25 research outputs found

    Factors Influencing Blockchain-based Mobile Banking Adoption: Evidence from a Developing Country

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    This study attempts to explain the factors influencing blockchain-based mobile banking acceptance in Bangladesh. Based on a technology acceptance framework termed UTAUT2 (unified theory of acceptance and use of technology 2), an enhanced model with a mediating variable is built for this research. Data were collected from the first-ever blockchain-based mobile banking stakeholders in Bangladesh called 'UPAY' by applying a structured questionnaire. Structural equation modeling was then processed using Smart-PLS. There are eight direct hypotheses and one mediating hypothesis in this research. The findings reveal that all of the direct hypotheses except the impact of social influence on the behavioural intention (BI) to use blockchain are statistically significant. The mediating role of BI in the connection between facilitating conditions (FC) and actual blockchain use is also supported. The combination of FC and BI contributes to 88.8% of the variation in blockchain usage behaviour for mobile banking adoption. The findings of this study can help banking regulators devise a strategy for engaging a significant number of banks to create a blockchain-based mobile banking platform Keywords: Blockchain use behaviour; Mobile banking, PLS-SEM; UPAY; UTAUT

    Clustered regularly interspaced short palindromic repeats cas systems: a comprehensive review

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    The clustered regularly interspaced short palindromic repeats (CRISPR) system was recently identified as a bacterial defense mechanism against phages and plasmids. The CRISPR system is composed of DNA arrays containing short sequences identical to those present in phages and plasmids. These short DNAs are transcribed and processed by CRISPR associated proteins that also guide other CRISPR proteins to target the invading DNA. Only a few of the CRISPR components have been characterized to date, and their mechanism of action is still largely unknown. Phage defense mechanisms probably have co-evolved against the CRISPR system, but none has yet been found. We propose to identify phage genes that counteract the CRISPR system

    Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective

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    Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management

    Revitalizing the Electric Grid: A Machine Learning Paradigm for Ensuring Stability in the U.S.A

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    The electric grid entails a diverse range of components with pervasive heterogeneity. Conventional electricity models in the U.S.A. encounter challenges in terms of affirming the stability and security of the power system, particularly, when dealing with unexpected incidents. This study explored various electric grid models adopted in various nations and their shortcomings. To resolve these challenges, the research concentrated on consolidating machine learning algorithms as an optimization strategy for the electricity power grid. As such, this study proposed Ensemble Learning with a Feature Engineering Model which exemplified promising outputs, with the voting classifier performing well as compared to the rainforest classifier model. Particularly, the accuracy of the voting classifier was ascertained to be 94.57%, illustrating that approximately 94.17% of its predictions were correct as contrasted to the Random Forest. Besides, the precision of the voting classifier was ascertained to be 93.78%, implying that it correctly pinpointed positive data points 93.78% of the time. Remarkably, the Voting Classifier for the Ensemble Learning with Feature Engineering Model technique surpassed the performance of most other techniques, demonstrating an accuracy rate of 94.57%. These techniques provide protective and preventive measures to resolve the vulnerabilities and challenges faced by geographically distributed power systems

    Dominance of AI and Machine Learning Techniques in Hybrid Movie Recommendation System Applying Text-to-number Conversion and Cosine Similarity Approaches

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    This research explored movie recommendation systems based on predicting top-rated and suitable movies for users. This research proposed a hybrid movie recommendation system that integrates both text-to-number conversion and cosine similarity approaches to predict the most top-rated and desired movies for the targeted users. The proposed movie recommendation employed the Alternating Least Squares (ALS) algorithm to reinforce the accuracy of movie recommendations. The performance analysis and evaluation were undertaken by employing the widely used "TMDB 5000 Movie Dataset" from the Kaggle dataset. Two experiments were conducted, categorizing the dataset into distinct modules, and the outcomes were contrasted with state-of-the-art models. The first experiment attained a Root Mean Squared Error (RMSE) of 0.97613, while the second experiment expanded predictions to 4800 movies, culminating in a substantially minimized RMSE of 0.8951, portraying a 97% accuracy enhancement. The findings underscore the essence of parameter selection in text-to-number conversion and cosine and the gap for other systems to maintain user preferences for comprehensive and precise data gathering. Overall, the proposed hybrid movie recommendation system demonstrated promising results in predicting top-rated movies and offering personalized and accurate recommendations to users

    Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA

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    Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce.  The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions

    Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning

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    Workforce performance prediction plays an instrumental role in human resource management since it facilitates pinpointing and nurturing high-performing staff, fortifying employee planning, and boosting overall productivity. This study presents a consolidated approach that integrates business analytics and machine learning methodology to forecast personnel performance. The proposed model leverages data-driven info from distinct sources, entailing performance metrics, staff data, and contextual factors, to tailor accurate predictive models. The study examined different aspects of data analytics such as feature engineering, data preprocessing, model selection, and evaluation metrics. The findings of this report demonstrate the efficiency of the consolidated approach in forecasting workforce performance, therefore presenting valuable insights for companies to make informed decisions associated with talent management and resource allocation

    Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market

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    In the recent past, algorithmic trading has become exponentially predominant in the American stock market. The principal objective of this research was to explore the employment of machine learning frameworks in formulating algorithmic trading strategies tailored for the US stock market. For this investigation, an array of software tools was employed, comprising the Pandas library for data manipulation and analysis, the Python programming language, the Scikit-learn library for machine learning algorithms and analysis metrics, and the LIME library for explainable AI. In this study, the researcher gathered an extensive dataset from the Amazon Stock Exchange, spanning from October 19, 2018, to October 16, 2022. The dataset comprised a wide range of parameters related to Amazon's stock data, facilitating a rigorous analysis of its market performance. Five models were subjected to the experiment, notably Ridge Regression, Ada-Boost, Light-GBM, XG-Boost, Linear Regression, and Cat-Boost. From the experiment result, it was evident that the XG-Boost attained the highest R-squared (99.24%) and accuracy (99.23%) among all the algorithms. From the above results, the analyst inferred that the XG-Boost was able to learn a more complex and accurate model of the stock exchange data compared to the other algorithms. XG-Boost algorithm can be utilized to back-test distinct trading strategies on historical data, enabling investors to evaluate their efficiency before risking real capital. By assessing a wide array of factors, the XG-Boost algorithm can assist investors in selecting stocks with a higher probability of outperforming the market
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