{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:34:30Z","timestamp":1776976470055,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Copper-aluminum composite conductors are suitable for modern electrical applications due to their high electrical conductivity and lightweight nature; however, predicting their performance in changing environmental and electrical conditions is difficult and expensive using conventional techniques. Problem Statement: Previous models ignore the combined effects of copper-aluminum ratio, tensile strength, temperature, and current load; furthermore, metrics such as accuracy and F1-score cannot accurately reflect physical performance characteristics. A new Copper-Aluminum Feature-Weighted Ensemble (CAFWE) classifier was developed using the CopperAluminum_WirePerformance_Dataset containing 5000 samples with 10 input features and one target output (performance_level). The model integrates three base learners \u2014 Linear Classifier, k-Nearest Neighbor (k = 5), and a Decision-Stump classifier \u2014 combined through a weighted voting mechanism that assigns higher weights to copper_percentage, tensile_strength, and electrical_conductivity based on feature-sensitivity analysis. The dataset was partitioned using an 80:20 stratified split, and all results were averaged over five repeated experiments to ensure stability. Five domain-specific evaluation metrics were introduced: Conductivity Accuracy (CA), Strength Reliability (SR), Temperature Adaptation Score (TAS), Load Prediction Stability (LPS), and Composite Material Alignment (CMA), enabling alignment with real-world engineering behavior. Results: Across five independent training runs, CAFWE achieved consistent performance, with mean scores of CA = 93.5%, SR = 91.2%, TAS = 89.8%, LPS = 90.6%, and CMA = 92.4%, demonstrating superior predictive reliability under varying material and operational conditions. Feature importance analysis confirmed copper_percentage and electrical_conductivity as the most influential contributors to final predictions. The CAFWE model accurately and interpretably predicts copper-aluminum conductor performance; and provides a scalable framework to optimize hybrid material design for smart grid applications.<\/jats:p>","DOI":"10.31449\/inf.v50i11.12882","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:14Z","timestamp":1776972974000},"source":"Crossref","is-referenced-by-count":0,"title":["CAFWE: A Feature-Weighted Ensemble Classifier for Predicting Performance of Copper-Aluminum Composite Conductors"],"prefix":"10.31449","volume":"50","author":[{"given":"Yinghan","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congrui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shibo","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,23]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12882\/6659","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12882\/6659","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:36:14Z","timestamp":1776972974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,4,23]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i11.12882","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}