Engineering Journal: Volume-10, Issue-7, July, 2024

Abstract— Identification of lncRNA-protein interactions is important for understanding the biological functions and molecular mechanisms of lncRNAs. In this study, we proposed a computational model for predicting lncRNA-protein interactions based on Graphlet interactions to find potential LPIs (GILPI). First, five LPI datasets were collected. Second, vector features of lncRNAs and proteins were extracted from the sequence data by pyfeat and BioTriangle, respectively. Third, these features were subjected to Pearson's correlation coefficient to calculate the similarity between lncRNAs and the similarity between proteins. Fourth, the Jaccard similarity between lncRNAs and proteins was calculated based on the LPI network, and then the corresponding Pearson similarity and Jaccard similarity were taken as the average value of the final lncRNA-lncRNA similarity and protein-protein similarity to construct the network. Finally, lncRNA-protein classification prediction was performed on both networks. Comparing GILPI with five state-of-the-art LPI prediction methods through 5-fold cross-validation, the results show that the GILPI prediction model has strong LPI classification performance. The case studies show that there may be interactions between NONHSAT021830 and Q9H9S0, n385685 and Q07955, and NONHSAT098243 and P25490.The novelty of GILPI is that it integrates the two similarities to construct a network, and then utilizes Graphlet interactions on the network to directly and indirectly link the features to mine out potential features, thus greatly improving the performance of the model.
Keywords— Graphlet interaction, Jaccard similarity, Pearson similarity, lncRNA-protein interaction.

Abstract— Investigations into the machinability of Al6063 alloy reinforced with TiO2 particles typically focus on understanding how the addition of TiO2 affects the machining characteristics of the metal matrix material compared to the base Al6063 alloy. Here are some aspects that such studies would typically explore: Tool Wear, cutting forces, Material removal rate, surface roughness. These investigations are crucial for understanding how the addition of TiO2 particles modifies the machinability of Al6063 alloy and for optimizing machining processes to ensure efficient production of components with desirable mechanical and surface properties. Keywords— DoE, MRR, Process parameters, Resultant Force.

Abstract— The proliferation of electronic transactions has heightened the vulnerability to credit card fraud, demanding more robust detection methodologies. This paper introduces DT-CNN, an innovative hybrid model that integrates a Decision Tree (DT) and a Convolutional Neural Network (CNN) to enhance the accuracy* and efficiency of fraud detection significantly. By leveraging decision trees' interpretability and CNNs’ pattern recognition capabilities, DT-CNN offers a comprehensive approach to identifying fraudulent transactions. Unlike conventional models, DT-CNN adeptly addresses challenges related to precision* and recall*, achieving notable performance metrics in real-world datasets prone to biases. The hybrid model's architecture enables effective learning from vast and intricate datasets. This study builds upon previous research by advancing techniques in feature engineering, dataset balancing, and overfitting mitigation, positioning DT-CNN as a dependable solution for combating fraud. Detailed insights into its architecture, training methodology, and performance evaluation further underscore DT-CNN's effectiveness in combating credit card fraud. Keywords— Convolutional Neural Network, Credit Card Fraud Detection, Decision Trees

Abstract— This comprehensive study addresses the critical challenges of providing clean water and proper sanitation in rural Afghanistan. Despite significant international aid and various governmental initiatives, rural communities face persistent issues related to water scarcity, contaminated sources, and inadequate sanitation facilities. These challenges are compounded by political instability, cultural practices, and economic constraints. The hypothesis posits that integrated, community-driven, and sustainable interventions are essential to overcome these obstacles. The objectives include identifying key barriers, evaluating current initiatives, and proposing viable strategies for improvement. A thorough literature review underpins the study, highlighting the severity and complexity of the crisis. Research methods incorporate qualitative approaches such as interviews, focus groups, and observational studies. Data analysis focuses on thematic patterns and practical gaps. The conclusion emphasizes the need for coordinated efforts and continuous monitoring to ensure long-term success. Keywords— Sustainable development, Clean water, Water scarcity, and Rural Afghanistan.