Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

Chronic Diseases Prediction using Machine Learning and Deep Learning Methods

Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior performance, particularly in capturing complex data patterns. The findings highlight the potential of ML and DL in revolutionizing chronic disease prediction, enabling early diagnosis and personalized treatment strategies. However, challenges such as data quality, model interpretability, and the need for advanced computational techniques in healthcare to improve patient outcomes and reduce the burden of chronic diseases. This study was conducted as part of Big Data class project under the supervision of our professors Mr. Abderrahmane EZ-ZAHOUT and Mr. Abdessamad ESSAIDI.

Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests

Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This study investigates two key survival models: the Functional Survival Tree (FST) and the Functional Random Survival Forest (FRSF). It introduces novel methods and tools to enhance the interpretability of FST models and improve the explainability of FRSF ensembles. Using both real and simulated datasets, the results demonstrate that the proposed approaches yield efficient, easy-to-understand decision trees that accurately capture the underlying decision-making processes of the model ensemble.

Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion

With the timely formation of personalized intervention plans based on high-dimensional heterogeneous time series information becoming an important challenge in the medical field today, electronic medical records, wearables, and other multi-source medical data are increasingly generated and diversified. In this work, we develop a system to generate personalized medical intervention strategies based on Group Relative Policy Optimization (GRPO) and Time-Series Data Fusion. First, by incorporating relative policy constraints among the groups during policy gradient updates, we adaptively balance individual and group gains. To improve the robustness and interpretability of decision-making, a multi-layer neural network structure is employed to group-code patient characteristics. Second, for the rapid multi-modal fusion of multi-source heterogeneous time series, a multi-channel neural network combined with a self-attention mechanism is used for dynamic feature extraction. Key feature screening and aggregation are achieved through a differentiable gating network. Finally, a collaborative search process combining a genetic algorithm and Monte Carlo tree search is proposed to find the ideal intervention strategy, achieving global optimization. Experimental results show significant improvements in accuracy, coverage, and decision-making benefits compared with existing methods.

AI-Based Vulnerability Analysis of NFT Smart Contracts

With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts. We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn. Python-processed data was structured into training/test sets. Using the CART algorithm with Gini coefficient evaluation, we built initial decision trees for feature extraction. A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration. GridSearch hyperparameter tuning further optimized the model, with 3D visualizations demonstrating parameter impacts on vulnerability detection. Results show the random forest model excels in detecting all five vulnerabilities. For example, it identifies Risky Mutable Proxy by analyzing authorization mechanisms and state modifications, while ERC-721 Reentrancy detection relies on external call locations and lock mechanisms. The ensemble approach effectively reduces single-tree overfitting, with stable performance improvements after parameter tuning. This method provides an efficient technical solution for automated NFT contract detection and lays groundwork for scaling AI applications.

Partition Map-Based Fast Block Partitioning for VVC Inter Coding

Among the new techniques of Versatile Video Coding (VVC), the quadtree with nested multi-type tree (QT+MTT) block structure yields significant coding gains by providing more flexible block partitioning patterns. However, the recursive partition search in the VVC encoder increases the encoder complexity substantially. To address this issue, we propose a partition map-based algorithm to pursue fast block partitioning in inter coding. Based on our previous work on partition map-based methods for intra coding, we analyze the characteristics of VVC inter coding, and thus improve the partition map by incorporating an MTT mask for early termination. Next, we develop a neural network that uses both spatial and temporal features to predict the partition map. It consists of several special designs including stacked top-down and bottom-up processing, quantization parameter modulation layers, and partitioning-adaptive warping. Furthermore, we present a dual-threshold decision scheme to achieve a fine-grained trade-off between complexity reduction and rate-distortion (RD) performance loss. The experimental results demonstrate that the proposed method achieves an average 51.30% encoding time saving with a 2.12% Bjontegaard Delta Bit Rate (BDBR) under the random access configuration.

