Reviewerly/exHarmony huggingface dataset:
Advancing Data Privacy: A Novel K-Anonymity Algorithm with Dissimilarity Tree-Based Clustering and Minimal Information Loss

Anonymization serves as a crucial privacy protection technique employed across various technology domains, including cloud storage, machine learning, data mining and big data to safeguard sensitive information from unauthorized third-party access. As the significance and volume of data grow exponentially, comprehensive data protection against all threats is of utmost importance. The main objective of this paper is to provide a brief summary of techniques for data anonymization and differential privacy.A new k-anonymity method, which deviates from conventional k-anonymity approaches, is proposed by us to address privacy protection concerns. Our paper presents a new algorithm designed to achieve k-anonymity through more efficient clustering. The processing of data by most clustering algorithms requires substantial computation. However, by identifying initial centers that align with the data structure, a superior cluster arrangement can be obtained.Our study presents a Dissimilarity Tree-based strategy for selecting optimal starting centroids and generating more accurate clusters with reduced computing time and Normalised Certainty Penalty (NCP). This method also has the added benefit of reducing the Normalised Certainty Penalty (NCP). When compared to other methods, the graphical performance analysis shows that this one reduces the amount of overall information lost in the dataset being anonymized by around 20% on average. In addition, the method that we have designed is capable of properly handling both numerical and category characteristics.
Security Aware Virtual Machine Allocation Policy to Improve QoS

Cloud service providers find managing the energy consumption for datacentres as a critical operation. Significant energy is being used by a rising spike in the number of data centres. To overcome this challenge datacentres, attempt to reduce the number of active physical servers by carrying out virtual machine consolidation process. However, due to inadequate security measures to verify hostile cloud users, the security threats on cloud multitenancy platform have escalated. In this paper we propose energy efficient virtual machine consolidation using priority-based security aware virtual machine allocation policy to improve datacentre security. The proposed security solution considers the host threat score before virtual machine placement, which has reduced the security threats for co-residency attacks without impacting datacentre energy consumption.
An Enhanced Security Model for Protecting Data Transmission and Communication in Recent IoT Integrated Healthcare Industry Using Machine Learning Algorithm

Different kinds of security need to be applied to various application-centric IoT networks. Safety is one of the most important aspects to be considered regarding user, device, and data. The healthcare industry is a special IoT network fully connected with medical/healthcare IoT devices. The data generated from the IoT devices are transmitted or shared from one hospital to another through the Internet. Healthcare data has more private, medical, and insurance information that intruders can use on the Internet. The intruders misbehave with the patient or the general public registered in the healthcare industry. Some intruders blackmail the patient based on their private/personal information. Healthcare industries and their research team are trying to create a security framework to safeguard the data to avoid these malicious activities. This paper aims to secure and analyze healthcare IoT data using the Support Vector Machine algorithm. It learns the entire dataset, classifies it, and calls the encryption-decryption algorithms (RSA) to secure private data. The proposed SVM and the RSA algorithm are implemented in Python, and the results are verified. The performance of the proposed SVM-RSA is evaluated by comparing its results with the other algorithms.
A Machine Learning Model to Identify Fake Data from Social Media using Sentiment Features

