Reviewerly/exHarmony huggingface dataset:
Automation of Data Transmission for Wallet With Novel Merchant Payment System Using Cluster Classification Over Sequence Analysis Method

To improvise a data transmission for wallet with merchant payment system using cluster classification over sequence analysis method is proposed in this paper. The implementation have been carried out for processing the data through MySQL and the algorithms are tested with Net Beans (IDE) applications. The cluster classification paved the way to achieve the enhancement of data transmission of data. With the sample size of 46 and tested around 11 number of times compared with the sequence analysis method. The speed with the accuracy of 92% for data transmission for wallets with merchant payment systems using cluster classification, provides significantly better results compared to sequence analysis methods. There was a statistical significance between cluster classification and sequence analysis method.To improvise a data transmission for a wallet with a merchant payment system using cluster classification over sequence analysis method provides more significant accuracy of 94.4 percent than sequence analysis method by analyzing the parameters of time with data and received time.
Blockchain Applications Beyond Cryptocurrency: a Focus on Decentralized Markets and Contract Management

Ever since the introduction of Bitcoin, the first and largest cryptocurrency, blockchain technology has successfully made a name for itself as the foundation of digital currencies. It wasn't just used to decentralize money and payments. It is also used to decentralize markets more generally. All types of contracts can be registered, confirmed and transmitted to other network participants via a distributed ledger powered by blockchain technology. This research offers a thorough review of cutting-edge blockchain-related applications that have been written about in the literature. Based on their contributions to the pool of knowledge around blockchain, several published books have been thoughtfully included. We will discuss and go through some ideas in the paper's final portion.
An investigation of the effects of the global epidemic on Crypto Currency returns and volatility

In order to establish sustainable operations, Internet innovation has been widely adopted in the retail sector; however, certain operational difficulties, such as security and confidentiality have been uncovered in practice. As a result, linked fields must do more thorough theoretical study. In order to compete with online merchants and promote sustainable growth, this study examines how traditional brick and mortar stores might use IoT technology. First, by taking into account the costs of customer inconvenience associated with shopping through various retail partners and the influence of Technology, pricing Plans were created for the store and the retailer of concrete blocks, respectively. Furthermore, by contrasting different scenarios, the benefits of internet to retail shops and consumer excess are analysed. After that, the basic models were expanded to take into account consumer - perceived sensitivity regarding IoT technology and price competitiveness in order to test the robustness of the analytical conclusions. Additionally, actual data-based numerical analyses are performed. Smart metres, electric vehicles, rooftop solar photovoltaic panels, smart air conditioners, electric heating systems, and architectural usage of green protons and methyl in manufacturing applications, as well as control systems are all examples of how this decarbonization plan is now being implemented. This paper includes the details analysis of the adoption of this analytics software in the electricity system and associated connected sectors can have a favourable influence on numerous of the 17 sustainability agenda listed by the Unesco Institute of Economic Affairs.
Analysis Of Software Defect Prediction Using Machine-Learning Techniques

The development of the group based on the machine learning process is being generated with the support of software analysis. Development of the ideas is being generated and proper predictions about the research topic have been determined. Moreover, improving the problem-solving capacity is being generated and it helps to analyze the hybridized approaches. Analytical data is defined in this portion. Moreover, the prediction of the machine learning capacity is highlighted and it tries to focus on the software-based system. On the other hand, the input of the attribution is highlighted and it helps to improve the significance value of the research topic. Assisting activities have to be generated and the growth of the organization has to be highlighted.
The ANN Method for Better Living's Method of using Artificial Neural Networks to Predict Heart Attacks Caused by Anxiety Disorders

