Exploiting the Capabilities of Blockchain and Machine Learning in Education

Advancing the science of coaching in education: An introduction to the special issue

School-based coaching has emerged as an effective approach for enhancing school personnel's knowledge, implementation skills, and wellbeing, as well as student achievement and behavior. This article introduces the special issue Advancing the Science of Coaching in Education, highlighting four large-scale, rigorous investigations on the effects of coaching and possible contextual variables that influence effective coaching in education. Two studies (Glover et al., 2022; Pianta et al., 2022) assessed how components of coaching (e.g., feedback, practice) may mediate or moderate classroom practices and/or teacher-student interactions that lead to improved student learning. One study (Pas et al., 2022) examined fidelity profiles of motivational interviewing embedded coaching on classroom practices and student behavior in middle school. The final study (Reddy et al., 2022) examined the effects of behavior support coaching on paraprofessional implementation of research-based behavior interventions, wellbeing, and elementary school student social behavior. This article spotlights common elements across studies and offers directions for research and innovation. The special issue closes with a commentary (Erchul, 2022) on the contributions of school consultation research to contemporary coaching research and offers recommendations for bridging research to practice.

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

In recent years cryptocurrencies are emerging as a prime digital currency as an important asset and financial system is also emerging as an important aspect. To reduce the risk of investment and to predict price, trend, portfolio construction, and fraud detection some Artificial Intelligence techniques are required. The Paper discusses recent research in the field of AI techniques for cryptocurrency and Bitcoin which is the most popular cryptocurrency. AI and ML techniques such as SVM, ANN, LSTM, GRU, and much other related research work with cryptocurrency and Bitcoin have been reviewed and most relevant studies are discussed in the paper. Also highlighted some possible research opportunities and areas for better efficiency of the results. Recently in the past few years, artificial intelligence (AI) and cybersecurity have advanced expeditiously. Its implementation has been extensively useful in finance as well as has a crucial impact on markets, institutions, and legislation. It is making the world a better place. AI is responsible for the simulation of machines that are replicas of human beings and are intelligent enough. AI in finance is changing the way we communicate with money. It helps the financial industry streamline and optimize processes from credit judgments to quantitative analysis marketing and economic risk management. The main goal of this research has been investigating certain impacts of artificial intelligence in this contemporary world. It's centered on the appeal of artificial intelligence, confrontation, chances, and its influence on professions and careers. The research paper uses AI to enable banks to generate financial resources and to provide valuable customer services. The application of the growing Indian banking sector is part of everyday life made up of several banks like RBI, SBI, HDFC, etc. and these banks have digitally implemented using chat-bots that have brought benefits to the customers.

An empirical study on the application of machine learning for higher education and social service

The work used the current mature computer technology, machine learning technology, and other high-tech to explore the comprehensive application of educational information management under the Internet to provide educational scientific researchers with a retrieval platform for educational statistical information. Deep learning was used to extract useful network features more effectively and make the machine learning model fully consider the constraints of satisfying the constraints and optimization objectives in the problem. Based on the classification of the restricted Boltzmann machine, the Gauss-binary conditional classification of the restricted Boltzmann machine model was proposed as the routing decision unit, with the given specific training algorithm of the model.

Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm

In this paper, we present an efficient and optimal method for optimization of Hahn parameters a and b using the Artificial Bee Colony algorithm (ABC) in order to improve the quality of reconstruction and the compression of bio-signals and 2D / 3D color images of large sizes. The proposed methods are essentially based on two concepts: the development of a recursive calculation of the initial terms of Hahn polynomials in order to avoid the problems of instability of polynomial values and the use of ABC algorithm to optimize the values of the parameters a and b of the discrete orthogonal Hahn polynomials (HPs) during the reconstruction and the compression of bio-signals and 2D / 3D color images. The simulation results performed on bio-signals and on large size 2D /3D color images clearly show the efficiency and superiority of the proposed methods over conventional methods in terms of reconstruction of signals and images.

Blockchain Hyperledger with Non-Linear Machine Learning: A Novel and Secure Educational Accreditation Registration and Distributed Ledger Preservation Architecture

This paper proposes a novel and secure blockchain hyperledger sawtooth-enabled consortium analytical model for smart educational accreditation credential evaluation. Indeed, candidate academic credentials are generated, verified, and validated by the universities and transmitted to the Higher Education Department (HED). The objective is to enable the procedure of credential verification and analyze tamper-proof forged records before validation. For this reason, we designed and created an accreditation analytical model to investigate individual collected credentials from universities and examine candidates’ records of credibility using machine learning techniques and maintain all these aspects of analysis and addresses in the distributed storage with a secure hash-encryption (SHA-256) blockchain consortium network, which runs on a peer-to-peer (P2P) structure. In this proposed analytical model, we deployed a blockchain distributed mechanism to investigate the examiner and analyst processes of accreditation credential protection and storage criteria, which are referred to as chaincodes or smart contracts. These chaincodes automate the distributed credential schedule, generation, verification, validation, and monitoring of the overall model nodes’ transactions. The chaincodes include candidate registration with the associated university (candidateReg()), certificate-related accreditation credentials update (CIssuanceTrans()), and every node’s transactions preservation in the immutable storage (ULedgerAV()) for further investigations. This model simulates the educational benchmark dataset. The result shows the merit of our model. Through extensive simulations, the blockchain-enabled analytical model provides robust performance in terms of credential management and accreditation credibility problems.

DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff’s alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning and deep learning methods. The dataset is available on Github and Zenodo.

Emotionally charged text classification with deep learning and sentiment semantic

Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional document representation, such as vector sequences or matrices combined with document sentiment, should be explored. In this paper, we show that documents can be represented as a sequence of vectors carrying semantic meaning and classified using a recurrent neural network that recognizes long-range relationships. We show that in this representation, additional sentiment vectors can be easily attached as a fully connected layer to the word vectors to further improve classification accuracy. On the UCI sentiment labelled dataset, using the sequence of vectors alone achieved an accuracy of 85.6%, which is better than 80.7% from ridge regression classifier—the best among the classical technique we tested. Additional sentiment information further increases accuracy to 86.3%. On our suicide notes dataset, the best classical technique—the Naíve Bayes Bernoulli classifier, achieves accuracy of 71.3%, while our classifier, incorporating semantic and sentiment information, exceeds that at 75% accuracy.

Applications of blockchain in government education sector: a comprehensive review and future research potentials

Purpose

With the time and increase in usage of information technology (IT), blockchain technology is gaining immense attention from researchers, academicians, and practitioners because of its distinctive features such as transference, security and data reliability. The purpose of this study is to endow with a systematic review of literature on blockchain in context to the government education sector in terms of its usage, benefits, obstacles and practical implementation in future areas in education.

Design/methodology/approach

The study adopted a bibliometric visualization tool to classify data in yearly publications, highly cited journals, prominent authors, leading publications in countries and institutions and highly cited papers – the data was extracted from the SCOPUS database by using relevant keywords. Thus, the following research questions were developed: How has blockchain technology been used in the government educational sector? What are the benefits examined in the field of education? What were the problems/obstacles faced using the technology in a government education structure?

Findings

The findings identify and provide a comprehensive review of the technique regarding the present research stream in terms of highest publication, author, journal, subject-wise and relevance of the technology in government education structure. Thus, the future research potential of the technology in the education sector is much more as it is in the initiation stage. A lot of opportunities and benefits need to be extracted at large.

Research limitations/implications

The present findings of the study provide a base work for government education institutions, policy developers and researchers to investigate other areas where the technology can be implemented. Finally, more technology applications will develop strategies for proper data management and cost-effective decisions.

Originality/value

This study explains the relevance of technology in education through bibliometric visualization. The study adopted the review and significance of blockchain technology in the government education sector by identifying its benefits, current scenario, application and future research potential areas.

Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow

Standing at the cusp of an augmented age facilitates a glance into the future of a cybernetic world aligned with planetary wellbeing. The era of exponential technological revolutions has brought with it a plethora of opportunities expanding in a multi-faceted dimension with an added emphasis towards nurturing a mutual synergy of nature with a daily dose of digitalization. The paper is written with an intent to lay out an accumulated comprehensive review of different literary works which lay the grounds for how different Smart Earth Technologies aid in monitoring and tackling the degradation of air and water resources. If an intertwined state-of-the-art centralized research source could be created, it would become a boon for seasoned researchers and neophytes succeeding portion of the article expands itself to a wide variety of research literature complimented with real-time models, case, and empirical studies which help heighten the previous limit to the research done on these Technologies tinkering the present monitoring systems. The primary aim of this work is to fuel the need of theoretical, practical, and empirical evolution in the ways the intelligent technologies help blossom a pollution-free environment. The secondary intention was to ensure that in-depth study of Smart Environmental Pollution the Monitoring Systems provisioned a multitude of prospects for upgrading one's knowledge on environmental management through current world technologies. By looking at these trends of the past, the enthusiast of technology could collaborate with the researchers of Environmental Pollution to assist in proliferation of diverse 'smart' solutions creating a Smarter, Greener, and Brighter future for research and developments in Sustainable Technologies devising a pollution-free environment.

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

ECG compression with Douglas-Peucker algorithm and fractal interpolation

In this paper, we propose a new ECG compression method using the fractal technique. The proposed approaches utilize the fact that ECG signals are a fractal curve. This algorithm consists of three steps: First, the original ECG signals are processed and they are converted into a 2-D array. Second, the Douglas-Peucker algorithm (DP) is used to detect critical points (compression phase). Finally, we used the fractal interpolation and the Iterated Function System (IFS) to generate missing points (decompression phase). The proposed (suggested) methodology is tested for different records selected from PhysioNet Database. The obtained results showed that the proposed method has various compression ratios and converges to a high value. The average compression ratios are between 3.19 and 27.58, and also, with a relatively low percentage error (PRD), if we compare it to other methods. Results depict also that the ECG signal can adequately retain its detailed structure when the PSNR exceeds 40 dB.