The exponential growth in the use of social media is leading to sharing of information among each other through which the spreading of fake news is common these days. online social networking is the main source for fake news. The most popular social media are Twitter and Facebook, through which the majority of the news reaches the public. This study is aim to try different classification algorithms in comparing with Dataset. For our experiment purpose the dataset used is Real or Fake News dataset which is extracted from Kaggle, which comprises 30Mb of twitter data. The two major classification algorithms used are Naive Bayes and Logistic Regression classification algorithm. The algorithms result in Accuracy score 82.48%, AUC 1.0 and kappa score 0.64 and Accuracy score 91.16%, AUC 0.91 and kappa score 0.82 respectively for the given dataset. The two different classification algorithms are successfully checked with the given dataset. The sentimental analysis is the other way of identification of fake data problem which can be implemented to know the positive and negative sentiment in the given twits. VADER feature is the one of the feature extraction which can be tried with the dataset to find out fake and real data.
Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled is nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used in conjunction with computational, mathematical, and statistical modeling methodologies to simulate technical trading. Fuzzy logic may be modeled using linear, nonlinear, geometric, dynamic, and integer programming. These techniques, when combined with fuzzy logic, help the decision-maker arrive at a better solution while still facing some degree of ambiguity or uncertainty. The moving average method is a useful metric that may give trade recommendations to aid investors further. While trading signals inform investors of when to purchase and sell, a simple moving average provides no such information. In this research, we suggest a fuzzy moving average approach in which the intensity of trading signals, measured in terms of trading volume, is determined by using the fuzzy logic rule. In this research, we propose using fuzzy logic technical trading rules, which are more resistant to decision-making mistakes, to mitigate the trading uncertainty inherent in the conventional technical indicators method.
Novel Frame Work for Blockchain Based Votingapplication Using Ethereum Virtual Machine

Blockchain fills in as a scattered record development which licenses propelled assets for be executed in decentralized framework which works in a common manner. In bound together systems everything depends upon the central structure and the outcomes of throwing a voting form system were not exact considering the way that anyone can change. These kinds of results are wrong and not trustworthy by the people who are voting. The Voting machines that are accessible as of now rely upon the servers which are centralized. Here the people who are voting have to keep belief on the concentrated individuals for the accurate results. So, the contemplating decentralized law-based systems that can settle on the political choice procedure very snappy and easy. These scattered systems are the most praised advancement improvement in this present genuine world. Here Blockchain advancement has various wide extent of usages starting and beginning from distributing statistics, economics etc. Here the blockchain advancement is used as a help of achieve this just assembled application that depends concerning a decentralized appropriated application. Here, the structure works in the way as follows, the transactions or understandings whichever executed are changed over into machine reasonable method of reasoning that engages and ensures understanding among various people who are in the organization, and in like manner who has the solidarity to support their individual money exchanges, different activities and opposite party challenges that are associated with their checking and observing. Smart understandings will be enabled and they are to be passed on into a blockchain space or stage, the amount of possible use cases for this development will be checked and improves amazingly. The most critical use of this blockchain is that to empty the prerequisite for pariahs in the two individuals when all is said in done and the private parts, to end up being progressively capable and effective.
Dog Breed Identification using ResNet Model

As dogs are domestic animals due to the many numbers of dog breeds available around the world. It’s hard to find out the exact dog breed name for a common person. There are many techniques available to identify dog breed. But the proposed work introduced the new technique called RESNET which is the part of CNN to classify dog. RESNET is used to identify images. It helps to perform different tasks on larger datasets. Identification of different dogs is one of the important applications of Convolutional Neural networks. Since the identification of dog breeds is very difficult because they spread in a large number and it makes very hard for a person to identify or classify dogs. With the help of Keras and TensorFlow, a dataset is created, tested, and trained for the detection of dog breeds by using RESNET. Around 120 different dog breeds are present in the dataset which consist of 20600 images of dogs. From this paper, load these images and convert them into a NumPy array and normalize them. Then,100 epochs were used with a batch size of 128 to achieve the best accuracy. The model is saved for further process to create a web application to identify the dog.
An Efficient Probabilistic Deep Learning Model for the Oral Proficiency Assessment of Student Speech Recognition and Classification