A subset of mental diseases known as anxiety disorders is characterised by intense sensations of fear and worry. In recent years, the development of machine learning techniques has substantially aided the creation of tools. helping doctors anticipate mental problems and providing patient support care. A comparative literature search was done for this paper. based on studies on the prognosis of particular forms of anxiety disorders, utilizing methods of machine learning. The analysis of sixteen (16) papers showed that machine learning techniques can be used to predict anxiety disorders well. Depending on the type of anxiety disorder, the results' accuracy varies and the methodology used to forecast the problem. We can infer that the prediction has undergone significant research utilizing machine learning approaches, of anxiety.Heart attacks and mental illnesses are now recognized as two major physical problems. This research highlights the significance using deep learning and artificial neural systems in correctly forecasting these major medical issues. This research article discusses the need of using ai - based strategies to lessen diagnosing mistakes. Here is a quick summary of how AI technology has advanced in the field of medical diagnostics. This research article goes into detail on the numerous applications of AI, including computer vision and convolutional neural networks, and how these technologies are used to analyse and comprehend cardiac diseases. In order to understand more about the way individuals see the world, qualitative research has also been conducted. This research article goes into detail just on numerous applications of AI, including computer vision and convolutional neural networks, and describes how these technologies are used to analyse and comprehend cardiac diseases. Primary research has also been done to understand more about the ways humans see things. Heart attacks and anxiety symptoms are now recognised as two major physical issues. This research highlights the significance of computer vision and synthetic neural networks in correctly forecasting these major health problems.
AI-Powered Cybersecurity: Identifying Threats in Digital Banking

The rise of artificial intelligence (AI) has led to significant changes in how businesses operate, particularly cybersecurity. The increasing reliance on technology for business processes and data storage has made companies vulnerable to cyberattacks, making it crucial for businesses to incorporate AI-powered cybersecurity systems to safeguard their operations. However, despite the numerous benefits of using AI in cybersecurity, there are still concerns surrounding the technology's safety and effectiveness. This study explores the role of AI in identifying threats in digital banking and highlights the benefits of using AI-powered cybersecurity to support business efficiency. The study also aims to raise awareness about the need to overcome the fear of experimenting with new technologies and to show how examples of Malta’s risk management study drawing on the experiences of businesses that have implemented AI-powered cybersecurity systems in Malta, highlighting the success of such systems in safeguarding business operations. The study also examines the challenges of implementing AI-powered cybersecurity and offers recommendations for overcoming these challenges. The study concludes by emphasizing the need for businesses to embrace AI-powered cybersecurity systems to mitigate cyber risks and enhance their overall security posture. The findings of this study contribute to the body of knowledge on the role of AI in cybersecurity and provide insights for businesses looking to implement AI-powered cybersecurity systems.
Deep Learning Face Characteristics in the Wilderness

Because of the backdrop clutter and facial variations, it can be difficult to predict face attributes (FA) from web photos. For the purpose of predicting facial attribute in the wild, a novel deep learning (DP) framework (FW) is developed. For face localization (FL) and attribution predictions, correspondingly, it cascading two CNNs (LNet and ANet). With feature labeling, but now with PT in a different manner, those networks are developed in a cascades fashion. While ANet is pre-trained (PT) with enormous face IDs, LNet is trained and tested with huge broad object classes. This FW not just to works significantly better than current best practices, but also discloses a number of important details about learning face(LF) representations, as seen below.(1) It demonstrates the various pre-training (PT) techniques that can be used to enhance LNet and ANet. (2) It shows that, despite the fact that LNet filtration are adjusted by characteristic labeling, their reaction mapping over the image sequence have a significant indicator of the position of faces. Due to this aspect, LNet may be trained for FL using simply feature tags and not face coordinates. The cascading of LNet and ANet can locate features and identify properties in photos of any size in instantaneously using a revolutionary rapid feed-forward method. (3) It also shows that, upon supervised learning, the rising hidden units of an ANet automatically identify semantic ideas, which are then markedly enriched. A sparse continuous mixture of these notions can adequately express each property. By examining such combinations, characteristics reveal distinct grouping patterns that can be meaningfully interpreted.
Django: Developing web using Python

Due to the COVID-19 pandemic situation digitalization gets more popularity. Every sector including education, medical, business, agriculture focuses on the digitalization that results the use of web development. There is the use of various technologies for the web development. Django and flask are the popular python based framework that redefine the meaning of web development. Due to the rapid development feature, it now demanding in the market. It repeals the demand of other web framework. It is in the demand for the people who want to understand how to develop an elegant website with minimal fuss. Django and flask helps to reduce the repetition of task to make web development code less complex. In this paper the Django and flask web framework with its working at frontend and backend is designed for the development of shorten the URL is discussed.
Empirical Method to Analysis Mobile Payment Authentication Enhancement using Blockchain

