Reviewerly/exHarmony huggingface dataset:
An Advanced Multi-Model Cloud Services using Load Balancing Algorithms

Cloud computing is the domain that can provide online services to people without any hardware requirement. Cloud computing has become an emerging technology for companies migrating into cloud computing. Cloud computing offers several advantages for the users, such as data storage, security for data, load balancing, and cloud hosting. Every cloud user needs on-demand services that can provide online services based on applications-several issues identified in the cloud domain, such as security, load balancing, and cloud deployment. Security is a significant task in cloud computing, and it focuses on using cloud resources such as software, data storage, and managing power utilizing the internet. In cloud computing, organizations use third-party service providers to host their applications and data in data centers distributed across the globe. Load balancing is a crucial aspect of cloud computing that ensures efficient utilization of resources and maximum availability of applications and services. In cloud computing, load balancing refers to distributing network traffic across multiple servers, virtual machines (VMs), or other resources to optimize usage and avoid overloading any single help. This research study introduced a multi-model approach, which can provide the double pack security model for the cloud data stored by the users. An advanced load balancing system is introduced to manage multiple users simultaneously to overcome the cloud server crashing issue and provides better cloud deployment for the cloud applications. All these algorithms provide better cloud services to the clients. The evaluation results show the performance of the proposed models in terms of service availability.
Developing AI-based Fraud Detection Systems for Banking and Finance

Safeguarding financial institutions and their consumers against fraudulent activity makes fraud detection a top priority in the banking and finance business. There has been a rise in the development of artificial intelligence-based fraud detection systems in tandem with the popularity of machine learning methods. This study presents a comprehensive evaluation of modern machine learning approaches like neural networks in comparison to more conventional ones like logistic regression and decision trees. These techniques are tested using financial and banking data from the real world, and the findings indicate that neural networks are superior to more conventional approaches. In addition, our research emphasizes the significance of data gathering and administration in the evolution of fraud detection systems.
Software Systems Using Variability Approaches

A system's ability to adjust and adapt to different events and surroundings is known as variability. Variability is required to increase the product's flexibility and potential for reuse. Researchers have so far examined variation in the software product line, but it is increasingly necessary in software systems as well. In every stage of software engineering, variation is a planned action that is carried out utilizing variant features and variation points. It is now a crucial characteristic of many systems. This study reviews the literature on software system variability. The research investigates variability managing process that is implemented in each phase of software development life cycle. Variability is classified into eight dimensions that describe its types and mechanism. This classification provides an integrated approach of variability in a unifying manner in software systems. From the survey, the implementing techniques on various system software such as service-based systems, web-based systems, and adaptation systems and the effect of variability in each system have been determined.
CSO — VGG-19: Prediction of Cervical Cancer Using Cuckoo Search-Based Deep VGG-19

Nearly four percent of women worldwide have cervical cancer, which can be fatal if not detected and treated early. When contrasted to current numbers, the death rate a few decades ago was unacceptably high. In recent years, clinics have intended to employ advancements in digital image and machine learning to improve cervical cancer screening. Women that test negative have a low risk of developing cancer of the cervical cavity over the following ten years, even if the majority of cervical infections that result in positive testing do not cause precancer. For the prompt identification as well as prompt cure for cervical cancer, it is crucial to identify important genes in cancer, identify healthy people as cervical cancer predictors, and perform an initial evaluation of a high-through expression information. The major objective of the suggested effort is to identify cervical cancer in order to forecast cervical genes. To find the cervical genes, a cuckoo-based deep conversion is created. To some degree, the proposed model predicts the progression of cervical cancer using the VGG-19 model. The deep conversion-based cuckoo search technique can be used to identify cervical cancer patients. The proposed model obtains a 99.30% accuracy rate.
Collective Multi-Trends Sentiment Classifiers for Trending Tweets in Social Media

