Reviewerly/exHarmony huggingface dataset:
Block chain Technology Adoption in various Domains and the way of getting Double Security

Blockchain is a technique that allows for the decentralized data storage and coin is a form of trade that operates similarly to fiat money. There in old, government’s bankers and the administration acquired possession of and controlled all assets, including money, platinum, and other rare metals, by stealing them from the population. Because of the reasons outlined beneath, crypto aspires to return power to average folks.The first Bitcoin was released in 2008 by either a man going by the moniker Pseudonym. The foundation of this system is encryption, or concealed reading. The privacy of players and activities is ensured via cryptographic autonomously without interference from a 3rd party Blockchain-based crypto currencies is a decentralized mentoring electronic payments and financial transaction system that is secured and decentralized. Elevated info may be securely stored using bitcoins. Although fiat (written) money is widely utilized nowadays, it has certain drawbacks. These problems, which are being abused by strong groups, concern Him. Banknotes has several disadvantages, including the ability to be copied, misappropriated, and being difficult to transport between locations owing to expensive costs. He created Bitcoins, a kind of electronic money that can fulfil all of those promises. With Bit, the goal was to provide a fee-free, open-source, and user-controlled virtual trading. The primary problem with bank notes is it companies and states, however, supervise and regulate it.Bankers are unreliable since they have caused individuals to lose their hard - earned cash. Crypto currencies offers its customers anonymity and returns ownership of their income to ordinary folks. Blockchain technology has the ability to revolutionize many different industries, including voting, medical, estate development, among others. It has the potential to fix system that have been compromised for a long time. However, it is now utilized all over the globe after thirteen years, although there is still a considerable distance to go. It will talk about the history, present, and destiny of crypto currencies and block chains as well as ways to get this innovation more widely used.
An Innovative Internet of Things (IoT) Computing-Based Health Monitoring System with the Aid of Machine Learning Approach

The community health area comprises an enormous measure of data, and specific methodologies are utilized to deal with that data. One of the most common approaches is handling as well as processing. This technique forecasts the likely consequences of cardiovascular disease. The result of this strategy is to foresee the former heart disease. The work controls IOT utilizing a sensor (a heartbeat sensor to screen beats) and Arduino, and the outcomes might be seen on a successive screen. IFTTT is utilized to break down sensor readings in Google Sheets, which are accordingly changed over into CSV go-like information. The datasets utilized are characterized by treatment boundaries, in addition to being used for data preparation and testing. This technique assesses those boundaries utilizing the data arrangement request strategy. With artificial intelligence calculations and order work. The dataset is first taken apart, analyzed, and screened, after which the accumulated information is handled in Python programming utilizing AI Calculations, specifically Choice Tree Calculation with Irregular Woodlands Arrangement Calculation. SVM (Backing vector machine) creates the best outcomes concerning identifying coronary illness. Thus, the recommended worldview is demonstrated to be a solid one for foreseeing past coronary illness. The recommended equipment and programming innovation help patients in anticipating heart illness in its underlying stages.
Real-Time Signal Processing using AI Integrated Framework for Color and Drawing in Gesture Recognition

In the realm of image processing and pattern recognition, writing with gestures in the air has grown in popularity and difficulty over the past few years. It contributes significantly to the creation of automation processes and can improve human-machine communication in a range of systems. Many studies focus on cutting-edge methods and techniques that will expedite processing while preserving a high level of identification accuracy. Internally, the area of computer vision considers object tracking to be crucial. Object tracking techniques are credited to modern computers, easy access to good-looking, affordable video cameras, and automated video analytics requirements. The video analysis technique generally consists of three fundamental steps: first, recognizing the object, second, tracking its movement from frame to frame, and third, assessing the behavior of that object. Each of the four takes into account a separate issue: choosing the best item representation, choosing the tracking function, purchasing the item, and tracking the item. Numerous real-world applications, such as autonomous monitoring, video identification, and vehicle navigation, depend heavily on object-tracking algorithms. This area is utilized by the research, which focuses on it to create a prospective text-to-text converter transition. This Research occasionally works as a freelance journalist. Your fingerprint will be tracked by a computer. The created text can be copied and used for a variety of things, including emails and SMS. For the deaf, it will be a potent means to establish this connection. By removing the need for writing, an efficient communication system decreases the use of the phone and laptop.
An Efficient Techniques For Disease Prediction From Medical Data Using Data Mining And Machine Learning

