Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence as a Tool for Enhanced Data Integrity and Data Security

The present era of "big data analytics and management" requires a robust and impenetrable system to deal with the issues of data integrity and security. If data is altered or deleted without sufficient audit trails, a corporation loses its most valuable asset. This has major repercussions on the use of available data and the conclusions drawn from such use. It is commonly said that "data is money" and that such information is crucial to the success of any business. For this reason, it is crucial to ensure that data is stored safely and securely. This is true no matter what sector a firm resides in.Hence, data management requires a solution that can guarantee simultaneously the security and integrity of data, as both are administratively intertwined for enforcement.The present article is an attempt at identifying the various sectors in general where in the tools of Artificial intelligence AI, contributes towards an effective data integrity and security as also on the lacunae and missing aspects that have to be addressed to overcome the hindrances in the use of AI tools.
Design and Implementation of Fast and Secure SSO Authentication for Multi-Application Services Deployed in Cloud

This paper presents the design and implementation of a cloud-based Single Sign-On (SSO) authentication system called D-IAM that supports fast and secure SSO authentication for multi-application hosted on cloud computing. The system consists of three main modules including Identity and single sign-on (SSO) Authentication module, OAuth system, and authorization system. At a core of our proposed scheme, we introduced the secure user identification and SSO authentication method leveraging the secure identification and authentication and OAuth 2.0. We implemented the multi-thread programming to enhance the SSO token generation performance for supporting a high number of user access requests. For the system evaluation, we presented the implementation of our prototype system and conducted the experiments to substantiate the correctness of the functionality and performance of D-IAM system.
Design and Development of Stress Monitoring System in Use Case: Twitter #Dek65

Twitter is a social media platform where users can post, make a conversation, comments, and share experiences that express their emotions and sentiments. Our objective is to monitor and analyze the #Dek65 hashtag’s Twitter messages. We take 166,110 Twitter messages on the #Dek65 hashtag from August 2021 to July 2022 and bring them to analyze attitudes, thoughts, emotions, and stress during the preparation for university entrance exams and the Thai education system. We designed and developed a system by creating a model that can sentiment message, a model for cluster topics from the negative message then represent sentiment messages in a way that is simple to understand through visualization. We do this to make stakeholders can monitor and aware of the problems in the Thai education system.
Facial Emotion Detection for Thai Elderly People using YOLOv7

Currently, many countries around the world are moving towards becoming an aging society. The mental health of the elderly is one of the key challenges in an aging society. In this research, the use of YOLOv7 for facial emotion detection in Thai elderly is examined. In the experiments, the performance of YOLOv7 is compared to Faster R-CNN and SSD. All models are trained and tested with a facial dataset of Thai elderly people. From the experimental result, YOLOv7 achieved the best performance among the compared models with the mean average precision of 0.95 while Faster R-CNN and SSD have the mean average precision of 0.86 and 0.84, respectively.
Blockchain Applications for Security Issues and Challenges in IOT

A common network image (COP) across the numerous applications of contemporary life was made possible by the internet with things (IoT). The development of wireless sensor network devices, which allowed data to be exchanged and numerous analyses to be carried out, allowed for the achievement of the COP. IoT raises security and privacy issues since data authentication and exchange are handled only via the central server. There might be a possibility of device spoofing, incorrect authentication, and decreased data exchange dependability. A central server’s model is dropped and cryptocurrency (BC) technology is added as a component of IoT to solve similar confidentiality and security problems. This study examines how the distributed ledgers based on block chain (DL-BC) technology contributes to potential security and privacy concerns in the context of IoT component interaction. Application of BC with regard to the targeted industries and categories were obviously examined here. To comprehend the role of blockchain technology, certain issues unique to IoT and IoT using BC also were explored.
Quantum Machine Learning and Recent Advancements

Quantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning enhances the performance in multiple ways. This paper studies different aspects of quantum machine learning. It introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area. We look at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc. Finally, we present the literature survey to encourage researchers and academicians.
Design an Improved Model of Software Defect Prediction Model for Web Applications