Boosting Classifier Performance with Opposition-Based Data Transformation

In this paper, we introduce a novel data transformation framework based on Opposition-Based Learning (OBL) to boost the performance of traditional classification algorithms. Originally developed to accelerate convergence in optimization tasks, OBL is leveraged here to generate synthetic opposite samples that replace the acutely training data and improve decision boundary formation. We explore three OBL variants; Global OBL, Class-Wise OBL, and Localized Class-Wise OBL; and integrate them with several widely used classifiers, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Logistic Regression (LR), and Decision Tree (DT). Extensive experiments conducted on 26 heterogeneous and high-dimensional datasets demonstrate that OBL-enhanced classifiers consistently outperform their standard counterparts in terms of accuracy and F1-score, frequently achieving near-perfect or perfect classification. Furthermore, OBL contributes to improved computational efficiency, particularly in SVM and LR. These findings underscore the potential of OBL as a lightweight yet powerful data transformation strategy for enhancing classification performance, especially in complex or sparse learning environments.

An Extended Horizon Tactical Decision-Making for Automated Driving Based on Monte Carlo Tree Search

This paper introduces COR-MCTS (Conservation of Resources - Monte Carlo Tree Search), a novel tactical decision-making approach for automated driving focusing on maneuver planning over extended horizons. Traditional decision-making algorithms are often constrained by fixed planning horizons, typically up to 6 seconds for classical approaches and 3 seconds for learning-based methods limiting their adaptability in particular dynamic driving scenarios. However, planning must be done well in advance in environments such as highways, roundabouts, and exits to ensure safe and efficient maneuvers. To address this challenge, we propose a hybrid method integrating Monte Carlo Tree Search (MCTS) with our prior utility-based framework, COR-MP (Conservation of Resources Model for Maneuver Planning). This combination enables long-term, real-time decision-making, significantly enhancing the ability to plan a sequence of maneuvers over extended horizons. Through simulations across diverse driving scenarios, we demonstrate that COR-MCTS effectively improves planning robustness and decision efficiency over extended horizons.

Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects

Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and calibration. Many existing tree-based causal inference techniques, however, exhibit high estimation errors when applied to observational data because they struggle to capture complex interactions among factors and rely on static regularization schemes. In this work, we propose Dynamic Regularized Causal Boosted Decision Trees (CBDT), a novel framework that integrates variance regularization and average treatment effect calibration into the loss function of gradient boosted decision trees. Our approach dynamically updates the regularization parameters using gradient statistics to better balance the bias-variance tradeoff. Extensive experiments on standard benchmark datasets and real-world clinical data demonstrate that the proposed method significantly improves estimation accuracy while maintaining reliable coverage of true treatment effects. In an intensive care unit patient triage study, the method successfully identified clinically actionable rules and achieved high accuracy in treatment effect estimation. The results validate that dynamic regularization can effectively tighten error bounds and enhance both predictive performance and model interpretability.

Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework

We present a novel framework that bridges the gap between the interpretability of decision trees and the advanced reasoning capabilities of large language models (LLMs) to predict startup success. Our approach leverages chain-of-thought prompting to generate detailed reasoning logs, which are subsequently distilled into structured, human-understandable logical rules. The pipeline integrates multiple enhancements - efficient data ingestion, a two-step refinement process, ensemble candidate sampling, simulated reinforcement learning scoring, and persistent memory - to ensure both stable decision-making and transparent output. Experimental evaluations on curated startup datasets demonstrate that our combined pipeline improves precision by 54% from 0.225 to 0.346 and accuracy by 50% from 0.46 to 0.70 compared to a standalone OpenAI o3 model. Notably, our model achieves over 2x the precision of a random classifier (16%). By combining state-of-the-art AI reasoning with explicit rule-based explanations, our method not only augments traditional decision-making processes but also facilitates expert intervention and continuous policy refinement. This work lays the foundation for the implementation of interpretable LLM-powered decision frameworks in high-stakes investment environments and other domains that require transparent and data-driven insights.