Individuals are susceptible to numerous ailments in today's global setting, with people living highly automated lives under tremendous job pressure, both at home and at work. Such disorders have recently been on the rise at an alarming pace. As a result, the healthcare business must assume a prominent position soon, accountable for people's health, a better society, and a successful country at large. Healthcare prices are rising as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data is being generated and used for diagnostic and detection approaches. This data might be used to obtain data for anticipating disease, commencing preventative measures, and improving treatment procedures long before the diseases progress. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. The purpose of this paper is to examine the effectiveness of various classification techniques, such as Naive Bayes (NB), J48, REF Tree, Metaheuristic Optimization (SMO), and Linear regression (LR), on data sets pertaining to cardiovascular disease (CVD). Employing the WEKA tool, an analysis was designed to test the efficacy of several strategies using illness data sets obtained from UCI respiratory. Different categorization methods are used to compare the values of various metrics, such as the percentage of right classifications, the recall, the F-measure, and the time required.
Predicting Student’s Mental health by Analyzing their Tweets

Feedback systems become very common and useful in each domain of interest with the advent of the social network. One of the domains where social networks play an essential role in educational institutions is where students give their opinions on the teaching staff, institute, and their interests. This kind of data causes difficulties in understanding students’ behavior so need to understand and process that data carefully to drive useful information. One of the study support this is known as sentimental analysis (SA). SA is the computational study of user’s likes and dislikes of goods. It considers the polarity of natural language texts, such as positive, negative, and neutral in some cases. Student online feedback datasets in the form of tweets are employed, and three measures: Precision, Recall, and F-Measure are used for comparative analysis. Tables and graphs are used to display the results.
Administration of Big Data using the Cloud as base

A vast amount of data is being generated from various sources every day which is dynamic and large in size, making conventional processing techniques unable to manage it. The term Big Data is referred to the storage, processing, and analysis of massive quantities of data. Large-scale and complex computing is required for storing and analyzing this data, a problem that can be tackled by the use of cloud computing. Big data has found its perfect platform in the public cloud. A firm can consume resources and services from a cloud on demand without having to construct, own, or maintain the infrastructure itself. Big data technologies are thus made available and affordable to nearly any size of organization thanks to the cloud. We can obviate the need of maintaining high-cost computing, storage, and software with the aid of cloud computing technologies. Thus, cloud computing can offer infrastructure to perform Big Data Analytics in an economical and efficient manner. The current concerns, aids and future research prospects regarding the topic of Cloud-Based Big Data Analytics are presented in this paper.
Implementation of KNN Algorithm with BOA to Predict the Cancer with more Accurate Way

K-nearest Neighbor (KNN) is one of the most widely used ML (Machine Learning) methods for data includes organizational, and categorizing illnesses and faults. This is important due to frequent changes in the training sample, for which it would be costly to create a new classifier using most methods each time. As a result, KNN may be employed successfully since it does not need the creation of a residual classifier in before. KNN is simple to use and has a wide range of application possibilities. Here, an unique KNN classification method is proposed that optimizes utilizing the Bayesian Optimization Algorithm (BOA). In order to exploit knowledge about the dataset’s architecture and the cosine similarity of distance, this study proposes changes to the closest neighbour K value in an effort to improve classification accuracy. The results of experimental work based on datasets from the University of California Irvine (UCI) repository indicate enhanced classifier performance relative to traditional KNN and increased reliability without a substantial speed penalty.
Comparative Analysis of Artificial Intelligence and its Powered Technologies Applications in the Finance Sector

Banks and other financial institutions will be able to adopt innovative products and services, and more importantly, endure disruptions to their customers’ experience, thanks to advancements in artificial intelligence (AI). Financial institutions would struggle to thrive in today’s mechanized economy without the aid of fintech companies, which use innovative innovation to enhance or try and supplant human specialists with complex calculations. To keep a critical upper hand, banking and monetary organizations should take on man-made intelligence and integrate it into their business system and tasks. This examination venture will look at the elements of computer based intelligence environments in the banking and money industry and how they are quickly arising as a key disruptor by focusing on probably the most basic perplexing business worries around here. Several viewpoints exist on the potential of AI in this field, with the majority centering on the effects and relevance it will have on employment in the banking and financial services industry.
A Novel Cognitive Analysis to Identify and Recognize Sentiments using Recommendation Engines through Text