In the software industry, web applications are crucial and are frequently updated to comply with standards or to include new capabilities. However, even while testing ensures quality, the existence of faults obstructs a smooth development. Defects are caused by a number of variables, many of which must be eliminated at great cost. Determining defects at early stage of the software development process is crucial. Therefore, it is highly desirable to develop such a model that can detect defects in a web application. In this study, the researcher surveyed object-oriented metrics and many Software Defect Prediction (SDP) models for web applications. The author has proposed an architecture for an enhanced model of SDP using machine learning techniques. The proposed model will use supervised and unsupervised learning on object oriented metrices to overcome the problem of class imbalance, cost sensitivity and correlation between Attributes and Faults.
An Enhanced and Efficient approach towards text detection and extracting text in multi-oriented images

Machine learning is a part of artificial intelligence. It allows a machine to learn on its own and earn knowledge and experience based on the input data set and training set. Text detection and extraction is one of the prominent applications of machine learning. It identifies text in an image with varying orientation, style, alignment, brightness, or contrast in images with complex backgrounds and extracts valuable information from them. Hence solving the problem of reading text in images with text having various variations. The goal here is to help people from different places read the text in languages that the person might not even know. This paper focuses on various challenges in text detection and the techniques used in extracting text in multi-oriented images such as image processing, data mining, or neural network techniques, and a comparative study on them. One such technique is OCR (optical character recognition). This technique helps us to detect and extract text data and convert it into data that can hence be edited and searched. This paper additionally focuses on performance comparison of various techniques that are currently in use. The results of the experimental outcome are judged on parameters like recall, precision, and accuracy.
An Assessment of Analysis Techniques

The Supervised Learning in Organizations (PPDM) method, data analysis, mining and data, and subjects related to data mining have all been covered in this page. Due to the enormous quantity of readily viewable info and the urgent need to transform it into useful skills and data, data analysis has grown in popularity both in the data business and the general public in past years. To convert collected data into usable research and wisdom, innovative technology and methods are needed to keep up with the ever-growing data amounts. Some many data analysis techniques are required in the offering of information facilities, including cloud computing and Wifi services, in in order to better comprehend user behaviour, ease congestion, and increase cash flow. To meet this need, information mining strategies will be contrasted from the perspective of a file scientist. The report classifies and compares these data mining methods.
IOT-Based Analysis for Effective Continuous Monitoring Prevent Fraudulent Intrusions in Finance and Banking

The research looks at how important the internet of stuff has become for our youth. It has had significant expansion in the industry, and many people regard it as the next big thing, although they are mostly uninformed of its standards and advantages. There are lots of advantages of the web of things in today's highly competitive and running planet, but it has so many drawbacks as well, making the internet of everything a phenomenon that ought to be cared for in the right hands. Essentially what are web of things or indeed the object we approach as IOT thus are the ones that are happening to just be constructed by the year 2022 to about there may be and around 50to 1.2 trillion online world of things seem to be going to be implemented in our rapidly changing world. As little more than a result, here we will describe how the internet of objects should indeed be managed and tracked in order to effectively monitor the data and make a proper storage and of the information in order to protect them against targeted hackers on banking hoarders and sectors of the economy. This is because our discussion is showing us that there have been daily attacks made on such web of things.
WAFL-GAN: Wireless Ad Hoc Federated Learning for Distributed Generative Adversarial Networks

Diverse images are needed to train Generative Adversarial Network (GAN) with diverse image output, but privacy is a major issue. To protect privacy, federated learning has been proposed, but in conventional federated learning, the parameter server is a third party to the client. We propose WAFL-GAN, which does not require a third party, and which assumes that each node participating in the learning process is mobile and can communicate wirelessly with each other. Each node is trained only with the data it has locally, and when nodes opportunistically contact each other, they exchange and aggregate model parameters without exchanging raw data. This allows all nodes to eventually have a general model and produce a general output, even if each node has a dataset with a Non-IID distribution. We evaluated WAFL-GAN on the Non-IID MNIST dataset and quantitatively showed that the output diversity of WAFL-GAN can be as high as that of conventional federated learning.
Analysis of BERT Email Spam Classifier Against Adversarial Attacks

Email is still a preferred method of one-to-one communication among customers, businesses, and employees. As the usage of emails is increasing daily, the number of spam emails is also increasing. Many natural language models are developed to avoid the problem of spam emails. However, customers continue to report an increase in scams and attacks carried out through spam emails. Even though numerous spam filters have been suggested for identifying spam, they are weak and may be fooled by some expertly created adversarial samples. In this paper, we have taken the BERT classifier which has the best performance among other classifiers, and analyzed its performance when we inserted the adversarial samples. We have taken eight types of different adversarial attacks and analyzed the performance of the BERT Model. This analysis will be helpful in further studying defense methods against these attacks.
Blockchain effect on E-commerce: A framework for key research areas

