Reviewerly/exHarmony huggingface dataset:
Exploring Sustainable and Affordable Cancer Care Using Artificial Intelligence

Now, in recent decades, AI and ML have become a major part in developing and maintaining the healthcare system. Now, by using AI and ML in healthcare, it can provide a massive help for the healthcare workers. AI and ML help the healthcare workers for making better decisions. In some practical areas, it may take the place of human action for making decisions such as radiology. It can help to gather medical knowledge or information from different journals, textbooks, or clinics, which will help in reducing time for study and research. AI and ML help in predicting the early diagnosis of disease based on the patient’s data and even help to prevent that disease. Breast cancer is the most frequent category. Breast cancer is followed by lung cancer, followed by mouth cancer. These statistics have triggered this research. Breast cancer is found in every one women among eight woman. Sustainable care shall help to fight with the disease. Sustainable care includes affordable cancer care and it’s possible through early prediction of cancer. In this research we are using artificial intelligence-based techniques for early prediction of cancer. Future direction of work will focus on usage of transfer learning and other models of AI-ML to help the society and mothers of nations to fight against the increasing spread of cancers.
Properties of the New Time-Space with a Constant Path (Sconst.)

It is known that the material world in which we live is in time-space with constant time (Tconst). It is made up of transverse vortices that propagate in space at a constant speed (Vconst.) and only the distance is variable (S var.). According to the axiom which makes true the Maxwell's theory of the electromagnetic field , these transverse vortices also have a constant tangential velocity along the motion of the vortices [1]. That is why they form so called "closed vortices" and all their points move with in one and the same time (Tconst.). That is way the transverse vortices form so called real time-space with T=const.The new time-space, described in previous works by the same author, is made up of longitudinal ( not transverse) vortices, which have a variable (Vvar.) (not constant ) velocity, i.e. they can be either accelerating or decelerating [2,3]. These longitudinal vortices with variable velocity (Vvar.) are described by the theory of New Axioms and Laws by the same author [4].According to this theory, the accelerating longitudinal vortices suck transverse vortices from out and as they attract to each other and concentrate themselves to a funnel-shaped package. So they form accelerating funnel in S=const.This it happens by inserting the fastest vortex (Vmax.) in the center of the funnel, that moves with a minimum number and minimum diameter of coils .It passes the fixed path (Sconst.) with minimum time (Tmin.) and therefore it appears first in time. But the slowest vortex (Vmin.) is wound up in the periphery of the funnel , that is winding with the maximum number and maximum diameter of the coils .Therefore passes the same fixed path (Sconst.) with maximum time (Tmax.) and it appears last in time.The decelerating longitudinal vortices emit transverse vortices to outside and repel each other by distracting and scattering their longitudinal vortices sideways.So they form decelerating funnel in S=const.The accelerating and the decelerating funnels of longitudinal vortices ( in Sconst. time-space) plays the role of a link between the decelerating and accelerating transverse vortices ( in Tconst. time-space).The properties of the new structures of longitudinal vortices are very unexpected and exclusive useful. For axample the gravity attraction is due to the acceleration along longitudinal vortices but not to space distortion. The real time in time-space T=const (TrealTconst ) has an opposite direction to the real time in time-space S=const ( TrealSconst) and so on.
EFFECTIVE EDUCATIONAL PRACTICES BASED ON ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE TRAINING OF FUTURE TEACHERS

The paper considers practical aspects of the implementation of the educational process in an electronic educational environment for students — future teachers in order to form their readiness to perform work functions and actions in conditions of uncertainty and digital transformations. Based on the analysis of scientific literature, the results of the survey, the experience of preparing students of pedagogical directions, as well as taking into account the application of a multi-paradigm approach to pedagogical education, the methods of training future teachers using artificial intelligence algorithms are proposed. The article presents the methodology of the existence and functioning of a collective pedagogical megasystem, both forecasting pedagogical processes and identifying new patterns and making effective didactic decisions based on the use of collective intelligence algorithms in the implementation of pedagogical practice. The article reveals the applied aspect of the use of collective intelligence algorithms in the implementation of quasi- professional activities at the university and during pedagogical practice in order to form the readiness of future teachers to implement pedagogical activities based on learning to choose effective didactic solutions. This aspect is key for the readiness and ability to implement professional actions and functions due to variations: the contingent of students, subject content, organizational and pedagogical conditions of a particular educational organization. Taking into account both spontaneous and systemic aspects in the implementation of professional activity is crucial for the choice of learning technologies that ensure maximum productivity of the teacher and, as a result, the achievement of high educational results of students. For these purposes, the most suitable algorithm for choosing methods and technologies for the implementation of professional activities by future teachers is the ant algorithm. This algorithmic model allows us to take into account both the probabilistic nature of the changing impact parameters, and allows us to evaluate and select the high effectiveness of the application of this algorithm, which distinguishes it favorably among other artificial intelligence algorithms. The article provides an example of an ant algorithm for determining the best hierarchy of organizational and pedagogical conditions conducive to the best choice of technologies and methods for the implementation of professional activities by future teachers. As a conclusion, the authors propose to use collective intelligence algorithms in the process of quasi-professional activity of future teachers in the framework of pedagogical practice, which will contribute to the formation of their readiness to implement pedagogical activities in terms of performing labor functions and actions based on learning to choose effective didactic solutions.
Impact of COVID-19: Role of Strategic E-Learning System

