Reviewerly/exHarmony huggingface dataset:
Multiclassification Hybrid Model Using CNN and SVM for National Emblems

The national emblem is the symbol of high respect for the nation, this symbol indicates the nation’s power, wealth, pride, and people. The national emblem has a special respect and place in both the nation’s history and even the nation’s present. These national emblems represent the identity of a nation. These national emblems are mainly used on occasions of national importance. So, this was important to preserve these emblems’ importance in this AI-growing modern era. So hybrid model is developed over 8 national emblems using CNN and SVM deep learning approaches. These 8 national emblems represent the eight countries, India, the UK, Germany, Australia, New Zealand, Italy, the USA, and the Netherlands. A dataset of 9950 images was used to train this hybrid model. 96.92% is the best accuracy obtained for this multiclassification of national emblems. 90.15% is the best F1 score recorded. And macro average is 86.0225% for this classification. In the future, more than 8 national emblems can be considered for multiclassification. And other hybrid models can be developed for obtaining better accuracy.
Graphical Password Authentication Using Decentralized Single-Sign-On

Authentication is a crucial process that verifies the identity of an individual or system seeking access to resources or services. Password-based authentication systems, which are the most common and widely used, can be complex, difficult to remember, and vulnerable to various attacks. Graphical passwords offer a more user-friendly alternative to text-based passwords and have been validated by software companies and psychology research. However, existing password authentication systems, including OAuth2.0, Google Auth, and Facebook Auth, have limitations, such as single-point failure, centralized architecture, privacy concerns, and resource-intensive computation systems. This paper proposes a decentralized platform, Visual-D-Auth, that leverages blockchain and IPFS technologies to provide secure and reliable access to users through a Decentralized Single Sign-On (DSSO) technique. The Visual-D-Auth system eliminates the need for remembering multiple usernames and passwords in a decentralized environment while providing improved security, user privacy, and convenience. The paper introduces a unique graphical password generation algorithm that generates highly secure passwords using a combination of images and a user-selected username. Finally, the Visual-D-Auth solutions address the most common weaknesses of graphical password systems, such as shoulder-surfing and smudge attacks, offering a more secure and user-friendly solution. Overall, this research presents a novel approach to authentication that could potentially revolutionize the way we authenticate access to resources and services.
Lung and Colon Cancer Classification using EfficientNet B3 Transfer Learning Model

Lung and colon cancers are among the most common kinds of cancer in the globe. Lung cancer affects the respiratory system, while colon cancer impacts the digestive system. Both cancers have a high mortality rate and are often diagnosed at an advanced stage. Early detection and treatment are critical in improving survival rates for both lung and colon cancer. The study conducted an experiment to classify lung and colon cancer into five distinct classes using an EfficientNet B7 model. The model has been trained using Adam as the optimizer for a total of 12 epochs along with the batch size of 128 and the resulting accuracy and loss plots were carefully examined. Following this, accuracy is calculated based on precision, recall, and f1 score, and the model’s accuracy is 98%. The study’s findings show that the model accurately classified the various types of lungs as well as the colon cancer with high precision and recall. These findings indicate that the EfficientNet B7 model is a good fit for accurately classifying lung and colon cancer.
Fault Prediction Method of Watt-Hour Meter Based on Decision Tree Algorithm

Their components are complex and the fault types are diverse. Most maintenance personnel do not have the ability to repair all types of faults. Therefore, when smart meters fail, they can accurately predict the guide the operation and maintenance personnel to dispatch, It will effectively reduce the human and resource costs and maintenance. The demand for electricity. The current situation requires and electricity to meet the growing. In this case, it is very important to predict the possible faults in the power system, so that these faults can be avoided at any cost. This can be achieved by using various technologies, such as load forecasting, voltage stress testing, etc. However, an uncommon technology is the watt-hour meter fault prediction method (WattFPA). The fault prediction technology of intelligent watt-hour meter is studied. Firstly, a deep neural network structure that can extract the depth attribute of fault data is designed, and the model optimization and parameter debugging methods are studied, and the optimal model is constructed; Secondly, in multi-class in intelligent meter fault prediction, a cost-sensitive multi-class XGBoost model is designed, and the relevant parameter debugging method is analyzed, and the model effect is verified on the test data.
The Enhanced Performance of Hierarchical Fusion Based Data Mining for High Density Data Traffic in Big Data Servers