To train sentiment classifiers, a collective multi-Trends sentiment classification approach is proposed for numerous tweets simultaneously. This technique uses sentiment facts from exceptional tweets to train accurate and potent classifiers. The sentiment classifiers are divided into universal and trend-specific components. The approach automatically recognizes key elements in online customer reviews, diagnoses critical product components based on observations, and administers exams and assessments using language-neutral skills. The global version captures the entire sentiment expertise across multiple tweets. Each Trend's emotional emotions are captured by the Greedy & Dynamic Blocking Algorithms model Naive Bayes and Drimux SVM. For sentiment knowledge, it uses labeled and unlabeled samples, and tweet similarity is taken into account as a regularizer. This study considers two different versions, one based on language and the other on facial expressions. For clarity, green algorithms are shown. The performance of the multi-Trends sentiment class has enhanced.
Review on Real Time Approach to Identify a Person based on Hand drawn Sketch Using Deep Learning

To identify a criminal in law enforcement, facial images collected by forensic sketch artists are used. These sketches play a crucial role in identifying a criminal or missing individual if no pre-existing database is accessible. The existing recognition systems are based on a mugshot database, which comprises images of criminals. This research work presents a novel method based on a real-time technique for identifying a person from a live video using free hand-drawn sketches created using eyewitness information. Deep learning for forensic sketch recognition is an important area of research in computer vision. The deep learning, Convolution Neural Network (CNN) algorithm is used to identify human faces from a live video and extract various features from an actual image and sketch. Siamese neural network is a deep learning model that is trained to compare two images and learn about the similarity metrics between two images to select it. This real-time approach leverages enhanced performance.
Comprehensive Study of Indexing Techniques Used for Extracting CNN Features

With the widespread use of convolutional neural networks (CNNs) for image analysis and retrieval, it has become increasingly important to efficiently index and search the large feature space generated by these networks. This study provides a comprehensive review of various indexing techniques used for extracting CNN features. CNNs are commonly used in deep learning for image classification, object detection, and image retrieval. The main challenge in CNN-based image retrieval is the high-dimensional nature of the CNN features. Indexing techniques can be used to efficiently store and retrieve these features. In this study, different indexing techniques are discussed based on data structure, indexing scheme, feature type, and retrieval approach. This paper also provides an overview of the applications of these techniques, their pros and cons. The study will be useful for researchers and practitioners who want to use CNN features for image retrieval and need to select an appropriate indexing technique for their application.
A Comparative Study of Handwritten Digit Recognition Algorithms

Humans have handwriting styles distinct from human to human. the handwritten text is not always consistent in its size, alignment, or breadth. In order to create a system to comprehend this, it has a computer that can identify and categorise handwritten digit images as ten digits (0 to 9). it is frequently used by academics for a variety of purposes, such as deciphering computerised bank cheque numbers. For this, a variety of deep and machine learning techniques are employed. This study focuses on identifying handwritten digits, namely numbers 0 to 9, using the well known MNIST dataset, which contains 10000 test samples and 60000 training samples. digits are this research presents a comparative review of machine learning technique methods, including convolutional neural network (CNN), decision Tree, logistic regression, and k nearest neighbours (KNN). The dataset is preprocessed and input to the algorithm, and its recall, precision, f1 score, accuracy are determined and compared to test the effectiveness of different algorithms.
A Hybrid Approach to Classify the Multiclass Imbalanced Datasets

The imbalanced nature of a dataset often leads classifiers to show more favor towards recognizing the class with a higher number of data instances. Handling this imbalance in a multiclass environment presents an additional challenge compared to binary class environments. Previous research primarily focused on addressing the imbalance in binary class scenarios, but there is a growing shift towards addressing the imbalance in multiclass environments. In imbalanced environments, data balancing methods are used to address the imbalance issue by equalizing the number of data instances in both the major and minor class groups. This approach aims to avoid bias in learning by modifying the class distribution. Algorithmic-level methods enable the learning model to adjust its learning process towards the class with a lower number of data instances, aiming to improve the prediction rate. The existing multiclass imbalance handling methods either attempted to solve the imbalance problem either by transforming the problem into binary class problems or by approaching the problem as a whole and finds the solution. In both the cases the classification model suffers from the issues like overlapping problem, least recognition of minority class samples, ignoring the minority class samples as outlier and over fitting. This research study proposes a hybrid approach that combines both data level balancing and algorithm level tuning methods. The proposed hybrid approach combines the effort of algorithm level tuning to create a cost associated dataset which avoids the models' biasness towards majority class samples and the data level balancing method to improve the count of the least class samples count and thereby avoiding the ignorance problem and makes the learning model to have pay more attention towards the minority class samples.
An Extensive Review of Blockchain-Enabled Healthcare

