9 research outputs found
Blockchain Applications in Retail Cybersecurity: Enhancing Supply Chain Integrity, Secure Transactions, and Data Protection
No full textBlockchain technology has proven a powerful tool for reinforcing cybersecurity in the retail sector. This research offers an extensive overview of the applications of blockchain in retail cybersecurity, particularly, underscoring supply chain integrity, data protection, and transaction security. The research explored how blockchain can facilitate provenance and traceability well as prevent counterfeiting and enhance vendor management in the supply chain. It also explores how blockchain-based payment frameworks and fraud detection systems can boost transaction security. Moreover, the study assesses the capability of blockchain to safeguard data via privacy and consent management, and secure and immutable data storage. The findings outline the capability of blockchain technology to diminish risks, enhance transparency, and affirm trust in retail cybersecurity. While challenges are inevitable, such as regulatory and scalability considerations, the research infers that blockchain technology presents noteworthy opportunities for innovation and advancement in the retail industry\u27s cybersecurity landscape
Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning
No full textWorkforce 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
Dominance of External Features in Stock Price Prediction in a Predictable Macroeconomic Environment
No full textUnderstanding the factors affecting future stock prices has been of prime importance across the globe, as accurate stock price prediction is directly related to financial gains. Its interest has been reflected by a large and growing literature trying to investigate stock price prediction with an effort to gain higher prediction accuracy. Recent literature has identified relevant external features, such as current and anticipated future macroeconomic environment-related information, and has incorporated such external features along with historical data on stock prices into the prediction models to gain improved accuracy. However, the current literature fails to quantify the relative importance of those external features for a better understanding of their relevancy. In this article, we bridge this gap and quantify the relative importance of those external features in stock price prediction by combining macroeconomic data with historical stock price data and by utilizing dominance analysis. Our results demonstrate that external features are highly dominant in the prediction of future stock prices
Transforming Breast Cancer Identification: An In-Depth Examination of Advanced Machine Learning Models Applied to Histopathological Images
No full textBreast cancer stands as one of the most prevalent and perilous forms of cancer affecting both women and men. The detection and treatment of breast cancer benefit significantly from histopathological images, which carry crucial phenotypic information. To enhance accuracy in breast cancer detection, Deep Neural Networks (DNNs) are commonly utilized. Our research delves into the analysis of pre-trained deep transfer learning models, including ResNet50, ResNet101, VGG16, and VGG19, for identifying breast cancer using a dataset comprising 2453 histopathology images. The dataset categorizes images into two groups: those featuring invasive ductal carcinoma (IDC) and those without IDC. Through our analysis of transfer learning models, we observed that ResNet50 outperformed the other models, achieving impressive metrics such as accuracy rates of 92.2%, Area under Curve (AUC) rates of 91.0%, recall rates of 95.7%, and a minimal loss of 3.5%
Optimizing E-Commerce Profits: A Comprehensive Machine Learning Framework for Dynamic Pricing and Predicting Online Purchases
No full textIn the online realm, pricing transparency is crucial in influencing consumer decisions and driving online purchases. While dynamic pricing is not a novel concept and is widely employed to boost sales and profit margins, its significance for online retailers is substantial. The current study is an outcome of an ongoing project that aims to construct a comprehensive framework and deploy effective techniques, leveraging robust machine learning algorithms. The objective is to optimize the pricing strategy on e-commerce platforms, emphasizing the importance of selecting the right purchase price rather than merely offering the cheapest option. Although the study primarily targets inventory-led e-commerce companies, the model\u27s applicability can be extended to online marketplaces that operate without maintaining inventories. The study endeavors to forecast purchase decisions based on adaptive or dynamic pricing strategies for individual products by integrating statistical and machine learning models. Various data sources capturing visit attributes, visitor details, purchase history, web data, and contextual insights form the robust foundation for this framework. Notably, the study specifically emphasizes predicting purchases within customer segments rather than focusing on individual buyers. The logical progression of this research involves the personalization of adaptive pricing and purchase prediction, with future extensions planned once the outcomes of the current study are presented. The solution landscape for this study encompasses web mining, big data technologies, and the implementation of machine learning algorithms
Advanced Cybercrime Detection: A Comprehensive Study on Supervised and Unsupervised Machine Learning Approaches Using Real-world Datasets
No full textIn the ever-evolving field of cybersecurity, sophisticated methods—which combine supervised and unsupervised approaches—are used to tackle cybercrime. Strong supervised tools include Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), while well-known unsupervised methods include the K-means clustering model. These techniques are used on the publicly available StatLine dataset from CBS, which is a large dataset that includes the individual attributes of one thousand crime victims. Performance analysis shows the remarkable 91% accuracy of SVM in supervised classification by examining the differences between training and testing data. K-Nearest Neighbors (KNN) models are quite good in the unsupervised arena; their accuracy in detecting criminal activity is impressive, at 79.56%. Strong assessment metrics, such as False Positive (FP), True Negative (TN), False Negative (FN), False Positive (TP), and False Alarm Rate (FAR), Detection Rate (DR), Accuracy (ACC), Recall, Precision, Specificity, Sensitivity, and Fowlkes–Mallow\u27s scores, provide a comprehensive assessment
Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining
No full textThe airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies
Investigation of distal repetitive sequences in the genus allium
No full textPhDThe telomere is a DNA/protein structure required to maintain the ends of linear chromosomes.
Usually the DNA component comprises a highly conserved tandemly repeated
minisatellite sequence. In most plants the minisatellite sequence is typically present in several
hundred copies at each chromosome end, and is extended primarily by telomerase, which
adds telomere repeats to the 3’ end. In the plant genus Allium, which contains around 700
species, there is an absence of typical telomeric DNA repeats. It is of great interest to determine
what sequence or sequences have replaced the ancestral repeats and how they are
lengthened.
A range of molecular cloning methods were used to isolate candidate telomere sequences
from the genomes of two diverged species, Allium cernuum and Allium cepa. I analyse
several putative telomere sequences, isolated in this work and by others, but no proven
candidate sequence has emerged. Nevertheless, one of those sequences, 35S ribosomal DNA
(rDNA) encoding 35S rRNA, proved to have a structure that is previously not described for
plants. I show that some units have a Ty1/copia retrotransposon fragment in the intergenic
spacer region. Sequence analysis indicates that there was a single insertion followed by
amplification, probably involving homogenisation mechanisms. Furthermore, I show high
levels of rDNA length heterogeneity and rDNA unit divergence both within species and
across the genus, respectively
Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
No full textThe robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends
