Reviewerly/exHarmony huggingface dataset:
A framework for Interoperability in Blockchain Without Any Intermediaries

This Blockchain technology has been growing for Information and Communication Technology (ICT) because of its features such as decentralized, peer to peer, secure, and immutability of data in the network. Due to enormous features of blockchain technology like immutability, tamper free and decentralised data storage, blockchain is gaining popularity day by day. Though there are divergent applications of blockchain but again due to lack of interoperation between different blockchains there is still a doubt of sustaianbility of blockchains for next decade.Interoperability between two or more blockchains is important for sharing exchanging and transfer of different data, assets and transactions from one blockchain to another blockchain to increase the scalability of blockchains as well as to increase the flesxible usage of blockchains in the security domain. Though there are few solutions available for interoperability, those limited to only for exchanges or for asset transfer but not for all the interoperability purposes together, such as transfer, share, exchanges all together, also they are using some intermediaries for all these type of interoperation. So in this paper we proposed a design of a framework for interoperability solution without any intermediaries; so that any blockchain can interoperate with each other without any external entity without compromising blockchain features
Software Effort Estimation using Multi-objective Binary Rao Algorithm

Software effort estimation depends on the standard of features that are considered. Considering irrelevant features will effect the model by degrading its performance. Most Research studies focus on Single Objective optimization for feature selection. A Multi-Objective Binary Rao Algorithm for Feature Selection is proposed in this work to estimate software development effort. We have validated the performance of proposed approach using four bench mark datasets and compared with single objective Binary Rao algorithm and NSGA-II algorithm. It is observed that our approach is able to provide better performance than the compared models.
Emerging Technologies in the Field of Smart Monitoring Healthcare System with Cardiac Disease

The genesis and spread of illnesses have become a serious problem in the ever-expanding world of innovation and evolution. Technology-assisted precaution, disease control, and prevention have emerged as the top challenges facing the medical community. Due to our hectic work schedules, it is now hard to keep a healthy routine. The answer to the issues raised earlier is a smart health-monitoring system. The creation of smart and affordable sensors that provide in-person, real-time health screening has been facilitated by the current industry 5.0 and 5G revolution. IoT system integration increased patient confidentiality information integrity and effectiveness to guard against patient data exploitation. The establishment of a smart health monitoring platform model on Bi-LSTM (bidirectional long short-term memory), which uses deep learning to evaluate patient records in order to fulfill numerous goals, has made it possible to provide patients with preventative healthcare and control patient fatalities. This has made it feasible to diagnose chronic conditions early, such as cardiac diseases, which was previously impossible. The study includes a thorough analysis of the health system, as well as recent technological developments and current difficulties.
Design and Implementation of Low Quantum Cost Reversible Universal Shift Register

This paper proposes a novel and efficient design of a 4-bit Reversible Universal Shift Register (RUSR) using reversible logic gates. RUSR performs shifting operations the same as CMOS-designed universal shift registers, such as left shift, right shift and parallel load, respectively. In this paper, we are designing a 4-Bit RUSR using a reversible Master-Slave D-Flipflop (RMSDFF) and reversible 4X1 Multiplexer (RMUX41). The proposed RMSDFF and RMUX41 are designed using Feynman (FG), Fredkin (F) and Modified Fredkin (MF) gates. Modified Fredkin gate plays a remarkable role in the implementation of the proposed optimized architecture. The efficiency of the proposed architecture is demonstrated in terms of garbage outputs (GO) and quantum cost (QC). The proposed architecture reduces the power consumption with a low quantum cost, a minimum number of reversible logic gates and garbage outputs. Due to this, the proposed design is particularly beneficial in quantum computing, Low-power CMOS design, and other applications. In this paper, the RUSR is designed by the proposed RMUX41 and RMSDFF, which are designed with the reversible logic gates using the hardware description language Verilog and verified with the simulated results using the ModelSim Altera tool.
Fake Product Identification using Blockchain

Manufacture and sale of counterfeit goods has been plaguing several business sectors for many years. The production of counterfeit goods are increasing, this has an impact on the sales and earnings of the businesses. By stifling revenues, economic growth, and consumer health, it has an impact on governments, global trade, businesses, and consumers. The current technology used for combating counterfeit goods rely on a centralised authority. Issues with this design include single point processing, failure, and storage. Blockchain technology has can be used as an alternative to address all these issues for these problems.QR code can be used along with a blockchain hash code to uniquely identify every single product that ever enters the supply chain, hence no fake product can flood the supply chain. Ethereum blockchain is utilized and smart contracts are deployed to store the data about each and every product. Each products whole history can be tracked by using its QR code.
Review on Writer Identification and Verification Methods