Several business applications and procedures are expected to be significantly disrupted by Blockchain-based technologies, which has significant consequences for ecommerce. In light of the potential blockchain based technology are popularly known as "trust building systems" that can be used in multiple business models and standard guidelines which evolved placed above a white period to achieve believe, serviceability, and enforcement in various domains such as cunsumers related business, business related connections have to be questioned and possibly adjusted. By enabling trustless trade interactions that work without specialized intermediaries or even central authority in the case of permission lessblock chains, Blockchain has the power to change how electronic commerce operates. Furthermore, giving universal access to immutable data across the whole supply chain may significantly alter the information and value exchange between businesses and consumers. The methodology presented in this paper are intended to motivate further study of the potential effects of Blockchain on e-commerce. Technology, law, organizational and quality challenges, as well as consumer difficulties, are the four key categories. This paper demonstrates how Blockchain may affect many Electronic commercial components in these relevant fields.
Assessing the robustness of multi-criterial recommendation systems

Being able to comprehensively understand the intri- cacies of recommendation systems can be a significant competitive advantage. No surprise, then, that marketers are increasingly preferring robust and cost-effective multi-criteria recommenda- tion systems in this age of personalisation and lifetime value maximisation. Multi criterion recommendation systems as a methodology is already fairly well analysed. Yet, as selection criterion complexity increases - it becomes an exponentially more difficult task to check the robustness of such a system. The authors try to evaluate the strength of these systems as a part of this research. In this paper, we empirically compare two different mechanisms which measure similarities and ratings to evaluate their rating estimation performance. The results achieved can be considered one step closer to the understanding of how multidimensional evaluation can be used to assess underlying value of multi-criteria ratings.
Node-Aware Dynamic Weighting Methods for Federated Learning on the Blockchain to Improve Performance

In contrast to the traditional centralized learning approach, federated learning (FL) is a decentralized learning strategy. Every device’s local learning progress when the FL interacts with the central server, progressively improving the learning model. Nonetheless, it may cause network congestion because of limited transmission capacity and the participation of a large number of users. The model’s ability to converge quickly, steadily, and with target learning accuracy is one method for reducing the network load. We suggest a scenario for federated learning using blockchain in this study. Every user who participates in learning may be distinguished as a “node” via blockchain, which effectively encourages user participation. Integrity, stability, and other goals can also be pursued. When choosing between the two sorts of weights, the consumer in charge of refreshing the global representation should be consulted. When determining how much each customer matters, we first factor on how well they've learned locally.. Second, we take each client’s involvement frequency into consideration when determining the weight. To compare the effectiveness of our suggested system with that of other schemes, we select two critical performance indicators: learning rate and standard deviation. The simulation results demonstrate that, in comparison to existing schemes, our suggested system delivers greater stability and quick convergence time for the desired accuracy.
For Smartphone Occur under Normal Conditions with Data Protection, Distributed Edge Knowledge and Cryptocurrencies Should be Combined

The mobility of a large number of workers is essential if several types of sensing tasks are to be completed with increased adaptability and at reduced expense. However, in order to make use of it, the conventional MCS requires an excessive amount of computer power and data storage space. This is because the traditional MCS has a high resource consumption rate for communication during the transmission of raw data. A revolutionary MCS learning framework that includes four main components is presented by us as an alternative. This structure takes use of There have been a number of studies conducted on blockchain-based MCS and FL; however, these studies have not been able to solve the fundamental problems with MCS in terms of or address the privacy issues that have been brought up by requestors and workers who are participating in the learning process but have limited resources. To ensure that the information stored on smart phones is kept confidential, our company suggests implementing a data submission policy that is based on the structure of the underlying mechanism.
A Trustful Payment System for Crowdfunding using Blockchain