One of the fundamental IoT technologies, near-fleld communication (NFC), has made mobile payments possible for a variety of applications. As the design of payment systems has been transformed by crypto currencies like Bitcoin, the technology has grown in popularity. However, this system is vulnerable to security issues including man-in-the-middle attacks, double-spending, and replay attacks, necessitating the use of other technologies like blockchain. The necessity to implement blockchain-based crypto currency payment is driven by worries about the security and privacy of transactions made utilising NFC technology. For example, NFC payment has come under fire for the lack of safeguards against potential assaults like brute force or double-spending. As a result, adopting blockchain technology is anticipated to enhance both user experience and the security characteristics of the NFC mobile payment system. Because it allows for direct transactions without using a third party, blockchain technology has been commended for facilitating equitable payment. In order to facilitate value transfer utilising IoT devices, blockchain cryptocurrency integration will transform the NFC payment method. In order to solve security and privacy issues, it is important to combine NFC with blockchain technology and cryptocurrencies. The goal of this research is to examine the possibility for integrating cryptocurrencies like Bitcoin and blockchain technology into the NFC payment mechanism.
Impact Of Using AI On Supply Chain Operation Using Blockchain Technology

Investigating the effects of using AI and block chain technology inside the area of supply chain management has helped to build the related research work (SCM). The study includes a summary of the function that the relevant technologies play in transforming various SCM elements. Using the relevant technologies that have been described here, the problems in the area of SCM can be resolved. In this study, the advantages of using this technology have already been described in great depth. Blockchain's public ledger technology, which tracks every cargo module's movement, can help logistical businesses operate more effectively. The data may be used by businesses to build fast delivery routes and eliminate pointless procedures. With payment systems, it also lessens clerical mistakes and inefficiencies. Clerical related logistical mistakes can be decreased through decentralised and distributed ledgers. There are no requirements for law firms, brokerages, or other logistical entities when using merchants and cryptographic protocols.
Publicly Verifiable Attribute-Based Encryption and Distributed Decryption

A useful crypto method for access control and precise delivery of encrypted information is attribute-based encryption (ABE). A fundamental issue with ABE, meanwhile, is because the analytical expense rises exponentially with the intricacy of the access structure. Exporting of computation in a secure manner is a potential solution to the problem. T hedecompiler may contract out the bulk of the decoding costs while still protecting the privacy of the information. Current ABE with contracted decryption cannot provide public authenticity or outsource the deciphering of preexisting ABE decryption. For every ABE scheme, we must create a special outsourced decryption in order to address the first flaw. The second prevents the use of ABE (with external decryption) in a hostile environment. We provide a broad strategy for changing any ABE.To overcome the aforementioned two concerns, we provide a universal method for converting any ABE strategy (premised on pairs) as just an ABE strategy publically verified contracted decryption. Under the suppositions because the fundamental economic ABE system is safe as well as other person performing the contracted Unlocking is analytical, our method is secure.
An Integration Of Wireless Communications And Artificial Intelligence For Autonomous Vehicles

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links.
Improved Algorithm for Solar Power Systems for Efficient Tracking System

Current evaluation of a pilot concentrated solar energy plant found that the monitoring technology was unable to precisely align the complex heliostats with the sun and direct the sunlight onto the goal. The inability of concentrating solar energy monitoring systems to both increase precision & lower operating costs was shown by a subsequent examination of solar tracking devices. A computers vision solar tracking systems is presented and put into practice in this work. It consists of vision controllers, camera, light sensors, Arduino microprocessor, two stepping motor, sticks, and a transparency plate. The light sensors determine whether sunlight is present, the webcam records a picture of the stick’s shadows on the plate, & the sight processor interprets this information in order to derive the sun inclinations. The microprocessor receives the angle and uses them to properly position the heliostat using stepper motor. The suggested system may be put in a single heliostat or a collection of heliostats & is site independently. It follows the sun correctly and costs little to operate, according to initial testing.
Industrial-Organizational Neuroscience And The Rise Of Big Data

