommendations accurately.
Non-Local Bayesian based Denoising of Magnetic Resonance Images

Magnetic Resonance Imaging is a non-invasive and efficient technique to visualize the various parts of the body. One of the major problems facing MRI as with any other medical imaging techniques is the noise and artifacts that affects it. This in turn can affect the accuracy of the diagnosis. Denoising is therefore an important part of medical image analysis. Denoising is a post-processing imaging technique aimed to achieve better diagnostic interpretation of medical images. Non-local Means is an efficient image denoising technique that exploits the self-similarity in images. Non-local Bayesian denoising combines non-local means denoising with Bayesian inference. This paper expands upon this approach for Magnetic Resonance Images. The performance of the method is measured quantitatively by means of Peak Signal-to-Noise Ratio(PSNR). In this patch-based approach, the number of patches taken is varied and the PSNR is calculated for different standard deviation of Gaussian noise. Other parameters like search radius and patch size are also varied to evaluate their effect on the performance. An empirically obtained patch count and search radius has been shown to provide optimum denoising results for Magnetic Resonance Images.
Various Optimizers' Performances for CNN-Based Hand Gesture Recognition for PDP Assistance

Many elderly people suffer from movement-related problems like Parkinson's Disease (PD). Parkinson ’s Disease Patients [PDP] invariably depend on others to fulfil their daily needs. To convey the need of the Parkinson’s Disease Patient to the caretaker, the Parkinson’s Disease Patient Hand Gesture Recognition [PDP-HGR] system using deep learning is proposed here. So nine simple hand gestures to fulfil the daily need of PDP are formulated and an indigenous HG dataset with 16389 images having simple and complex background was prepared. The PDP was initially informed of the hand gesture and its meaning describing to his daily needs. PDP-HGR system recognize the hand gesture performed by the PDP and inform the need of PDP to the caretaker. Using this dataset, a Convolution Neural Network (CNN) based PDP-HGR system to identify the need of PDP is presented. Five gradient descent-based optimizers viz, Stochastic Gradient Descent (SGD), Adaptive Gradient (Adagrad), Adaptive Delta (Adadelta), Root Mean Square Propagation (RMSProp), Adaptive Momentum (Adam) studied on indigenously prepared Hand Gesture dataset. For PDP, it is found from experimental result that RMSprop outperforms amongst the all optimization algorithm for PDP-HGR system.
Virtual Reality in Education: Designing and Implementing a Dedicated Application for e-Learning

This paper outlines both the framework and the steps involved in building a chat room for high school students, utilizing immersive virtual reality (IVR) technologies to bring together professors and students in a VR distance learning experience. We have created three-dimensional items that represent the world we have designed, including platforms, chairs, tables, whiteboards, and other environmental elements. This application utilizes the Unity game engine for IVR scenarios, providing students with a new resource for communication when they are unable to attend school due to challenges that do not affect their learning experience. Designing IVR environments poses various challenges, and when possible, solutions are suggested.
IBit a Novel Representation for Multidimensional Information Universal Database

According to modern physics theories, all of the universe's materials, energy, particles, and dimensions are interconnected information in a multidimensional network that can be expressed as mere numbers and defined under the information theory. The information is organized in a multidimensional structure whose construction unit is suggested in this paper to be ibit which represents an abstract number instead of a classical bit. This number represents the building unit of the information system (universal database) so the selection of the appropriate ibit will help to organize this multidimensional structure and can reduce the number of the universal database dimensions. Hence, using a binary system will result in a very complex system, whereas using a decimal or hexadecimal order reduces the number of dimensions and clarifies the interconnectedness of information. Similarly, using fractional complex rather than integer will lead to additional arrangement and clarification of the multidimensional structure in the same format. This paper will be a new and distinctive first step toward representing arithmetic numbers in an informational format interconnected in the form of information with a systematic and multidimensional structure. This step is critical for activating and generalizing information theory for everything, and the research will propose a distinctive definition of dimensions that is compatible with the theory of information as well as reveal the most significant weaknesses in today's enumeration systems through analysis and suggest the best ways to expand them in the future, thus expanding the horizon of technology and engineering to areas that may be revolutionary.
Comparative Survey of Various Intelligent Methods for Breast Cancer Diagnosis and Prognosis

Cancer remains a major contributor to mortality rates across the globe, causing nearly 10 million fatalities in 2020. Among all cancer types, breast cancer has the highest incidence, around 2.26 million cases reported across the globe by the World Health Organization. In the last few years, breast cancer was diagnosed in approximately 2.3 million people and caused around 685,000 deaths. In this paper, our investigation entails executing a statistical scrutiny of the worldwide proliferation of breast cancer, as well as delving into various machine learning and deep learning techniques for detecting breast cancer, along with their constraints and intricacies. Additionally, we have proposed a hybrid Machine Learning (ML) approach for detecting breast cancer with utmost accuracy, which streamlines the assessment procedure presently employed by radiologists in screening mammograms.
IoT Based Single Axis Solar Tracker

