A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

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 actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision

An AI-Empowered Home-Infrastructure to Minimize Medication Errors

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 system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment

An AI-Empowered Home-Infrastructure to Minimize Medication Errors

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 system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

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 MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

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 available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a 20% higher sum-rate performance over the conventional methods is reported

An AI-empowered infrastructure for risk prevention during medical examination

A medical examination at Nuclear Medicine Department (NMD) carries out at multiple stages. Patients are accompanied and guided by nurses during their movements within the NMD to avoid them entering into any hazardous situation. However, even accompanying nurses could be exposed to harmful radiation, which puts their safety at risk. Artificial Intelligence (AI) technologies can address these issues by supporting these processes avoiding risky situations, and preventing patients’ and clinicians’ safe. This article presents an artificial intelligence-based architecture for risk management during the nuclear medical examination to automatically guide the patients during the medical examination and support injury prevention. The architecture comprises two main components; the first component integrates Deep Learning (DL) techniques and WiFi tools to monitor and verify the patient’s position continuously; the second integrates Reinforcement Learning (RL) techniques to guide the patient during his/her examination. Experimental results show the suitability of the proposed architecture. Therefore the proposed risk management system can support the prevention of risks and injuries during medical examination and reduce operational costs

Clinicopathological importance of Papanicolaou smears for the diagnosis of premalignant and malignant lesions of the cervix

Background: Premalignant and malignant lesions are not uncommon in Pakistani women, especially in the older age-groups Aim: This study was conducted to determine the clinicopathological importance of conventional Papanicolaou (Pap) smears for the diagnosis of premalignant and malignant lesions of the cervix. Materials and Methods: Pap smears of 1000 women were examined from January 2007 to June 2009. Only cases with neoplastic cytology were included. Results: The overall frequency of normal, inadequate, neoplastic, and infective smears was 50%, 1.8%, 10.2%, and 38.3%, respectively. Most of the patients (67%) were in the postmenopausal age-group, with the mean age being 44.7±15.63 years. The commonest clinical signs/symptoms seen among the 102 patients with neoplastic gynecological lesions were vaginal discharge and abnormal bleeding (93/102;(91.2% and 62/102;60.7%). Of the 102 cases with neoplastic lesions 46 patients (45%) had low-grade squamous cell intraepithelial lesions (LSILs), 22 (21.5%) had high-grade squamous cell intraepithelial lesions (HSILs), 14 (13.7%) had squamous cell carcinoma, and 6 (5.8%) showed features of adenocarcinoma. Ten (9.8%) cases showed cytology of atypical squamous cells of undetermined significance (ASCUS) and four (3.9%) cases had atypical glandular cells of undetermined significance (AGUS). Conclusion: We conclude that cervical smear examination is well suited for diagnosing neoplastic disease. It is clear that cervical neoplastic lesions are becoming a problem in Pakistan