The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 92% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake... More >

Intelligent project management systems rely on high-quality historical data for accurate automated decision-making, yet missing data in software project repositories remains a persistent challenge that degrades intelligent estimation performance. This study proposes an Intelligent Decision Support Framework (IDSF) for software development effort estimation (SDEE) that integrates Multiple Imputation (MI) as a critical data quality enhancement layer within the Analogy-Based Effort Estimation (ABEE) model. The framework is evaluated on the ISBSG dataset by systematically comparing six imputation strategies. Results demonstrate that the MI-enhanced framework achieves competitive and more stable... More >

This systematic review and meta-analysis explores the integration of artificial intelligence (AI) technologies into forensic odontology from an intelligent systems perspective, with particular emphasis on enhancing identification accuracy, pattern recognition capabilities, and workflow efficiency. Traditional dental identification methods rely heavily on manual comparison of charts and radiographs, which are time-consuming and susceptible to human bias. Recent advancements in machine learning algorithms, deep learning-based image recognition networks, and intelligent decision-support systems have demonstrated significant potential in automating critical tasks such as bite-mark analysis, dent... More >

Sentiment analysis is a crucial component of intelligent information processing systems, enabling machines to understand and categorize human opinions expressed in text. While extensively studied for high-resource languages such as English and Chinese, it remains underexplored for low-resource languages like Urdu. This paper presents an intelligent multilingual sentiment analysis framework for Urdu text by integrating supervised machine learning techniques with a transformer-based model. We manually annotated and preprocessed a dataset collected from various Urdu blog websites, categorizing sentiments into positive, neutral, and negative classes. Five machine learning classifiers—Support V... More >

Cataracts are a leading cause of blindness in Pakistan, contributing to more than 54% of blindness cases in Pakistan, primarily due to poor living conditions, nutritional deficiencies, and limited healthcare access. Early detection is critical to avoid invasive treatments, but current diagnostic approaches often identify cataracts at advanced stages. This paper presents an advanced,automated cataract detection system using deep learning specifically the ResNet-50 architecture, to address this gap. The model processes fundus retinal images curated from diverse datasets, classified by ophthalmologic experts through a rigorous three-stage process. By leveraging the ResNet-50 model, cataracts ar... More >

The increasing deployment of intelligent cyber-physical systems (CPS), autonomous devices, and IoT infrastructures in Industry 4.0 has introduced complex and dynamic security challenges that static, identity-based mechanisms are no longer sufficient to address. Intelligent industrial environments demand security frameworks capable of fine-grained, context-aware, and adaptive access control that can operate at scale across heterogeneous networked systems. In response to this need, Attribute-Based Encryption (ABE) and Attribute-Based Authentication (ABA) have emerged as essential building blocks for intelligent security architectures, enabling policy-driven data protection and authentication t... More >

We investigate the use of deep learning models for retail sales forecasting in this research. Proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research evaluates deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and a hybrid CNN-LSTM model. The models are further improved by adding dense layers to process daily sales data from a major pharmaceutical company. The models are trained on 80% of the dataset and tested on the remaining 20%. Model performance is compared using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).... More >