Natural Language Processing is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. Speech recognition systems utilize machine learning algorithms and statistical models to analyze acoustic features of speech, such as pitch, duration, and frequency, to convert spoken words into written text. The Student English Oral Proficiency Assessment and Feedback System provides students with a comprehensive evaluation of their spoken English skills and offers tailored feedback to help them improve. It can be used in language learning institutions, universities, or online platforms to support language education and enhance oral communication abilities. In this paper constructed a framework stated as Latent Dirichlet Integrated Deep Learning (LDiDL) for the assessment of student English proficiency assessment. The system begins by collecting a comprehensive dataset of spoken English samples, encompassing various proficiency levels. Relevant features are extracted from the samples, including acoustic characteristics and linguistic attributes. Leveraging Latent Dirichlet Allocation (LDA), the system uncovers latent topics within the data, enabling a deeper understanding of the underlying themes present in the spoken English. To further enhance the analysis, a deep learning model is developed, integrating the LDA topics with the extracted features. This model is trained using appropriate techniques and evaluated using performance metrics. Utilizing the predictions made by the model, the system generates personalized feedback for each student, focusing on areas of improvement such as vocabulary, grammar, fluency, and pronunciation. Simulation mode uses the native English speech audio for the LDiDL training and classification. The experimental analysis stated that the proposed LDiDL model achieves an accuracy of 99% for the assessment of English Proficiency.
Quality Enhancement for Underwater Images using Various Image Processing Techniques: A Survey

Underwater images are essential to identify the activity of underwater objects. It played a vital role to explore and utilizing aquatic resources. The underwater images have features such as low contrast, different noises, and object imbalance due to lack of light intensity. CNN-based in-deep learning approaches have improved underwater low-resolution photos during the last decade. Nevertheless, still, those techniques have some problems, such as high MSE, PSNT and high SSIM error rate. They solve the problem using different experimental analyses; various methods are studied that effectively treat different underwater image distorted scenes and improve contrast and color deviation compared to other algorithms. In terms of the color richness of the resulting images and the execution time, there are still deficiencies with the latest algorithm. In future work, the structure of our algorithm will be further adjusted to shorten the execution time, and optimization of the color compensation method under different color deviations will also be the focus of future research. With the wide application of underwater vision in different scientific research fields, underwater image enhancement can play an increasingly significant role in the process of image processing in underwater research and underwater archaeology. Most of the target images of the current algorithms are shallow water images. When the artificial light source is added to deep water images, the raw images will face more diverse noises, and image enhancement will face more challenges. As a result, this study investigates the numerous existing systems used for quality enhancement of underwater mages using various image processing techniques. We find various gaps and challenges of current systems and build the enhancement of this research for future improvement. Aa a result of this overview is to define the future problem statement to enhance this research and overcome the challenges faced by previous researchers. On other hand also improve the accuracy in terms of reducing MSE and enhancing PSNR etc.
Twitter Sentiment Analysis on Meta: A Lexicon-Based Analysis using Rapidminer

Meta is a new parent company of Facebook, Instagram, WhatsApp, and more as the tech giant tried to move on from its scandal-plagued social network to its metaverse vision. Meta-trended on Twitter as users worldwide tweeted their opinions about the rebrand. The rebranding of Meta has become a popular topic of debate. Social media users, companies, and investors desire to be a part of the next big thing. In this study, lexicon-based polarity detection automatically classifies data as positive, negative, and neutral. This study will investigate the main sentiments towards Meta and the topic social media users discussed Meta. In this study, 2997 tweets from the 9th to the 10th of November 2021 were scraped from Twitter using RapidMiner. The findings showed that 36% had a positive sentiment, 29% had a negative sentiment, and 35% were neutral about the Mete rebranding announcement. In addition, the topics often tweeted include NFT and stock, reaction, Metaverse, partnership, AR and VR, and policy. This study can help the Meta understand the people's multi-faceted views towards the Meta rebranding.
Wrong Way Vehicle Detection in Single and Double Lane

