14th International Conference on Soft Computing, Artificial Intelligence and Applications (SAI 2025)

May 17~ 18, 2025 Zurich, Switzerland

Accepted Papers


Autoencoder for Image Classification with Genetics Algorithms

John Tsiligaridis, Department of Math and Computer Science, Heritage University, Toppenish, WA, USA

ABSTRACT

Autoencoders (AEs) are Deep Learning (DL) models that are well known for their ability to compress and reconstruct data. When an AE compresses input data, a latent space is created which yields a compressed representation of the original data with a smaller set of features. Genetic Algorithms (GAs) based on evolutionary principles can be used to optimize various hyperparameters of a DL model. This work involves two tasks. First, it focuses on the application of an AE on image data along with various configurations of the AE structure and its constituent encoder/decoder structure using Multi-Layer Perceptrons (MLPs). Visualizations of the AE loss functions during training are provided, along with various latent space results obtained using clustering techniques. The second focus of the paper is on the application of the GA on a Convolutional AE where optimization of the Convolutional Neural Networks (CNN) encoder/decoder structures is done by converting the architecture into genes for image classification. We see that the AE is a flexible and robust model that can successfully be applied on a variety of image datasets and the GA model surpasses the AE model.

Keywords

Machine Learning, Deep Learning, Autoencoders, Genetic Algorithms.


Transforming E-Commerce With AI Agents: A Technical Deep Dive

Sai Kiran Padmam, Partha Sarthi Samal,independent,United States of America

ABSTRACT

Online retail has come a long way since its early days of static web pages and manual price comparisons. The new frontier embraces artificial intelligence (AI) to interpret user queries, mediate real-time auctions among multiple vendors, and deliver personalized recommendations at blazing speeds. This paper highlights how such AI agents operate under the hood, drawing upon machine learning, reinforcement learning, and multi-agent coordination principles. We also offer glimpses into emerging research challenges and future directions that may reshape online shopping entirely.


From Genes to Insights: A Causal Framework for Diabetes Genomics and Predictive Analytics

Sheresh Zahoor1, Pietro Li`o1, Ga ̈el Dias2, and Mohammed Hasanuzzaman3, 1University of Cambridge, 2Normandie Univ, GREYC, 3Queens University Belfast

ABSTRACT

Diabetes is a global health crisis, demanding advanced genomic approaches to uncover molecular mechanisms and identify therapeutic targets. This study introduces the Genomic Causal Framework (GCF), a novel approach combining genomic data analysis, causal modeling, and predictive analytics to provide actionable insights into diabetes pathology. These include identifying potential therapeutic targets, such as CXCL8, S100A8, and COL1A1, implicated in chronic inflammation and complications like diabetic nephropathy. The framework also highlights regulatory genes, such as ROBO1 and FCGR2A, as upstream drivers of disease progression. Using the Diabetes genome dataset (GSE132831), we identify differentially expressed genes (DEGs) with pyDESeq2, stratifying upregulated and downregulated genes. These DEGs form the basis for constructing a protein-protein interaction (PPI) network, revealing critical functional pathways. The GCF framework integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to move beyond prediction and enable causal reasoning. CBNs model causal relationships between genes and diabetic outcomes, while PTrees quantify their impact. Achieving 82.22% accuracy and 95% recall, GCF ensures reliable patient identification, with SHAP analysis enhancing interpretability and biological relevance. Its integration of causal reasoning with predictive analytics prioritises biologically relevant features for clinical and research applications. By bridging causal inference with functional genomics, this study advances biomarker discovery and therapeutic target identification, providing a powerful tool for precision medicine in Type 2 Diabetes. Unlike traditional machine learning, our approach enhances interpretability while uncovering critical insights into disease development and progression.

Keywords

Diabetes, Causal Bayesian Network, Probability Trees, Genomics.

How New Technology and ICTS can Be used in Case of Teaching Algorithms

Kostas Dimitrios1 and Kostas Ioannis2, 1National and Kapodistrian University of Athens, 2University of Piraeus

ABSTRACT

In this article we will analyse how Information and communication technologies can be used in teaching Algorithms Information and communication technologies use algorithms can help create numerous applications that can solve a wide variety of problems.by University students. There are many applications in Universities specifically in laboratories teachers and students use algorithms to solve numerical and algebraic system problems. In Partial differential equations, we use Numerical Analysis to solve a system of PDEs. In research, also most people use Algorithms to validate their theoretical findings. Students need to use suitable algorithms to solve problems. Nowadays many people use Algorithms to solve problems in their companies or in their organisation and in every context, algorithms are widely used. This work is about teaching algorithms used to solve problems using the numerical analysis methods.

Keywords

Algorithms, Innovative, technology, Analysis.

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