Biometric authentication requires identifying patterns based on unique behavioral or physiological characteristics. The most commonly considered physiological biometric systems are fingerprint scanning and facial recognition. Behavioral biometric systems are signature, handwriting, and voice recognition. Verifying physical features occurs efficiently through comparison, whereas recognizing behavioral components presents a challenge. Among the different biometric modalities, writer identification and verification have garnered considerable interest because of their potential to improve accuracy and reliability of authentication systems. Writer verification forms the legal basis for documents such as wills, contracts, and suicide notes. Handwriting verification tasks require years of training, and handwriting analysis is time-consuming. Therefore, researchers have tried to build accurate and efficient writer identification and verification systems. Researchers over the decades have explored various approaches for writer identification and verification tasks, including traditional machine learning methodologies and, more recently, deep learning methods. This review paper presents an informative summary of the databases, preprocessing methods, feature extraction techniques, and classifiers employed in identifying and verifying writers. While writer authentication has shown promising results, further research is needed to address several challenges in this task.
Addressing Drawbacks of MART and Enhancing Predictive Performance<sup>*</sup>

Boosting, a successful machine learning algorithm, is often used for regression and classification tasks. Boosted ensemble algorithms, particularly when combined with tree base learners, can generate highly accurate and robust predictive models. This study explores the weaknesses of the well-known approach, Multiple Additive Regression Trees (MART), and proposes a new approach called Laplace Additive Regression Trees (LART) that addresses this issue by adding random Laplace noises. The algorithm is then improved upon by combining it with Dropout and multiple Additive Regression Trees, resulting in DLART [1]. In an experiment using a previously studied dataset, we compared the performance of LART, DLART, MART, and DART. The results were promising, as LART and DLART produced significant gains in accuracy for this particular task. For future studies, it would be beneficial to assess the effectiveness of LART on complex data sets and various other machine learning tasks, such as those related to data mining [2]
Volume Controller using Hand Gestures

This work presents the development of a volume controller using hand gestures as the input to control the system.This system uses the OpenCV module to detect and control gestures. The application captures images and videos from the webcam, and based on the user's input, it controls the volume of the system.Hand gesture recognition systems have a wide range of applications, including volume control, navigation, gaming, and sign language interpretation.In this project, we are developing a hand gesture volume controller system. The system uses a high-resolution camera to detect hand gestures, which are then used to control the volume of a device. The system is developed using the Numpy, OpenCV, and Python packages.The system is designed to be intuitive and easy to use. Users can simply make a gesture with their hand to control the volume of the device. The hand gesture volume controller system has the potential to revolutionize the way we interact with devices. By allowing us to control devices with our hands, hand gesture recognition systems can make our lives easier and more convenient.
Exploring the Core Components of Cloud Computing and Its Architecture

Cloud computing has been a revolutionary technology disrupting the IT industry for the past decade. It has revolutionized itself for individual, business and company growth. The low cost, scalability and flexibility have made it an innovation for large and small companies to grow themselves, and due to this, it has reached and provided services to millions of users. This article explored the overview of cloud computing, including its architecture, service models, applications, and deployment models. The article also reviewed different research articles and compared various existing approaches. The paper also discusses the current trends and future scope of cloud computing. Load-balancing approaches in cloud computing have been studied and compared on the basis of various factors. At last, the paper highlights the important applications of cloud computing. The article concludes by providing the future scope.
MRI Synthesis via CycleGAN-Based Style Transfer

Medical imaging plays a crucial role in modern medicine by supporting illness diagnosis and therapy. However, there are difficulties in illness diagnosis using medical imaging, such as the costs and infrastructure to conduct an MRI scan, and the strenuous task of correctly interpreting an MRI scan. It is necessary to develop a method for producing MRI images with adjustable contrast levels. A cyclic Generative Adversarial Neural Network called CycleGAN, which performs well with unpaired data and is best suited for medical imagery, is used for the proposed healthcare platform to create artificial MRI scans. The results show that our method converts non-fat saturated MRI images into fat saturated images more effectively than a benchmark pix2pix approach, resulting in a PSNR (peak signal- to-noise ratio) of 12.89, structural similarity of 0.97, and KL divergence of 0.07, using the MRI dataset, a sizeable collection of freely accessible MR brain pictures.
Software Bug Count Prediction Model Based on Software Source Code