Hierarchical fusion based data mining is a novel approach to analyzing high density data traffic, where multiple sources of data are combined in a hierarchical manner to develop an effective representation for the data. This is typically done by creating a hierarchical structure in the form of a tree, consisting of multiple methods for feature extraction, clustering and data classification. These hierarchical structures can be used to identify patterns in the data that are not easily discernible otherwise. The hierarchical fusion based data mining has been applied to a variety of domains such as image analysis, text mining, and web mining. It has been shown to be effective in finding patterns in high density data traffic, which otherwise may be difficult to detect due to the sheer volume of data. Additionally, the hierarchical structure allows for better scalability when the data sets grow with more data.
Automation of FaaS Serverless Frameworks OpenFaaS and OpenWhisk in Private Cloud

A methodology for cloud-native development called serverless facilitates the creation and execution of programmes by developers without worrying about maintaining servers. As no automation is available to implement FaaS serverless frameworks in private clouds and manual implementation is a tedious job. If the same tasks are performed manually, there is a greater risk of human error as well as an increase in cognitive strain that can take up to 4-5 hours for installation and configuration. Therefore, we are going to provide a command line tool to automate the process to implement FaaS serverless frameworks Open-FaaS and OpenWhisk in private clouds, which is easy to configure and straight forward to use. The proposed tool has the potential to save significant time and cost for customers compared to hiring a DevOps engineer to manually perform the installation and configuration. Additionally, the potential revenue generated from the tool’s use in the financial industry alone suggests a promising market for this type of automation tool. Overall, this paper highlights the usefulness and importance of automation in facilitating the adoption and implementation of serverless architecture in private clouds.
Performance Validation of Secret Data in IoT using Blockchain

Many industries have serious concerns about the secure transmission of sensitive data, and the use of blockchain technology, steganography, and optimization algorithms has emerged as a potent remedy. Steganography and optimization algorithms can hide data and increase the effectiveness of the transmission process, while blockchain technology offers a transparent and secure platform for data storage and transfer. The employment of these technologies in tandem to offer a reliable and secure solution for the transfer of sensitive data is provided in this paper. We also go over this strategy's possible advantages and difficulties and offer suggestions for successfully putting it into practice. Overall, a promising method for the secure transfer of confidential data in the current digital era is provided by the combination of blockchain, steganography, and optimization algorithms. This tactic ensures the development of a reliable and efficient blockchain platform for the management of sensitive data while upholding predetermined standards.
Captcha Generator for Visually Impaired (Colour-Blind)

Today's Internet users come from a variety of ages and groups. Humans enabled in other ways also use the Internet. Some websites are designed for people with special needs. Many websites offer human user services, but unfortunately some computer programs are designed to exploit this service. As a result, a specific system called captcha was introduced (Completely Automated Public Turing test to Tell Computers and Humans Apart). For security and privacy purposes websites widely use CAPTCHAs. However, for individuals with visual impairments, traditional text-based CAPTCHAs are not very suitable. With many captchas already in place to help the visually impaired no such captcha exists that specially aims to aid the colour-blinded in using captchas. In this research paper we have tried to understand and showcase how the colour-blinded can be aided in using captchas. To achieve the same, ML has been used.
Dodging Turtis - NFT Gaming on Blockchain

The project is an NFT-based game that runs on the POLYGON network (Matic Mumbai Test-net) on layer 2 of the Ethereum Blockchain and is powered by decentralized Chainlink Oracles. The game involves a Turtle character who dodges through the platforms coming towards it with increasing speed. The NFT here is the Turtle character. Every time a row of blocks is dodged by the turtle, 10 points are added to the score. The current game is over when the Turtle collides with any of the platforms. In order to dodge through the blocks, the Turtle can move left or right using the arrow keys. The speed of the left and right movement of the Turtle varies for each Turtle NFT. The game is really fun to play and it can get really challenging as the speed of the platforms increases with time.
Performance Evaluation of Machine Learning Methods for Detecting Credit Card Fraud

