56 results for “MLP”

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Research ArticleOpen accessIn the abstractCombustion Analysis of a CI Engine Performance Using Waste Cooking Biodiesel Fuel with an Artificial Neural Network AidGholamhassan NAJAFI, Barat GHOBADIAN, Talal F. YUSAF, Hadi RAHIMIA comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel … …American Journal of Applied Sciences, Vol. 4, Issue 10 · 31 Oct 2007 · 8,192 accessesView PDF Cite

Research ArticleOpen accessIn the abstractPredicting Antigenic Peptides Using a Multi-Level Transformer Model with Enhanced Feature SelectionAshwini S., Minu R. I., Jeevan KumarThis Antigenic Peptide (APs) prediction is one of the most important roles to improve vaccine design and interpret immune responses. This paper develops a Multi-Level Pooling-based Transformer model, which improves the accuracy and efficiency of predicting T-cell epitopes. The model utilizes peptide sequences from the Immune Epitope Database, employing a refined Kolaskar and Tongaonkar algorithm for feature extraction and a … …Journal of Computer Science, Vol. 21, Issue 7 · 17 Jul 2025 · 3,443 accessesView PDF Cite

Research ArticleOpen accessIn the abstractXAI_MLPCNN: A Novel Explainable AI-Based Deep Learning Framework for Stress IdentificationFateh Bahadur Kunwar, Rakesh Kumar Yadav, Hitendra Singh, Nitin TripathiOver the past ten years, there has been a lot of emphasis focusedon the development of Artificial Intelligence (AI) and Machine Learning(ML)-based mental health treatments. To increase practitioners' and patients'trust in AI applications, AI systems need to explain their actions. This iscalled Explainable AI (XAI). While significant progress has been achievedin stress prediction models, XAI has not advanced as much. … …Journal of Computer Science, Vol. 21, Issue 5 · 6 May 2025 · 6,161 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDiagnosis of Hepatitis Disease with Logistic Regression and Artificial Neural NetworksAlaa M. Elsayad, Ahmed M. Nassef, Mujahed Al-DhaifallahHepatitis C refers to the inflammatory state of the liver caused by viruses, bacteria, fungi, and exposure to toxins such as alcohol and self-immunity. The diagnosis requires investigating many laboratory tests and comparing the results to those of the former patients with the same conditions. This study presents the results of our experiments to build a hybrid system that combines … …Journal of Computer Science, Vol. 16, Issue 3 · 27 Mar 2020 · 7,466 accessesView PDF Cite

Research ArticleOpen accessIn the abstractUNUSUAL SPECTRUM OF GENETIC PATHOLOGIES AND NOVEL MUTATIONS IN PWS AND AS PATIENTS DETECTED BY A WIDE CLUSTER OF METHODSLivia Kotysova, Robert Petrovic, Jan ChandogaPrader-Willi and Angelman syndromes are clinically distinct neurodevelopmental genetic disorders that map to 15q11.2-q13 locus. The common phenotypes are attributable to loss of expression of parentally specific imprinted genes inside this region, where the gene function is dependent on parental origin. Initial diagnosis was proved for the years by methylation pattern analyses of the SNRPN exon 1/promoter region within the … …OnLine Journal of Biological Sciences, Vol. 14, Issue 3 · 12 Nov 2014 · 7,006 accessesView PDF Cite

Research ArticleOpen accessIn the abstractVulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction MethodAhmed M.A. Haidar, Azah Mohamed, Aini HussainVulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of … …American Journal of Applied Sciences, Vol. 5, Issue 6 · 30 Jun 2008 · 7,153 accessesView PDF Cite