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AI-Based Intelligent System for Personalized Examination Scheduling Open
Artificial Intelligence (AI) has brought a revolution in many areas, including the education sector. It has the potential to improve learning practices, innovate teaching, and accelerate the path towards personalized learning. This work in…
Enhancing Autonomous Vehicle Navigation at Complex Junctions Using LIDAR and YOLOv5-Based Detection Open
Vehicle autonomy demand continues to grow because AI developments and sensor technology enable vehicles to navigate schematically while protecting safety conditions. The main obstacle for autonomous vehicles involves handling advanced junc…
Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction Open
Time Series Forecasting (TSF) is an important application across many fields. There is a debate about whether Transformers, despite being good at understanding long sequences, struggle with preserving temporal relationships in time series …
Advancing Patient Care with an Intelligent and Personalized Medication Engagement System Open
Therapeutic efficacy is affected by adherence failure as also demonstrated by WHO clinical studies that 50–70% of patients follow a treatment plan properly. Patients’ failure to follow prescribed drugs is the main reason for morbidity and …
Enhancing Diagnostic Accuracy for Skin Cancer and COVID-19 Detection: A Comparative Study Using a Stacked Ensemble Method Open
In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the…
Defining a Metric-Driven Approach for Learning Hazardous Situations Open
Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms to obtain more benefits from technology. Reinforcement learning (RL) being an importa…
A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks Open
Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combi…
Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends Open
Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adult…
Improving Adherence to Medication in an Intelligent Environment Using Reinforcement Learning Open
Correct and timely medication plays an important role in the treatment and recovery of a patient. An intelligent and efficient patient engagement environment ensures enduring health and positive clinical outcomes. Reinforcement Learning (R…
Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning Open
Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute …
Inverse Reinforcement Learning Based Approach for Investigating Optimal Dynamic Treatment Regime Open
In recent years, the importance of artificial intelligence (AI) and reinforcement learning (RL) has exponentially increased in healthcare and learning Dynamic Treatment Regimes (DTR). These techniques are used to learn and recover the best…
A RL Based Model for Improving Human Task Management Performance Open
This paper discusses an Reinforcement Learning (RL) based system to improve human performance in task selection and management by incorporating various factors. A simulated task management environment of a coaching center is considered. Di…
An AI-Empowered Home-Infrastructure to Minimize Medication Errors Open
This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based s…
Learning and Assessing Optimal Dynamic Treatment Regimes Through Cooperative Imitation Learning Open
Dynamic Treatment Regimes (DTRs) are sets of sequential decision rules that can be adapted over time to treat patients with a specific pathology. DTR consists of alternative treatment paths and any of these treatments can be adapted depend…
Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems Open
The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MI…
View article: Self Learning of News Category Using AI Techniques
Self Learning of News Category Using AI Techniques Open
Numerous e-news channels publish the daily happenings in the world from different sources. These huge amounts of news articles have lamentably conceived the information overload issue among the users. Hence text mining, which aims in extra…
View article: Prediction of Breast Cancer Using AI-Based Methods
Prediction of Breast Cancer Using AI-Based Methods Open
Breast cancer has made its mark as the primary cause of female deaths and disability worldwide, making it a significant health problem. However, early diagnosis of breast cancer can lead to its effective treatment. The relevant diagnostic …
A Self-Learning Autonomous and Intelligent System for the Reduction of Medication Errors in Home Treatments Open
The treatment process at home after hospitalization may become challenging for elders and people having any physical or cognitive disability. Such patients can, nowadays, be supported by Autonomous and Intelligent Monitoring Systems (AIMSs…
A Gentle Introduction to Reinforcement Learning and its Application in Different Fields Open
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actio…