This paper aimed at investigating factors about interrelationship between E-Learning Orientation, E-Learning Implementation, E-Learning Perception, and E-Learning Satisfaction. The information of the study was gathered from 330 students of large university affiliated institutes. Judgment or purposive sampling is used for selection of respondents. The outcome of study showed relationship of all variables for a proposed model. All the constructs are correlated with each other. E-Learning Orientation, E-Learning Implementation, and E-Learning Perception are having positive relationship on E-Learning Satisfaction. The output of this study can be utilized to improve E-Learning environment system in better ways. Variables used in this study are taken for the first time for such type of research work. The work also intended to participate in current literature about E-Learning Orientation, E-Learning Implementation, E-Learning Perception, and E-Learning Satisfaction.
Real-Time Face Detection and Recognition Using Deepface Convolutional Neural Network

Many trendsetting inventions are often founded as technology improves, and face acknowledgment innovation plays a vital role. The surveillance camera recognizes and photographs a person's face. The image recorded is then compared to the image stored in the database. Face detection and recognition is the process of identifying faces and recognizing certain features in a photograph. We investigate facial recognition approaches utilizing a deepface convolutional neural network in this paper. The faces are matched using the Euclidean distance technique. The fundamental advantage of CNN over its counterparts is that it recognizes significant highlights without the need for human intervention. By preserving the fully linked layer of the real network, the number of parameters is minimized, and overfitting is avoided by employing adam. To identify and recognize masks in the image, the Softmax classifier is utilized. This system's algorithms have a higher accuracy rate and are more efficient.
Face Mask Detection for Real Time Video Streams

The Face Mask Detection model is used to make sure a person is wearing a mask or not. This model results from the grappling situation presented by COVID-19, resulting in the mandatory use of masks at public places. Security agencies need to plant actual personnel to make sure all the people in public are wearing ‘masks’, this model will lessen the risk of people being contacted by COVID-19. This research helps us understand a broader perspective about the Face Mask Detection models by comparing different state of the art models. The model uses MobileNetV2 architecture that has inverted bottlenecks and depth-wise convolutions to filter features. The complete model is built in two phases, the first one consisting of making a Face Mask Detection model trained to detect the face and mask, and then placing it in the Real Time environment by using the OpenCV for actually predicting the usage of face mask.
Cloud Computing Leads Towards Sustainable Supply Chain Management

In today's environment, businesses are looking into cutting-edge strategies to improve their supply chain's cost and operational effectiveness. Cloud computing appears to be a promising technology that can help with this optimization by providing infrastructure, platform, and software solutions for the entire supply chain network over the Internet. The use of cloud-based services in supply chain management has financial and operational benefits, but all supply chain stakeholders should be aware of potential dangers and restrictions. Cloud computing can improve supply chain collaboration and infrastructure needs by allowing for more flexible outsourcing of software. Rather of sustaining and paying for maximum use, this technology proposes an approach that allows for flexibility in adding on the way, depending on the entire business process and supply chain network model. Aside from the normal tech hype, cloud computing has the potential to be an ideal technology for supporting and managing a constantly changing and dynamic network, and thus for supply chain management (SCM). This paper provides an overview of cloud-based supply chain management. A quick introduction to cloud technologies is given first. The factors of cloud computing which effect supply chain activities is then examined, as well as the benefits and drawbacks of incorporating this technology into current supply chains. Finally, findings are offered, as well as future study directions.
Twitter Sentimentality Examination Using Convolutional Neural Setups and Compare with DCNN Based on Accuracy