Software bug prediction (SBP) model is a classification model that predicts buggy or non-buggy modules of software projects, while the Software bug count prediction (SBCP) model is a regression model that predicts the precise number of bugs in each module. So, SBCP model is a more useful model for a software tester to rank the software modules. Many SBP models are developed using object-oriented (OO) software metrics (PROMISE datasets) and simplified PROMISE source code (SPSC) datasets (DSs). But, no SBCP model was developed using SPSC dataset. To fill this gap, we created SPSC DSs using features extracted from the software source code’s Abstract Syntax Trees (ASTs). Further, we developed SBCV models using Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) over 8 SPSC DSs. The experimental results are compared with the six standard machine learning (ML) algorithms and two deep learning (DL) models (LSTM and CNN) implemented over 8 PROMISE DSs. We have applied a non-parametric statistical test (Nemenyi test) to show the significance of the proposed SBCP model. The proposed improved CNN model produces a better mean absolute error (MAE) of 0.50 and a mean relative error (MRE) of 0.19 averaged over eight SPSC DSs.
A Novel Framework for QR Code Detection and Decoding from Obscure Images using YOLO Object Detection and Real-ESRGAN Image Enhancement Technique

QR codes have become increasingly popular in various applications, such as inventory management, advertising, and payment systems. However, detecting and decoding QR codes from blurry or unclear images can be challenging due to various factors, such as low image resolution, noise. The research aims to propose an efficient approach for detecting and decoding QR codes from unclear images using the You Only Look Once (YOLO) object detection model and deep super-resolution techniques implemented through Real-ESRGAN [1]. Authors trained and evaluated five different YOLOv8 object detection models with varying sizes and parameters to detect and crop the QR code from the input image. Based on the evaluation results, authors selected the model which achieved the best performance in terms of precision and recall. The cropped QR code based on YOLOv8 bounding box, is then enhanced using real-ESRGAN [1], which applies deep super-resolution techniques to improve the quality of the image. Finally, the enhanced QR code is decoded using three QR code utility libraries in Python, namely pyzbar, zxing, and opencv, which are executed in parallel using multithreading for efficient retrieval in less time. The results after testing demonstrates the effectiveness of proposed approach in accurately detecting and decoding QR codes from blurry or unclear images. The proposed approach can be used in various fields, such as inventory management, advertising, and payment systems, to provide a seamless user experience.
Fraud Detection System Analysis

The study's results suggest that employing the ISOMAP technique holds significant promise in the field of fraud detection systems, particularly in reducing the complexity of datasets. ISOMAP was found to preserve the inherent data structure, retaining essential information for spotting fraudulent transactions. By using ISOMAP to lower data dimensionality, computational demands were reduced, a crucial factor for real-time fraud detection applications. When compared to traditional methods, the effectiveness of the fraud detection system based on ISOMAP was evaluated. The outcomes showed that ISOMAP surpassed traditional methods in terms of accurately detecting fraudulent transactions. This improvement in performance can be attributed to ISOMAP's ability to capture non-linear data relationships. Furthermore, the study's findings have significant implications for implementing fraud detection systems in various sectors, including banking, insurance, and online retail. The application of ISOMAP for dimensionality reduction can enhance system accuracy and reduce the occurrence of false positives, thereby mitigating potential financial losses for businesses. To sum up, the project successfully demonstrated the efficacy of using ISOMAP for dimensionality reduction in a fraud detection system. The results showed that utilizing ISOMAP effectively captured the underlying data patterns, leading to improved accuracy in identifying fraudulent transactions. This research provides valuable insights into how ISOMAP can enhance the performance of fraud detection systems, highlighting its practical relevance in real-world scenarios.
A Fair and Scalable Consensus Algorithm for NFT-Based Decentralized Autonomous Organization for Digital Art

Blockchain is an emerging technology that finds applications across various industries by providing secure transaction records that cannot be tampered with. It operates as a decentralized and open ledger, offering additional benefits such as traceability and immutability, which greatly contribute to its desirability. Two important blockchain applications include Non-Fungible Tokens (NFT) Marketplaces and Decentralized Autonomous Organizations (DAOs). NFTs are unique digital assets like artwork or music that can be bought and sold using cryptocurrency, ensuring both security and efficiency. On the other hand, DAOs are transparent organizations that make decentralized decisions. In our work, we have developed an integrated NFT marketplace and DAO that leverages NFT ownership to determine the weights of choices. The implementation is based on Ethereum ERC-721 tokens. However, the Proof of Stake (POS) consensus algorithm used in Ethereum 2.0 lacks fairness. To tackle this issue, we introduce the Delegated Reputation-based Proof of Elapsed Time consensus algorithm (DRPoET). Evaluations conducted on an Ethereum testbed demonstrate that our integrated DAO benefits from fair weights and improved transaction latency and fairness compared to the POS algorithm.
A Robust Edge Detector In The Presence Of Impulse Noise

