M. Feizi-Derakhshi

QLOA: A Self-Adaptive Q-Learning-Based Optimization Algorithm with Dynamic Mathematical Formulation Selection

PLOT: Phrase-Based Language Model Optimization for Event Detection in Text Streams

Event detection from text streams is a fundamental problem in natural language processing, with applications ranging from social monitoring to information retrieval. In Persian, the task is further complicated by the scarcity of labeled resources, the informal and highly dynamic nature of user-generated content, and the rich morphological and orthographic diversity of the language. Most existing approaches represent events as collections of isolated words, discarding the compositional semantic structure through which real-world events—particularly emerging ones—are most naturally expressed. We propose an unsupervised, phrase-based framework for event detection from Persian social media streams, in which semantically meaningful phrases serve as the fundamental units of event representation. Text segmentation is reformulated as an optimization problem, and the central contribution of this work is the definition of its objective function: rather than relying on traditional statistical resources, pre-trained language models are employed directly as the scoring backbone of the optimization process. The framework is instantiated and compared under three configurations—a statistical n-gram model built from Wikipedia, the multilingual model mBERT, and the Persian-specific model ParsBERT—enabling a controlled analysis of how the nature and capacity of the scoring function affect event detection quality. Experiments on a Persian Telegram dataset demonstrate that, under a unified evaluation protocol applied identically to all methods, the proposed framework outperforms word-based approaches and established baselines on both clustering coherence and topic recall, although no single parameter configuration of the proposed method maximizes both metrics simultaneously, and the reported gains are therefore metric-dependent. The language model–based configurations, in particular, show superior ability to identify novel and previously unseen phrases, owing to their capacity to evaluate semantic coherence independently of surface frequency. Beyond event detection, the principle of employing language model output distributions as formal objective functions within optimization algorithms represents a generalizable design paradigm with potential applicability to a broader class of natural language processing tasks.

Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review of Deep Learning Models for Early-Stage Disease Classification.

INTRODUCTION Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which early diagnosis-particularly the accurate prediction of conversion from mild cognitive impairment (MCI) to AD-is essential to enable timely and effective therapeutic interventions. Deep learning (DL) models have demonstrated substantial promise in this domain; however, critical challenges persist, including multiclass staging of disease progression, longitudinal data modeling, and effective multimodal data integration. This systematic review provides a critical appraisal of DL-based approaches for predicting MCI-to-AD conversion, with particular emphasis on these three key challenges. METHODS This systematic review was designed and conducted in accordance with the PRISMA guidelines. A comprehensive literature search was performed across PubMed, Scopus, IEEE Xplore, and Web of Science for articles published between January 1, 2019, and February 20, 2026. Following rigorous screening of titles, abstracts, and full texts, 60 studies were included that employed deep learning models for AD stage classification and/or MCI-to-AD conversion prediction. Data were extracted and synthesized regarding study design, datasets, input modalities, DL architectures, and classification tasks. RESULTS Cross-sectional approaches remained predominant (47 studies), while longitudinal designs were less common (13 studies) and showed heterogeneous but promising performance for MCI-to-AD conversion prediction. Heavy reliance on the ADNI dataset (42 studies) represents a major limitation to generalizability. Multimodal models were used in 24 studies and often reported strong performance, particularly for challenging MCI-related tasks; however, direct cross-study comparisons should be interpreted cautiously because of substantial heterogeneity in datasets, prediction tasks, validation strategies, and methodological quality. Convolutional neural networks (CNNs) dominated neuroimaging-based modeling (21 studies), while recurrent neural networks (RNNs) (4 studies) and transformers (4 studies) have emerged for capturing longitudinal dependencies and global relationships. The binary pMCI vs. sMCI classification proved the most challenging task (accuracy range: 71.71-96.3%), with performance declining as the number of classes increased in multiclass settings. Key limitations across studies include lack of diverse datasets, overfitting, and poor model interpretability. CONCLUSION Deep learning models hold considerable potential for predicting MCI-to-AD conversion, yet substantial barriers remain to their translation into routine clinical practice. Greater emphasis on longitudinal analysis, intelligent multimodal fusion, and interpretable architectures is essential for clinical impact. Future research should prioritize the development of diverse, multicenter datasets, advancement of explainable AI (XAI) techniques, and the design of personalized time-to-event models. This review offers a comprehensive roadmap to guide subsequent investigations toward more accurate, reliable, and clinically actionable diagnostic tools in Alzheimer's disease.

A Lightweight Security Decision Framework for IoT Intrusion Detection Using Entropy-Guided Feature Integrity and Adaptive Ensemble Learning

This paper presents a lightweight IoT security decision framework that combines entropy-guided feature selection with an adaptive ensemble-based intrusion detection strategy, establishing intrusion detection as an efficient and deployable security decision layer for real-world IoT environments.