Data is all around us. Data is gathering anywhere that is accessible. Manufacturing (I-O) psychologists are quite interested in a lot of that data, frequently in ways that the people who create the technology and procedures that produce and store that records were not aware of. In the course of their research and treatment, I-O therapists also produce data in ways that, particularly when combined with data from other sources, result in enormous databases. This wealth of information—variables, measurements, perceptions. This is the contemporary "big data" environment, and along with benefits, it also poses risks and concerns. The packets here should be prepared to change, maybe quickly, as the big data world continues to develop gain expertise and clout in this field. Additionally, these suggestions are meant to supplement suggestions in allied fields of I-O biology, such job testing and screening. We hope that the and via an open o p box will help to further enhance these suggestions with the collective expertise of the newspaper’s reader.
A Novel Hybrid Encryption Technique for Cloud Data Security

In this digital world, as the internet has become an integral part of our daily life and we are heavily using it to exchange data of numerous types. Cloud computing is also a type of internet-based computing which has established itself as a potential business model of the most rapidly expanding sector of the information technology industry. Most IT-related businesses, organizations, and educational institutions migrating their operations to the cloud to save their IT infrastructure cost and maintenance. On the other hand, people are also worried about the safety and privacy of their data, which is kept on the servers of the service providers. To address this aspect, in this paper, we have studied existing available solutions given by many researchers and proposed a novel hybrid encryption scheme as a means of ensuring the confidentiality and safety of the data stored in our cloud-based dynamic environment.
Identification of Cyber-Attacks in IoT-based Healthcare

The IoT has captivated the attention of the scientific and commercial sectors due to its profound influence on human existence. The IoT has lately developed as a cutting-edge platform for building smart environments. The IoT-based healthcare environment is a subsection of the IoTs wherein medical devices transmit data with one another to communicate sensitive data. The cybersecurity of IoT systems has recently been a major topic, particularly in the healthcare industry, where numerous cyber-attacks uncovered devastating IoT cybersecurity risks. Conventional network safety methods are well-established. Nevertheless, because of the limited resource nature of IoT equipment and the peculiar characteristic of IoT standards, traditional security procedures cannot be used effectively for defending IoT systems and networks from cyber-attacks. To improve the IoT security level, there is a need for IoT-specific datasets, tools, and methods. As a result, protecting the IoT-based healthcare setting from cyber-attacks becomes essential. This research's main objective is to illustrate how DRNN and SML approaches (such as RF, RC, DT, and KNN) can indeed be used to create an efficient IDS in the IoT-based healthcare setting for categorizing and predicting unforeseen cyber-attacks. Data from networks are normalized and preprocessed. Then, we used a particle swarm method with a bio-inspired design to improve characteristics. A comprehensive analysis of trials in DRNN and related SML is conducted using conventional statistics for intrusion detection. By extensive analysis, it was determined that the suggested SML model beats current methods with a 99,76 percent accuracy.
Neural Machine Translation and Detailed Analysis of Impact of Pre-Processing Techniques

In natural language processing, machine translation is important application to translate from a language to another. Neural machine translation has achieved the significant benchmark for English and some foreign languages. For Indian regional languages, neural machine translation is in initial stage. Indian languages are low resourced and there is not enough data to get better model for machine translation. Pre-processing is one important technique by which low resourced data can be processed in such a way that may increase machine translation accuracy. There are several preprocessing techniques in natural languages processing like stop word removal, stemming, lemmatization etc. In this paper existing pre-processing techniques are analyzed for neural machine translation. In this paper, detailed analysis carried out to find the impact of pre-processing in the natural language processing using Bengali language
Suspicious Object Tracking With Yolov3 with Python Using Open-CV

Object tracking system is essential for the surveillance of the suspicious abandoned objects. Tracking of the suspicious abandoned object is widely used in many areas such as airports, railway station and on its track, parking lots and public transportations to prevent terrorism and avoid the incident related to it. Precise recognition of the abandoned objects in an image arena can detect many applications using different vision algorithms. In this research paper, we present a multi-object tracking model along with left over baggage detection in a real time environment. In this article, we present the analysis of track-by-track methods, including the YOLO track and the SORT algorithm track. This article contains details about a procedure dataset of image edify with YOLO for 6 specific category, which is cumulated in videos for detection by the SORT algorithm. Identifying vehicles or pedestrians in continuous videos is useful for congestion survey. The purpose of this article is to analyze and understand the province.