CORTEXA
← Browse
crossrefFuture Internet2023-08-28Cited by 28

Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques

Ishaani Priyadarshini

Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual’s educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual’s ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN–LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU–CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models.

View free PDFSource page

Related papers

crossrefFuture Internet2026-03-07

Sentiment Classification of Amazon Product Reviews Based on Machine and Deep Learning Techniques: A Comparative Study

Eman Daraghmi, Noora Zyadeh

Sentiment classification plays a crucial role in analyzing customer feedback to identify market trends, enhance product recommendations, and improve customer satisfaction. This study focuses on sentiment analysis of Amazon reviews using two major datasets—Fine Food Reviews and Un…

View free PDFSource page
crossrefFuture Internet2024-11-17Cited by 3

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Abbas Kubba, Hafedh Trabelsi, Faouzi Derbel

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in…

View free PDFSource page
crossrefFuture Internet2024-12-23Cited by 47

Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques

Hesham Kamal, Maggie Mashaly

Network and cloud environments must be fortified against a dynamic array of threats, and intrusion detection systems (IDSs) are critical tools for identifying and thwarting hostile activities. IDSs, classified as anomaly-based or signature-based, have increasingly incorporated de…

View free PDFSource page
crossrefFuture Internet2025-05-27Cited by 3

Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks

Rasendram Muralitharan, Upul Jayasinghe, Roshan G. Ragel, Gyu Myoung Lee

The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long distances. This can adversely affect telecommunication systems, as cells with similar frequencie…

View free PDFSource page
crossrefFuture Internet2020-09-30Cited by 101

Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems

Niraj Thapa, Zhipeng Liu, Dukka B. KC, Balakrishna Gokaraju, Kaushik Roy

The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detect…

View free PDFSource page
crossrefFuture Internet2025-09-26Cited by 5

Evaluation Study of Pavement Condition Using Digital Twins and Deep Learning on IMU Signals

Luis-Dagoberto Gurrola-Mijares, José-Manuel Mejía-Muñoz, Oliverio Cruz-Mejía, Abraham-Leonel López-León, Leticia Ortega-Máynez

Traditional road asset management relies on periodic, often inefficient, inspections. Digital Twins offer a paradigm shift towards proactive, data-driven maintenance by creating a real-time virtual replica of physical infrastructure. This paper proposes a comprehensive, formalize…

View free PDFSource page