Wrong-way driving is one of the primary causes of traffic jams and accidents globally. It is possible to identify vehicles going the wrong direction, which lessens accidents and traffic congestion. Surveillance footage has become an important source of data due to the accessibility of less priced cameras and the expanding use of real-time traffic management systems. In this paper, we propose a technique for automatically identifying automobiles moving against traffic. Our system uses the You Only Look Once (CNN) algorithm to recognize and track vehicles from video inputs and the centroid tracking method to determine each vehicle's orientation inside a given region of interest (ROI) in order to identify vehicles traveling in the wrong direction. It functions in three steps. The Deep sort tracking method is particularly good in detecting and tracking objects, and the centroid tracking technique can effectively monitor the direction of travel. Experiments with a variety of traffic films show that the suggested method can detect and identify wrong-way moving vehicles in a variety of lighting and weather scenarios. The interface of the system is quite simple and easy to use.
Optimal and Efficient Searchable Encryption with Single Trapdoor for Multi-Owner Data Sharing in Federated Cloud Computing

Cloud computing, an Internet based computing model, has changed the way of data owners store and manage data. In such environment, data sharing is very important with more efficient data access control. Issuing an aggregate key to users on data enables and authorizes them to search for data of select encrypted files using trapdoor or encrypted keyword. The existing schemes defined for this purpose do have certain limitations. For instance, Cui et al. scheme is elegant but lacks in flexibility in access control in presence of multiple data owners sharing data to users. Its single trapdoor approach needs transformation into individual trapdoors to access data of specific data owner. Moreover, the existing schemes including that of Cui et al. does not support federated cloud. In this paper we proposed an efficient key aggregate searchable encryption scheme which enables multiple featuressuch as support for truly single aggregate key to access data of many data owners, federated cloud support,query privacy, controlled search process and security against cross-pairing attack. It has algorithms for setup, keygen, encrypt, extract, aggregate, trapdoor, test and federator. In multi-user setting it is designed to serve data owners and users with secure data sharing through key aggregate searchable encryption The proposed scheme supports federated cloud. Experimental results revealed that the proposed scheme is provably secure withrelatively less computational overhead and time complexity when compared with the state of the art.
Performance Analysis of Microservices Behavior in Cloud vs Containerized Domain based on CPU Utilization

Enterprise application development is rapidly moving towards a microservices-based approach. Microservices development makes application deployment more reliable and responsive based on their architecture and the way of deployment. Still, the performance of microservices is different in all environments based on resources provided by the respective cloud and services provided in the backend such as auto-scaling, load balancer, and multiple monitoring parameters. So, it is strenuous to identify Scaling and monitoring of microservice-based applications are quick as compared to monolithic applications [1]. In this paper, we deployed microservice applications in cloud and containerized environments to analyze their CPU utilization over multiple network input requests. Monolithic applications are tightly coupled while microservices applications are loosely coupled which help the API gateway to easily interact with each service module. With reference to monitoring parameters, CPU utilization is 23 percent in cloud environment. Additionally, we deployed the equivalent microservice in a containerized environment with extended resources to minimize CPU utilization to 17 percent. Furthermore, we have shown the performance of the application with “Network IN” and “Network Out” requests.
A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations.
Class Imbalance Reduction and Centroid based Relevant Project Selection for Cross Project Defect Prediction

Cross-Project Defect Prediction (CPDP) is the process of predicting defects in a target project using information from other projects. This can assist developers in prioritizing their testing efforts and finding flaws. Transfer Learning (TL) has been frequently used at CPDP to improve prediction performance by reducing the disparity in data distribution between the source and target projects. Software Defect Prediction (SDP) is a common study topic in software engineering that plays a critical role in software quality assurance. To address the cross-project class imbalance problem, Centroid-based PF-SMOTE for Imbalanced data is used. In this paper, we used a Centroid-based PF-SMOTE to balance the datasets and Centroid based relevant data selection for Cross Project Defect Prediction. These methods use the mean of all attributes in a dataset and calculating the difference between mean of all datasets. For experimentation, the open source software defect datasets namely, AEEM, Re-Link, and NASA, are considered.