Edge detection is a challenging task with many applications in image processing. Edge detection in images can be accomplished using various methods and well-known operators. The effectiveness of these techniques is frequently influenced by the extent of image detail preserved and the application at hand. Edge detection amplifies the noise, which has a major impact on their structural features. Impulse noise, often known as salt-and-pepper noise, is one such type of noise that affects the quality of images. Smoothing filters are frequently used to eliminate this kind of noise from images in order to reduce the noise's variation and maintain the image quality. The study presents a novel technique for detecting edges in images that utilizes denoising methods to retain image details while preventing blurring and preserving sharp edges along boundaries, even when high impulse noise is present. The effectiveness of the proposed approach is evaluated by measuring its performance using Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), and is compared to the commonly used CANNY Edge detector.
Optimization of Green Computing in Data Privacy in Healthcare BCT Framework

A central element of blockchain networks is immutability, which enables each participant to have a precise global record of all transactions. In permissioned blockchain networks, where validators are selected from a trusted group, immutability is easier to establish. This critical blockchain property can only be realized on public platforms through decentralized economic mechanisms. Despite the rapid advancement of IoT's technology, where an increasing number of medical devices and sensors are linked to Internet, security and inadequate energy resources owing to broadcast remain major obstacles. In most situations, patients wear many medical devices that wirelessly send identified medical info to node-servers. This may result in excessive traffic on communication networks and significant energy utilizations. It can be difficult to defend against various threats of unauthorized data access during the safe data gathering and transfer in healthcare apps to centralized servers utilizing IoT's. Large-scale security measures should be implemented as a result to ensure.
A Comprehensive Review of Machine Learning Techniques for Voice-Based Gender Recognition

For humans, determining a person’s gender by looking at a few traits is not difficult. This does not, however, apply to machines. To do this, machines must be trained. The process of analyzing speech data to detect a speaker’s gender is known as gender recognition by voice. There are several uses for gender recognition in the real world, including enhancing human-machine communication. For instance, the advertising might be tailored based on the gender and age of the caller. In criminal instances, it can also assist in the spotting of the accused, or at the very minimum, it can lessen the number of accused. This work presents a review concerning methods that use data from pitch and frequency of voice. We have described the difficulties encountered and examined the typical implementations of these approaches. The results show that data sets collected in controlled situations have shown good performance, but there is still much that can be done to increase the gender recognition system’s robustness in real-world settings.
Detection and Control of Cyberbullying via Machine Learning

Cyberbullying is disturbing harassment that has grave repercussions. It can be found in several forms and most social networks only display it as text. The online behavior of customers has been addressed, but the developing decade does provide real challenges. It has been challenging to increase incidents of provoking and harassing people as well as casualties. Intelligent frameworks are needed for the programmed discovery of these instances. The majority of the models created in this research may be applied simultaneously to a single social network, and a sizable portion of recent studies have been tackling this issue using traditional machine learning techniques. Machine learning-based models claim they can overcome the drawbacks of conventional models and improve the execution of discoveries, and they have shown methods for identifying cases of online abuse. Although there are numerous antiquated incident control models, an effective torture order is still unnecessary. With the use of machine learning and language preparation, it will be possible to successfully detect harassment in the virtual world and prevent its brutal effects. It is suggested that a methodology be used to describe cyberbullying in two ways. Support Vector Machine, Logistic Regression, the Decision Tree, AdaBoost Classifier, and Random Forest are just some of the machine learning models that have been employed in conjunction with metrics. Precision-Recall Accuracy, F1 score. Logistic regression yielded the greatest value, with accuracy 83.4, precision 84.0, recall 83.2, and F1 score 83.5, and Gaussian nave bayes yielded the lowest value, with an accuracy 45.8, precision 52.2, recall 45.8, and F1 score 45.7.
Blockchain in Healthcare System Using Hyperledger Fabric- a Comprehensive Review

A key component of the society structure is now medical healthcare. Understanding how to handle personal health data appropriately is becoming into an intriguing research issue because it is a vital and sensitive asset for each patient. In this overview, the solicitations of the Hyperledger Fabric blockchain with IoT devices are shown in order to limit access and create a chain of trust for the healthcare industry. Through encryption protocols, access controls, and cryptographic methods, the Hyperledger Fabric is designed to be safe against unauthorized access and use. In this study, a blockchain-based solution is defined following an assessment of the specifications for interoperability and solitude in the sharing of medical data. Considering its enterprise-grade data processing capabilities and other solitude protection features the Hyperledger Fabric architecture was evaluated. Our evaluation of the study's objectives and solutions is intended to help in the creation of an architecture or model that addresses the problems mentioned in the article. Another exciting subject that calls for more study is the solicitation of "blockchain and Internet of Things (IoT)" technologies in the healthcare sector. Network scalability is supposed to be enhanced by supporting low-end devices with patient-centric apps under smart contracts.