Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of most important and useful technology. It is a learning method where software agent interacts with an unknown environment, selects actions, progressively discovers environment dynamics. RL been effectively applied many areas real life. This article intends provide in-depth introduction Markov Decision Process, its algorithms. Moreover, we present literature review application variety fields,...

Time Series Forecasting (TSF) is an important application across many fields. There a debate about whether Transformers, despite being good at understanding long sequences, struggle with preserving temporal relationships in time series data. Recent research suggests that simpler linear models might outperform or least provide competitive performance compared to complex Transformer-based for TSF tasks. In this paper, we propose novel data-efficient architecture, GLinear, multivariate exploits...

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. AI-based system first learns the skills of patient using Actor–Critic method. After assessing patients’ disabilities, adopts appropriate method for monitoring process. Available methods process are Deep Learning (DL)-based classifier, Optical Character Recognition, and...

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 alternative treatment paths and any these treatments depending on the patient's characteristics.Reinforcement Learning (RL) Imitation (IL) approaches have been deployed for obtaining optimal patient but, rely only positive trajectories (i.e., concluded responses patient).In contrast, negative samples non-responding treatments)...

Multiple Input Output (MIMO) systems have been gaining significant attention from the research community due to their potential improve data rates. However, a suitable scheduling mechanism is required efficiently distribute available spectrum resources and enhance system capacity. This paper investigates user selection problem in Multi-User MIMO (MU-MIMO) environment using multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas' spatial degrees of freedom, devices can...

The ability of a patient to take correct medicine at right time may be reduced when having visual or auditory impairments. Use inappropriate drug intake can dangerous and it is important that the takes schedule time. But difficult for elderly persons patients with audio impairments carry out treatment process independently correctly. This article presents Convolutional Neural Network (CNN) based medication monitoring system this sub component an intelligent pill reminder system.The goal in...

Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms obtain more benefits from technology. Reinforcement (RL) being an important ML method largely applied in safety-centric scenarios. In such a situation, constraints are necessary avoid undesired outcomes. Within traditional RL paradigm, agents typically focus on identifying states associated with high rewards maximize its long-term returns....

Machine learning techniques have achieved a lot of success in many healthcare related aspects including personalized treatment, medical imaging, diagnostic systems etc. In present work, we propose medicine reminder system which can assist patients their treatment process at home. case home with different physical and/or mental disabilities, key challenge is the choice proper message for each specific patient taking into account his/her needs. To this aim, proposed use variety messages (i.e...

Breast cancer is the most common cause of death worldwide in women.  Several predisposing risk factors have been identified making its incidence constantly rising. The aim current study was to analyze thyroid hormone, vitamin D, and 8-hydroxydeoxyguanosine (8-OHdG) as onset breast cancer. In present case control study, a total two hundred seventy-four (n=274) participants were included after taken informed consent individually. further stratified into groups. Group A consisted one...

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 combining both centralized and decentralized learning approaches. This addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency preoccupied by vehicle-to-infrastructure (V2I) links. We introduce QMIX technique with Deep Q (DQNs)...

Modern medical software systems are often classified as devices and governed by regulations which require stringent risk safety activities to be implemented minimize the occurrence of risky events. This paper proposes a Reinforcement Learning (RL based approach for training agent management systems. The goal RL is avoid that patient enters in dangerous undesirable states. At same time, must able reach on safe state or an exit minimum interval time.

Scheduling more than one user could increase system capacity of multi antenna systems. Present article reviews Multi User MIMO (MU-MIMO) communication from scheduling perspective, discussing performance gains in terms sumrate, fairness, and computational complexity. Several techniques reported for MU-MIMO based on different algorithms are reviewed, along with partial channel state information full information. Moreover, the selection procedure first leakage interference is analyzed. The...

In multiuser MIMO systems, the spatial degrees of freedom can be effectively exploited to enhance system capacity by scheduling multiple users. This paper reviews communication from perspective, discussing performance gains, fairness, and computational complexity leakage based algorithms. We consider well known algorithm on user power reported for MU-MIMO systems analyze selection criteria first its impact algorithm. Through experimental validations, it is shown that if randomly selected...