Fraud regarding Credit card transactions is a growing concern for both consumers and financial institutions. Traditional methods of detection, such as rule-based systems and manual review, are often time-consuming and ineffective. Machine learning algorithms offer a potential technique for identifying fraudulent transactions regarding credit cards. These algorithms can learn from past transactions and detect patterns that indicate fraudulent activity. In this paper, we review different machine learning methods that have been used to identify credit card frauds, including decision trees, neural networks, and clustering algorithms. We also discuss the challenges associated with using these techniques, such as handling imbalanced data and ensuring robustness to changing fraud patterns. Ultimately, this article reflects the need for more research in this field and shows how machine learning approaches might be used to predict fraud using credit cards.
Secured E-Voting System Implementation Using Block-Chain Network

Electronic-Voting (E-Voting) system influences the real time with its cost effectiveness and increases the voter turnout at the ballots. Voters can give their vote from wherever they are, also no need to stand and wait for the opportunity. Unlike the electoral system, there are numerous traditional systems, which persist with the usage of ballot paper in its implementation. The issue of protection and transparency is a threat from nevertheless huge elections with the traditional machine (offline). The present ongoing voting technology is not safe enough to conquer the vulnerabilities in large-scale manipulations of votes. Electronic voting must be admissible, precise and secure when used for elections. Because of these issues, it became decided that the existing frameworks want to be stepped forward to be utilized in balloting systems. Block chain technology came into existence to erase all these difficulties. It offers decentralized nodes for EVoting and each vote here has proof of work, so theoretically impossible for voting proxies. Each block of the block chain technology system is hashed and if one of the blocks changes then it automatically changes the adjacent and providing the perfect replacement for the trivial voting system with distributed, nonrepudiation, security protection characteristics. That is why it sounds impossible to break into the network to make changes. Finding those blackholes may change the direction of thinking and it may result in the best outcome. This has afforded a conceptual description of the meant block chain-based digital vote casting utility and an advent to the essential shape and characteristics of the block chain in connection to electronic voting. Because of this examination, it was observed that block chain structures may additionally assist resolve some of the plights that now plague in the election structures.
Impact of Artificial Intelligence on Virtual Learning Ecosystem

Nowadays, one of the most intriguing fields of research in information technology is artificial intelligence (AI) and machine learning (ML). For educational researchers and scientists, it offers an excellent prospect. Since education practitioners have little awareness about utilizing AI in the educational system (AIEd), it is, therefore, a promising field of research for improving the quality of educational practices. This study aims to investigate AI-ML to create an AI-enabled educational eco-system. Researchers have expressed interest in using educational data mining and the data science approach to find patterns in extensive educational data collections. This study demonstrates how intelligent applications based on AI can be used to improve teaching and learning.
Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction.
Personalized Medicine Recommendation and Disease Flagging Model Based on User’s Previous Orders

The Recommendation systems gaining importance every passing day and are frequently used in e-commerce websites, content streaming platforms, social media platforms, and other applications to analyze large amounts of data and recommend products or services to users based on their past actions, preferences, and other pertinent factors. This kind of system can effectively be used in healthcare to provide personalized recommendations to patients. In this paper, a recommendation model has been proposed that flags the users with the diseases that they may have based on analyzing their historical medicine order and recommends them with alternate related medicines and products. The proposed model is based on a machine learning technique that takes into consideration the user’s medical history, disease diagnosis, and prescription orders and recommends other related medicine and products. The proposed model uses a combination of supervised and unsupervised learning algorithms. A sizable dataset of patient orders from a top pharmaceutical retailer served as the basis for training and validating the model. The simulation results show that the model can handle complex situations, such as multiple medications and medical conditions, and predict medication recommendations with high accuracy.
Edge Computing based Traffic Control Management for Distributed Environment

Mobile Edge Computing (MEC) users’ quality of service (QoS) may be significantly improved by local communication. The backbone of 5G communication is the Radio Access Network (RAN), which will generate and transmit a significant amount of user traffic to the fronthaul and backhaul gateways. Due to the fact that fronthaul and backhaul gateways are frequently constructed utilising optical networks, a bottleneck network will form when there is more traffic coming than the gateways can handle. The aforementioned problems must be resolved in order to meet real-time communication’s ULL (ultralow latency) criteria. In this study, we suggested that user traffic at both gateways be managed using MEC-based traffic management.) Chat, streaming, interactive, and background user traffic were separated into their own separate categories by the system. Additionally, split traffic may be stored on a MEC server connected to a gateway. Consequently, the MEC server. With the use of a computer simulation, the outcomes of the simulation may be verified.
Image Processing Based Intelligent Mini Robotic Face Recognition System