The goal of psychological analysis is to find and identify different emotional states expressed in writings, such as rage, contempt, terror, pleasure, sorrow, and shock. These days, writing may take many other forms, including social media postings, blogs, essays, and consumer reviews. Small scale and medium from these settings may be an important source for textual data to uncover hidden elements, such as sentiments. For recommender systems and customized suggestion, text with emotional content is a crucial resource. Each sentence in a text that expresses emotion is a potential unit for perfectly alright emotion recognition. The task's goal is to determine which sentence includes the emotion clause for each emotion. In order to test our model, we first turn emotion cause extraction into a supervised ranking issue from the standpoint of information retrieval. We next employ a publicly accessible database for emotions cause separation. We shall create the most potent ranking model in the future by creating efficient rating features for emotions cause extraction.
A Comprehensive Analysis of Ultrasound Image Processing Methods

The medical pictures that are utilized for medical purposes include things like ultrasounds, which provide such images. The ultrasound scans displayed a variety of approaches that may be utilized for the calcification of breast cancer. The calcification of breast cancer is analyzed with the help of the ultrasound pictures. A number of processes, such as pre-processing, segmentation, feature extraction, and masking, are included in the breast cancer calcification method. The cancerous part of the input picture will be hidden when the calcification procedure is applied. The findings will be examined using criteria such as recall, accuracy, and precision.
Robust Visual Object Tracking in Clustered Environment

To perform visual object tracking in clustered environment, kernel methods based Support Vector Machines (SVMs) can be used. A Scale adaptive Kernel support correlation filter Algorithm (SKSCF) is employed here for visual object tracking. In this method, the SVM models with circulant matrix formulation have been used. For visual tracking and its related applications it enhances the process of optimization. SVM models with circulant matrix formulation use the discrete Fourier transform operation to accomplish visual object tracking. The issue of visual objects is illustrated as a recursive command of Support Correlation Filters (SCFs). A Scale Adaptive Kernel Support Correlation Filter (SKSCF) has an O(n2 log n) computational complexity. The SVM-based techniques’ computational complexity is at O (n4). Along with the Scale Adaptive Kernel Support Correlation Filter Algorithm, factors like multi-channel features, scale adaptive approach, and kernel methods are applied. For a sizable standard dataset, the Scale Adaptive Kernel Support Correlation Filter Algorithm provides finer outcomes in terms of reliability and speed.
An Organized Study of Opinion Methods

Emotion recognition serves as one of the key research areas as Facebook use on web. On instagram, Google, Wikipedia, as well as others, individuals share their views, ideas, emotions, feelings, and views, bringing a considerable measure with fire to modern life. Ai, sometimes referred to as recommender systems, focuses on categorizing and predicting men’s sentiments regarding that same issue. It is sometimes referred to as “emotional ore” or “mood coal.” It involves classifying written texts into pro or con groups depending on the stated viewpoint on a particular problem. Despite the simple fact that recommendation system may seem to be similar to text categorization, it faces a variety of issues which has inspired much study in this field. To improve the tone study, many automation (ML) and also dictionary strategies were created in the story. In this work, we just provide results of an university level that aims to assess the state of the science. In order for new called electronic to be developed by researchers in the future who have more information and can address in all issues and get the best outcomes.
Accurate Short Text Classification for Improving Accuracy by using Bi-LSTM in Comparison with Recurrent Neural Network

Major goal focuses on getting the higher accuracy while classifying shorter texts depending on the Bi-LSTM and RNN and to perform comparison against Recurrent Neural Network. Materials and Methods: For the short text classification, the proposed system Bi-Directional LSTM will have a comparison with the RNN and the result is decided by major roles. Machine Learning algorithms based Bi-Directional LSTM (N=26) and RNN (N=26) were used for implementation of research. Research utilizes 52 samples to perform testing. Test is calculated with the help of two groups as well as G power value 80%. Statistical analysis is done with the help of SPSS. Results: Bi-Directional Long Short Term Memory has better mean accuracy of 85.62% when compared with Recurrent Neural Network where the mean accuracy produced is 62.42% and has significant value 0.113 (p>0.05). Conclusion: The output shows Bi-LSTM has better accuracy than the RNN.
NN Model for BC Transaction Accuracy Evaluation