This paper presents a solar photovoltaic (PV) array positioning and tracking system that integrates with the Internet of Things (IoT). The proposed system is designed to optimize the energy output of a solar PV system by adjusting the position and orientation of the PV array in real-time based on weather and solar irradiance data. The system consists of a microcontroller-based control unit, sensors to measure weather and solar irradiance, and a stepper motor to adjust the position of the PV array. The control unit uses the data from the sensors to determine the optimal position of the PV array and sends commands to the stepper motor to adjust its position accordingly. The IoT integration allows the system to communicate with other devices and services, enabling remote monitoring and control of the PV array. The system's performance was evaluated through simulations and experiments, demonstrating significant improvements in energy output compared to a fixed-position PV array. The proposed system has potential applications in both residential and commercial solar PV systems, providing a cost-effective and efficient solution for maximizing energy generation
A Transfer-Learning Based Unbiased Voting Bone Cancer Detection Framework from Histological Osteosarcoma Images

The most lethal types of bone cancer, osteosarcoma, have a substantially higher fatality rate for the child. The clinician’s labor can be significantly decreased and patient outcomes may be enhanced by using computer vision-based technology. A paradigm for the hard-voting, impartial identification of bone cancer is presented in our study. A histological osteosarcoma image dataset with three separate groups that is publicly available is employed in this research. A comparison of the models developed from balanced and unbalanced training sets has been accomplished using seven customized deep transfer learning algorithms, including VGG19, MobileNetV1, MobileNetV2, ResNetV250, InceptionV2, NasNetMobile, and EfficientNetV2-B0. Kappa scores and accuracy are foremost for MobileNetV2 and those are 87.55% and 92.14% respectively. In contrast, the creative Max Voting classifier eventually integrates a number of weakly performing transfer learning classifiers and exhibits robust performance on malignant categories. The Max voting classifier outperforms other existing models. The accuracy rate of the Max Voting classifier is 93.88%.
An Efficient Voting Based Consensus Algorithm for Permissionless Blockchains

Blockchain is an emerging technology that offers a wide range of applications in various sectors such as financial services, industrial products, healthcare, and media, among others. The important component of any blockchain system is the consensus algorithm. It plays a major role in deciding the performance, efficiency, and security of the blockchain network. Currently, there are over 30 consensus algorithms in use, including Proof-of-Work (PoW), Proof-of-Stake (PoS), and Proof-of-Authority (PoA), among others. However, these algorithms have drawbacks in terms of security, stability, and productivity. While PoW is widely used by popular cryptocurrencies, it is unsustainable and inadequate for ensuring blockchain-based security solutions. PoS is more vulnerable to the concentration of wealth, leading to potential system destabilization by those with significant stakes. To address these issues, Delegated Proof-of-Stake (DPoS) is created, an algorithm that eliminates the competition for computing resources in block production. This approach reduces the cost of block generation and introduces a fully PoS-based election system, but still has security issues. This work introduces an enhanced version of DPoS called Improved Delegated Proof-of-Stake (iDPoS), which includes a downgraded mechanism to eliminate malicious nodes while maintaining security. Performance analysis demonstrates that the improved consensus algorithm is more efficient compared to PoS and DPoS consensus algorithms.
Performance Analysis of Dimensionality Reduction Techniques for Datasets using Deep Learning

Dimensionality reduction techniques play a crucial role in handling high-dimensional datasets by transforming data into a lower-dimensional space while preserving essential information. Deep learning, a powerful subset of machine learning, has shown remarkable success in various domains. Dimensionality reduction strategies can be used to lessen the complexity between various qualities. With these techniques, a lower-dimensional dataset is created from the dimensions of the initial input dataset. Feature extraction and feature selection are the two primary categories of dimensionality reduction techniques. In contrast to the feature selection method, which selects a subset of features from the original set to create a smaller subset that can be used to build the model, the feature extraction method reduces the high-dimensional dataset to a lower dimension space, that is, a space with fewer characteristics with different values from the original dataset. We've examined a variety of dimensionality reduction techniques, including traditional methods like PCA, LDA and MDS, throughout data processing, to minimize the dimensionality of a sizable and complicated dataset. Additionally, we talked about numerous methods for reducing the dimensionality of data and contrasted them according to different factors. In conclusion, this research provides valuable insights into the effectiveness of dimensionality reduction techniques for high-dimensional datasets. Additionally, we identify potential areas for future research and improvements in the realm of dimensionality reduction.