The number of devices connecting to the Internet is increasing rapidly along with the exchange of large amounts of data. Many types of problems used to come in managing that data, but today’s new techniques coming in this time have helped a lot in managing big data. Among those techniques, machine learning is a very advanced and precise technique. With the help of which we can manage and categorize the data very easily in the coming times. Machine learning models were not found to be very complex. Such techniques, technology and algorithms are used in these to make it easier and more accurate for provide the result. In this paper have used some algorithm to increase the processing and best result like MX Net with 89%, Convolutional Neural Networks with 85%, ALEX NET with 83% and Support Vector Machine with 75% results. In such models some other techniques are used to make the hardware work continuously with the systems. Like as NLP, Open CV and other techniques which can analyze the data and show it in a systematic manner. And in the last of all this network, rare hardware units are used to analyze the data from it to make the model. The purpose of this paper is just to understand the purpose of technologies like network and machine learning in the future. And by doing it properly, life should be made easier. Now if the same human again comes to that hospital, then he calls from that unique ID by matching the previous data through this model.
Agricultural Management System Based on GPS and GIS

This article introduces an intelligent agricultural management system based on Global Positioning System (GPS) and Geographic Information System (GIS). This system uses GPS technology for geographic positioning and precise positioning of farmland, collects high-precision geographic information data, and combines GIS technology for analysis, processing, and visualization presentation. This system can intelligently manage farmland, including crop planting, fertilization, irrigation, harvesting, and other processes. At the same time, it can monitor and predict environmental factors such as soil moisture, temperature, and light, and make timely adjustments to ensure the healthy growth, high yield, and stability of crops. The advantages of this system include improving production efficiency, reducing losses, saving resource costs, and increasing profits. The experimental results indicate that the system can effectively improve agricultural production efficiency, promote sustainable development of agriculture, and is of great significance for achieving precision agriculture, agricultural intelligence, and other directions..
Similar Images Finder Using Machine Learning

Finding similar images using machine learning has become an important technique for image retrieval and organization in a numerous applications. This method involves training deep learning models to recognize visual patterns in images and extract features that can be used to identify similarities and differences between images. These features can then be used to create high-dimensional vectors that represent each image, which can be compared to find similar images. Various similarity metrics and clustering algorithms can be used to group similar images together, enabling efficient searching of large image datasets. This paper provides an outline of the methodology for finding similar images using machine learning, including data collection, preprocessing, deep learning model training, feature extraction, similarity calculation, and clustering. The paper also discusses the practical applications of this technique, such as image retrieval, image organization, and image analysis in fields such as medical research, computer vision, and machine learning. The proposed system can be a valuable tool for content creators, online platforms, and law enforcement agencies to ensure the protection of intellectual property rights and prevent unauthorized use of copyrighted images.
Processing and Detection of Lung and Colon Cancer from Histopathological Images using Deep Residual Networks

Lung & Colon cancer are amongst the leading cause of cancer related deaths worldwide. In this study, we used a five-class lung and colon cancer histopathology dataset and applied various image preprocessing techniques such as contrast stretching, unsharp masking, and resizing. We then trained a ResNetl01 model on this preprocessed dataset and achieved a high accuracy of 99.7%. The increased reliability and robustness of deep learning-based histopathology classifiers with high performance computers can enable instantaneous and automated diagnosis which can further help treatment and recovery.
Post Quantum Secure Aggregate Signature Assisted Data Authentication for Internet of Vehicles

The Internet of Vehicle enables million of vehicles to transfer sensitive information through an Internet of Things assisted networking system. It is a very challenging idea wherein different types of data authentication protocols have been designed number theory based assumptions (RSA/DLP). The quantum computers are big threats to number theoretic assumptions. In this article, we have designed a post quantum secure aggregate signature assisted data authentication framework for internet of vehicles. Further, a secure aggregate signature assisted data authentication framework have been proposed that supports batch data verification. We have proposed an efficient aggregate signature to verify authenticity of multiple vehicles in one step. The security of protocol relies upon two assumptions, (1) Module Learning With Errors and (2) Module Short Integer Solution. The proposed design ensures security against quantum attacks. The design is simulated in ns3 to capture latency and throughput, respectively.