Prototypes of a decentralized ledger (DLT). Programmable contracts, a decentralized ledger, encrypted updates, time and date stamps, and near real-time updates are all features that can be implemented using blockchain technology. When it comes to training NNS, DL employs the utilization of biological neurons, which are analogous to brain cells. Many layers of hidden neurons are used in deep NNS for optimal learning. The use of blockchain technology and AL in financial transactions has been the subject of several studies (AI). Synthetic NNS fall under this category. The use of AI within blockchain helps to lessen its environmental impact. AL equipment will reduce mining costs and increase efficiency.
Predictive Control Integrated with Block Chain and Machine Learning for Smart Power Plants

The share of new electric electricity production will steadily rise with the development of new power grids, and the exchange interaction of simulated power stations would become increasingly difficult and complicated. The old centralized forecasting approach can not only match the demands of the growth of online energy plants due to the rise in the quantity of energy entities participating in them. The operation efficiency of imaginary power plants may be increased thanks to the decentralization, tamper tolerance, and cryptographic protocols of the bitcoin. The consensual latency of the bitcoin in concluding activities, therefore, prevents the Ethereum blockchain from growing as more scattered users participate in exchanges. This study first suggests a design for a ledger digital electricity generation network and explains the connections between and functions of every level. Eventually, Segment Byzantine Fault Resilience, a chain division unanimity mechanism for digital power plant payments, was put out (PPBFT). The method in this study may significantly minimize the acceptance time as the number of nodes rises and enhance the fairness and timeliness of payments, it is proven using C++ language to model the technique.
Nobel Face Expression Recognition Technique

This research tries to create mind-implemented intelligent computers or robots. We first look into emotionally intelligent computers that are able to recognize human emotions. In this study, we used Principle Component Analysis (PCA) with Singular Value Decomposition (SVD) to identify the commonly acknowledged five main emotions: anger, disgust, happiness, sadness, and surprise, as well as neutral. On the training set, a 100% recognition rate for each of the five main emotions and neutral is achieved.
A Comparative Study of Cyber Security Awareness, Competence and Behavior

Cyber-attacks may pose a threat to information security. The importance of cyber awareness is increasing as internet and data usage rates rise. This research concentrates on the connections between general human awareness, knowledge, and behavior in relation to security tools. An investigation into phishing attacks was made for the purpose of cyber security simulation exercises for staff members of a sizable financial services company with hundreds of branches across the country. Information about students’ use of social media platforms to demonstrate Information Security Awareness (CSA) was gathered through an online survey. The results show that despite having a sufficient understanding of cyber threats, internet users only take a few simple and common precautions, which is insufficient. The study’s results also show a connection between greater cyber experience and understanding and threshold of cyber awareness, beyond the differences in participating member country or gender. Results, implications, and recommendation useful for cyber security training programs are presented and discussed.
A New Way of Recording Attendance of the Students using Face Recognition System

People’s connections at work affect the environment there, which is a crucial aspect of everyday supervision. However, keeping track of people’s present and absences may be done via attendance. For many years, techniques that need paper have been used to record attendance of the students. This engagement strategy had been around for a while. For a small number of participants, this approach could be suitable and workable, but it would take a lot of time and effort for a larger group. This approach has well-known and quite well disadvantages, including time requirements, an error-prone nature, and the potential for proxy participation. Using facial recognition software, we provide a useful and stylish solution to track attendance. In order to ascertain if a person is there, the technology will recognize their face and comparing it to the database. Additionally, the vital information is retained with school records and time, and if required, manual changes may be made. This method may be used to address the issue of intermediaries and false attendees.
Most Possible Likely Word Generation for Text based Applications using Generative Pretrained Transformer model Comparing to Long Short Term Memory Model

In contrast to long short term memory techniques, the proposed study intends to produce automatic next word generation for text-based applications while enhancing accuracy using state-of-the-art generative pretrained transformers and recurrent neural networks. Materials and Methods: On the data, which is a text file including a series of words, generative pretrained transformer models and long short term memory are used. Long short term memory model that contrasts cutting edge generative pretrained transformer models for recommendation accuracy of the next word. It has been suggested and created to use LSTM. The sample size was calculated to be 8046 for each group with a G power of 0.8. Results: When compared with a long short term memory model (70.84%) for the same dataset p=0.02(p<0.05), the accuracy in predicting the upcoming word for text editor based Applications utilizing generative pretrained transformers was greatest at 87.98% with the lowest mean error. In generating the next word for text-based Applications, the study shows that generative pretrained transformers are more accurate than long short term memory..Conclusion: The study demonstrates that when recommending the most possible word for text generation based applications, generative pretrained transformers are more accurate than long short term memory.