An emerging technology called Blockchain has numerous uses in the medical sector. The health domain is the earliest embracer of this technology. But Blockchain's implementation in the health domain is just getting started. This research highlights the ways Blockchain technology may improve medical care. The report summarises medical- related Blockchain technologies across diverse applications and analyses the difficulties and potential for implementing Blockchain in healthcare. This study reports the different obstacles and their concerns with using Blockchain in the health domain while improving the sector's overall performance. This research also focuses to examine and outline Blockchain in healthcare after 2017.
Password Authentication Key Exchange Protocol with Two Servers by IBE in Data Analytics

Communication between two parties needs security, so this study introduces a protocol called PAKE (password-authenticated key extended exchange), here client segments its password into two equal parts that are stored in 2S (two servers). These 2 servers communicate or coordinate with each other without indicating passwords to each other. Two server-based PAKE protocols on the basis of IBE (Identity-Based Encryption), by using this implicit authentication can be achieved. If any server gets compromised by an adversary, the password of the client has to remain secure. Currently, when compared to Katz et al. EPAKE, two-party PAKE and IBE provable safe without random oracles can save computation in each server. Index Term-PAK, IBE, 2Server, Ant optimization
Code Smell Detection using Hybrid Machine Learning Algorithms

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. Software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. The goal of the current research is to create methods for identifying code smells. The machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods.
Analysis of Blockchain Technology for Business Authentication

In today's world, the increasing advancement and digitalization with internet usage and its latest technologies have made almost everything available on the online platform. In almost all the applications, merchants and users must sign up or log in with username and password. Sometimes users save the login details to avoid delays in different applications, which in turn creates major security vulnerabilities for users, merchants, and applications as well. This study analyzes the technology-based implementation of blockchain in business authentication. This study concerns on the improvement of business, especially in authentication using blockchain technology. The analysis has been related to blockchain technology, business authentication, business security, and auditing using blockchain. Furthermore, the discussion has been carried out for various commercial uses of blockchain technology and for achieving security and quality standards. This analysis helps to provide a more secure platform and authentication solutions.
Assessment based on Performance of Harmonics and Asymmetry in Extra High Voltage Systems

This study explains the effect of harmonics and asymmetry in the performance and dependability of extra high voltage (EHV) systems. Electrical problems such as equipment failure and poor power quality may occur from harmonics and distortions. In order to ensure the effective operation of EHV systems, the article deals with the importance of regulations and requirements as well as the necessity of continual maintenance and monitoring. It also focuses on the need for additional study to comprehend the effects of renewable energy sources and cutting-edge technology on EHV systems in addition to developing more accurate diagnostic and mitigation solutions. By figuring out those difficulties, the study ensures the fact that Extra High Voltage systems continue to function accurately and reliably.
Hybrid Model for Vehicle Overload Detection System using Arduino Sensors

With the rapid growth of the market economy, the volume of imports and exports of road transport is increasing. At the same time, people's daily demands have increased the circulation of goods. As a result, truck loads are increasing, and truck accidents are increasing. There are many ways to measure truckload, such as weighbridges, strip sensors, and industrial load sensors, but the accuracy of these techniques is questionable. This study therefore provides the perfect solution for the sole measurement of truck payload. Measurement accuracy is achieved by using load cells with the Arduino UNO microcontroller. This technology is used to prevent vehicle accidents and property damages.
A Survey on Cryptocurrency Price Prediction using Hybrid Approaches of Deep Learning Models

Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods.
A Virtual Assistor for Impaired People by using Gestures and Voice

This study presents a system for virtual mouse control using hand gestures and voice assistant. The proposed system utilizes a camera to detect hand gestures and a microphone to interpret voice commands. The system is designed to provide an alternative to traditional mouse-based interfaces for individuals with mobility impairments or those who prefer a more natural and intuitive interface. User studies serve as a way to assess the system's efficacy, and the results demonstrate the feasibility and usability of the proposed approach. The study also discusses potentialapplications and future directions for the technology, including integration with virtual reality and other emerging technologies. Overall, the proposed virtual mouse using hand gesture and voice assistant system has the potential to improve accessibility and user experience in a variety of contexts.
Improving the Restaurant Recommendation Performance using Sentiment Analysis

Recommendation systems are effective information filtering systems that filter relevant information from a large amount of dynamically generated data and then provide prioritized information to the customers based on their preferences, likes and other factors. In this study, machine learning based restaurant recommendation system is developed and its performance is improved utilizing the reviews given by the customers. The Zomato Bangalore dataset used in this study is downloaded from Kaggle. Initially, reviews of various restaurants are given as input to classifiers namely Multinomial Naive Bayes (MNB) and K - Nearest Neighbor (KNN) to build models that can classify a given review as positive or negative. Then, similarity measures like Pearson correlation coefficient and cosine similarity are used to identify the reviews that are similar. Finally, the top ranked restaurants are then recommended to the user. Experiments were conducted to evaluate the performance of machine learning algorithms and similarity metrics. From the results obtained, it is found that MNB classifier with cosine similarity is able to recommend restaurants with higher accuracy.
Fuzzy WordNet-Based Document Representation and Clustering using Regularized K-Means

These days, sources such as the internet and digital libraries contribute to a mountain of text. Managing textual information in accordance with user needs is becoming increasingly important as textual data continues to grow. Document clustering is a method for organising massive amounts of text data into manageable chunks. To solve the problems of polysemantic ambiguity and synonymousness that plague current methods, this research introduces a robust framework that employs WordNet to guarantee text homology. Once again, Fuzzy WordNet is employed as the basis for the document representation in order to enhance the document clustering performance. K-means, the simplest clustering technique, is used to group similar text texts together. The traditional k-means clustering approach has the drawback of include all variables in the analysis, regardless of whether or not they contribute to the clustering pattern. This is unsatisfactory in high-dimensional cluster evaluation. By adding an adaptive group LASSO penalty term to the cluster vertices, k-means clustering can be regularised. The proposed methodology enhances clustering performance.
Implementation and Visualization of Path Finding Algorithms

Learning computer science is tough for many students, especially the ones who don't have any knowledge about it previously. Just like other subjects, there are a lot of things that needs to be learned in Computer Science. It is often to seen that students find it difficult to cope up with the extreme change in their learning environment. There are many reasons why students struggle in this field. ‘Algorithms’ is one such topic that students find the most confusing. Lecturers on the other hand, also face problems in describing the algorithms to students. Based on several researches, Algorithm Visualization has been to have a positive impact on learning abilities of student. It is already known that a person can learn much more efficiently if it sees what is going on and how is it done. Pathfinding refers to the exploration of an optimal and shortest route, commencing from a designated initial point and concluding at a specified destination. Within the realm of artificial intelligence, pathfinding algorithms are specifically crafted to navigate through graph structures. These algorithms find utility in diverse domains like navigation systems, computer gaming, network architectures, and beyond. Their performance can be degraded by numerous factors such as the length of the path, size of the problem, obstacles and its distribution, data structures used and much more. Whenever a new path-finding algorithm is suggested, its performance is often investigated on the basis of the observations made when that algorithm is used. Proper design and analysis play an important role in giving an explanatory and precise evaluation. By conducting analysis of multiple research works, this study systematically categorized them based on their provisional designs, methodologies, and logical approaches. Throughout this process, this work discovered minor imperfections in these algorithms, which were frequently observed as well.