Aim: To perform Twitter Sentiment Analysis using Convolutional Neural Setups and compare with the Deep Convolutional Neural Networks (DCNN) based on Accuracy. The Convolutional Neural Network (CNN) has been used for the work of text classification and has yielded quite good results. In this paper, we propose convolutional neural networks for the task of sentimental Analysis by using Tweets from Twitter. Materials and Methods: The data set that is needed for Twitter sentiment Analysis is acquired from Google’s Kaggle Website and the data set is provided by Analytics vidhya. The data set has the columns namely id, label, tweet. The Testing is performed in Google Collab Environment as it is a web-based application. The data set is imported into collab environment and CNN, DCNN Algorithms are trained and tested. Results: The various results came out in the form of accuracy for various input sample sizes. The IBM SPSS statistical software is used in order to perform operations and obtain results.From the results of this study, it is proved that CNN has performed better than that of the DCNN algorithm. CNN has an accuracy of 95.88% whereas DCNN has an accuracy of 88.10%. Conclusion: In this paper, based upon the obtained results Twitter Sentiment Analysis using Convolutional Neural Networks is more accurate than Deep Convolutional Neural Networks.
ON THE ISSUE OF SUPPORTING THE SOCIAL ADAPTATION OF STUDENTS FROM AMONG THE INDIGENOUS PEOPLES OF THE NORTH AND THE FAR EAST OF THE RUSSIAN FEDERATION IN THE CONTEXT OF DIGITALIZATION

The article considers the problem of adaptation of students from among the indigenous peoples of the North and the Far East of the Russian Federation to the conditions of study in an educational organization of higher education in the context of their modern ethno-cultural self-identification. A thesis has been put forward about the possibility of using digital resources for the development of ethnocultural identity as a factor in strengthening subjectivity, contributing to intercultural communications and social adaptation. Since 2021, a project aimed at preserving and developing languages and culture has been implemented at the Siberian Federal University as part of the state assignment of the Ministry of Science and Higher Education of the Russian Federation to carry out work on the topic “Implementation of measures aimed at providing additional social support to people belonging to indigenous small peoples of the North, Siberia and the Far East of the Russian Federation studying in Russian educational institutions of higher education under programs that provide for the study of the languages and cultures of these peoples" through the new digital educational platform "Snoword" for indigenous peoples studying free of charge under programs that provide for the study of languages and cultures of these peoples.
Elliptic Curve Cryptography Applied for (k,n) Threshold Secret Sharing Scheme

Invention of Secret Sharing Scheme by Adi Shamir, along with the prevalent advancements offers strong protection of the secret key in communication network. Shamir’s scheme, which is established using Lagrange Interpolation polynomial. The group manager or dealer of the group splits the secret S to be communicated into n pieces allots all the n pieces to n participants. A subgroup of t or more participants of the group come together to reconstruct the secret key. Later, the cryptanalysis of secret sharing scheme came into picture in the direction of cheater detection whose motivation is to fool the honest participants. The present paper goals to describe a modification to (k,n) threshold secret scheme using elliptic curve cryptography to avoid the dishonest shareholders and faked shares. In this scheme, the group manager or dealer distributes the shares among the participants as affine points on the elliptic curve so that the share modification by the participants or faked shares can be easily detected.
A Matrix-Based Homomorphic Encryption for Preserving Privacy in Clouds

As huge data need to store on clouds for big data analytics and related features to analyze data computationally. Cloud computing serves many services to the client over their data such as data storage, analysis, and different resources. To avail such services over clouds, client needs to provide the access to cloud service providers (CSPs) over their data, which leaves data unprotected and confidentiality of their data is at stake. Different techniques are proposed to preserve the privacy and confidentiality of client’s data. One of the most common is, cryptography, which converts plaintext to unreadable form that is ciphertext. But almost all the existing cryptographic techniques are not up to mark and don’t have potential to perform the computation on the ciphertext. The recent trends in field of cryptography, the homomorphic encryption is emerged as a boom, which enhance the privacy of data to a large extent, without compromising the confidentiality of users’ data at cloud end. Homomorphic encryption provides the ability to perform computation on encrypted data, even when data is in use. Most of the existing homomorphic encryption algorithms are having high computational and time complexity. In this paper, an enhanced homomorphic encryption algorithm is proposed specifically for the cloud environments, having better performance. In this paper, an enhanced homomorphic encryption algorithm is proposed specifically for the cloud environments, may having better performance. The algorithm uses prime number-based matrix for encryption and decryption. The algorithm may give better performance in terms of encryption, decryption, and overall execution time.
Enhancement of Imbalance Data Classification with Boosting Methods: An Experiment