Ideas lead to innovation which benefits the society. Innovation is a way of advancing, evolving, and improving our way of living. It is also a method of growing our economy. But to bring these ideas into reality, a huge capital investment is required. Sometimes, the person himself is capable of funding the project. Other times, when he’s not capable of funding his project, he may resort to other methods of raising money. One of these successful methods include crowd funding. Crowdfunding refers to the use of small amounts of capital from a large number of individuals to finance a new business venture. In exchange for their investment, the contributors get certain rewards. These rewards may include profits or some other stuff.Crowdfunding can be done through various platforms like social media. There are also some specific websites that focus on the idea of crowdfunding. These crowdfunding websites include crowdfunding platforms like Kickstarter, GoFundMe, Experiment, Fundly. But with the rising popularity of these investment based crowdfunding websites, there is also a rise in the cases of scam via these websites where the people don’t get any reward in exchange for their investment.Another issue with the traditional payment systems is that noticeable transaction fee is levied on payment via these payment methods. Our main goal is to create an intermediate network between the communities and the project idea for the scam-proof arrangement. The issue with the transaction fee would also be resolved to some extent. We proposed to do this by using Ethereum Smart Contracts-Blockchain instead of the traditional payment methods. This would result in considerable decrease in the scams and make it a trustworthy payment system and would attract more audience.
Automated system to detect social distancing among humans during covid pandemic situation

Maintaining the purported Social Separating is one of the essential and greatest ways to stop the new popular episode. Legislators are enacting restrictions on the standard of private distance between people in order to concur with this restriction. In light of this real-life occurrence, it is crucial to evaluate how consistent with realistic imperatives in our lives this is, in order to ascertain the causes of any prospective cracks in such distance obstacles and determine whether this portends an anticipated risk. In order to do this, we offer the Visual Social Removing (VSD) problem, which is defined as the automatic evaluation of the difference between the depiction of connected person aggregations and the private separation from an image.When this requirement is violated, it is vital for VSD to conduct painless research to determine whether people agree to the social distance restriction and to provide assessments of the degree of wellbeing of particular places. We first draw attention to the fact that measuring VSD involves more than simply math; it also suggests a deeper comprehension of the social behavior in the setting. The goal is to genuinely identify potentially dangerous circumstances while avoiding false alerts (such as a family with children or other family members, an elderly person with their guardians), all while adhering to current security protocols. Then, at that point, we discuss how VSD links to earlier research in social sign handling and demonstrate how to investigate fresh PC vision techniques that might be able to address this issue. Future issues about the viability of VSD systems, ethical repercussions, and potential application scenarios are the result.
Presentation of futuristic Malarial Disease through a Hybrid Model of A.I. and Big data

Transmission of parasites of female Anopheles mosquito is a major cause for the fatal disease like Malaria and most common symptoms are high fever, headache, abdominal pain, muscle pain, vomiting, diarrhea, Anemia, etc.Healthcare is a field where Forecasting is most beneficial and helpful for the cure of diseases and it can be possible only by using models of Neural Networks, Regression, and LSTM for predicting that how many confirmed cases will occur, how many people will get recovered and among them how many get dead cause of any disease based on past and present data for forecasting the future trend of these cases. Even though forecasting can be done by traditional methods but traditional methods are time consuming. The machine learning techniques are fast and accurate then also for adding efficiency and accuracy to these methods and techniques, Artificial Intelligence takes place in the field of Data Sciences. Artificial Intelligence came into the picture 100 years ago and shows remarkable growth in every field in the past few years. There are a lot of models proposed for different types of diseases in the field of healthcare. The visible patterns of symptoms and cases related to the disease can help in the medical field to prevent and cure it. This work is going to deal with the applications of Artificial Intelligence in Data Sciences which is to make an early reliable prediction of Malarial disease using Neural Networks, Regression, and LSTM model. It Compares the trends of different models and comparative results will demonstrate that the machine learning techniques practiced to forecast the malarial disease along with visible patterns gives information related to the patient’s data.
Analysis of Performance Metrics for Classification of Punjabi Poetry using Machine Learning Techniques

The poetic text has always been a challenge from the computational linguistics perspective. Literature classification has always been a tedious task in itself owing to so much diversity we have. We have so much information associated with a poem that it can be classified on basis of its writing schemes, on basis of poets, on the grounds of emotions, on basis of an era, etc. In this work, we developed a dataset of 401 poems in total and they were classified into four categories related to different genres. Considering the lexical features of poems, we performed several pre-processing stages to segregate the useful information in form of tokens from poems. These tokens were modeled using Latent Dirichlet Allocation and evaluated using the Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF- IDF) weighting techniques. SVM, Random Forest, Naive Bayes, and Logistic Regression experimented with lexical features in the poem and it was found that SVM proved to be the best- performing algorithm with the highest accuracy of 82.17%.