The idea of boosting emanates from the area of machine learning. It is a challenging task for imbalance data set to have appropriate distribution of data samples in each class by machine learning algorithm. To deal with this problem, ensemble learning method is one of the popular approaches. Ensemble methods integrate several learning algorithms, which gives better predictive performance as compared to any of the basic learning algorithms alone. Based on this research, a question is formulated. The null hypothesis is stated as “There is no significant difference between single classifier and classifier with ensemble techniques - AdaboostM1 and Bagging.” Alternative hypothesis is stated as “Ensemble techniques AdaBoostM1 and Bagging works more superior as compare to single classifier.” We have conducted an experiment on three imbalanced data sets. We examined the accuracy of four classifiers Naïve Bayes, Multilayer Perceptron, Locally Weighted Learning, and REPTree. The predicted accuracy score of these classifiers are compared with boosting techniques AdaboostM1, Bagging, Voting, and Stacking.
An Informative Solution to Predict and Improve Accuracy for Approving Bank Loan Using Novel Numerical and Categorical Data of the Customer by Comparing Logistic Regression Over Decision Tree Algorithm

Aim: The framework aims at classifying numerical and categorical data to predict the bank loan approval for improving customer retention rate. Materials and Methods: The customer information dataset used for training and testing of the proposed prediction model consists of 500 customers with 10 attributes. The classification of loan prediction is done by adopting logistic regression with the sample size =10 and decision trees with the sample size =10 with a G power of 80% and threshold 0.05%, CI 95% mean and standard deviation. Results and Discussion: Analysis of the results show that logistic regression (73.98%) performs better than decision tree classifier (72.35%). There is a statistically significant difference between the study groups with (P&lt; 0.05). Conclusion: Prediction in approving bank loans shows that the logistic regression appears to generate better accuracy than the decision tree algorithm.
Evaluating the Quality of Classification in Mixture Model Simulations

The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations, it was determined that models without a covariate better predicted the number of classes. These findings in general supported the use of the popular three-step approach; with its quality of classification determined to be more than 70% under various conditions of covariate effect, sample size, and quality of indicators. In light of these findings, the practical utility of evaluating classification quality is discussed relative to issues that applied researchers need to carefully consider when applying latent class models.
A Survey on Hadoop Security and Comparative Analysis on Authentication Frameworks in Hadoop Clusters

This paper presents a survey on various security frameworks which aim to secure Hadoop clusters and the files stored there. Most of the Hadoop platforms rely on Kerberos Authentication Protocol for user authentication. It is known that Kerberos itself has some vulnerabilities like Password Guessing Attacks, Single Point of Failure, Insider Attacks, and Time Synchronization problems. A new authentication framework that can address most of the identified issues and is computationally feasible than other schemes is introduced in this paper. The proposed framework is based on Secure Remote Password Protocol (SRP), Threshold Cryptography, and Blockchain Technology. The system focus on eradicating Password Guessing Attacks and Single Point of Failure in Kerberos Enabled Hadoop Clusters. The analysis results strengthen the claim that the proposed scheme beats the others in terms of computational efficiency and feasibility while offering the same security features. The paper is concluded with directions for future work.
An Asymmetric Cryptosystem of Skewed Affine Cipher Over Elliptic Curves with Block Matrix

The retaining of confidentiality in data transmission with user authentication has become eminent for easy and secure communication. This is achieved by using the art of cryptology at its best. Various ciphers are developed balancing time, memory, and security. In particular, the invertible property is the basic requirement in constructing a secure cryptosystem. In this paper, an invertible block matrix is constructed over Fibonacci sequences. The sender and the recipient can derive the block matrix through the key exchanged over a secure channel for encryption and decryption. Also, an asymmetric cryptosystem is designed based on the skewed Affine cipher, which uses a bijective map over points on an elliptic curve. The developed cryptosystem is resistant to cryptanalytic attacks.
Credit Card Fraud Detection Using Selective Class Sampling and Random Forest Classifier

The incidences of credit card fraud are increasing. It puts the hard-earned money of users at risk. The financial institutions and governing bodies are looking forward to some robust and reliable ways of detecting credit card fraudulent transactions. The task is challenging due to non-availability of enough data, high class imbalance, and high stake on false negative rates (FNR). Most of the methods available in literature perform well on the accuracy-based performance metrics. However, they fail to yield satisfactory ROC-AUC performance due to the high false positive or false negative rates. Mitigating this issue is the real challenge for the task of fraudulent credit card transaction detection. This paper investigates the problem of credit card fraudulent transaction detection and proposes a technique for it. The proposed method uses a custom selective class sampling-based class balancing technique, and subsequently, it uses random forest for classification. The experimental results show that the proposed technique has better AUC score, accuracy, precision, and recall values as compared with other similar approaches.