Abstract From a future perspective and with the current advancements in technology, deep reinforcement learning (DRL) is set to play an important role in several areas like transportation, automation, finance, medical and in many more fields with less human interaction. With the popularity of its fast‐learning algorithms there is an exponential increase in the opportunities for handling dynamic environments without any explicit programming. Additionally, DRL sophisticatedly handles real‐world complex problems in different environments. It has grasped great attention in the areas of natural language processing (NLP), speech recognition, computer vision and image classification which has led to a drastic increase in solving complex problems like planning, decision‐making and perception. This survey provides a comprehensive analysis of DRL and different types of neural network, DRL architectures, and their real‐world applications. Recent and upcoming trends in the field of artificial intelligence (AI) and its categories have been emphasized and potential challenges have been discussed.

Abstract The vulnerabilities of the Internet of Things (IoTs) in general and the Internet of Mobile Things (IoMTs) in particular motivate researchers to equip them with security systems against intruders and attacks. The integration of anomaly detection with intrusion detection for IoMTs has not been addressed adequately. This paper tackles this issue through building a Kalman filter and Cauchy clustering algorithm for anomaly detection and using them for authentication nodes within IoMTs using the Extreme Learning Machine classifier. The algorithm of this proposed work is composed of various components; first, the Kalman filter‐based model for estimating the trajectory of pedestrians within an indoor environment based on fusing WiFi with IMU data. Second, trustworthiness assessment for detecting anomaly behaviour in IoMT based on the estimated trajectory using the Kalman filter. Third, the trust IDS model for IoMT systems by integrating anomaly detection with online learning for attacks identification using an online sequential extreme learning machine. The algorithm has been implemented and evaluated using TamperU dataset for WiFi fingerprinting and KDD99 for intrusion detection. Furthermore, a comparison with benchmarks (the algorithms which used in other studies) for intrusion and anomaly detection proves the superiority of this proposed approach in terms of all the considered classification metrics.

Identifying the gender, race, age, and stature of the target during the forensic inquiry is a critical stage in various events such as accidents, bombings, terrorism, wars, and disasters. In this paper, an application has been developed that uses hand X-rays to identify and determine gender for medical applications such as special cases where diagnosing the gender is difficult, like accidents in which the hand is amputated and unknown, severe burns, and in old skeletal structures using deep learning models. For comparative purposes, GoogLeNet and ResNet-18 were employed. Gender determination using hand X-rays yielded positive results. The accuracy of gender detection in the model GoogLeNet (validation, training, test, and total) is (76.67%, 96.68%, 53.33%, and 89.5%) respectively, while the accuracy of gender detection in the model ResNet-18 (validation, training, test, and total) are (80%, 99.29%, 87.5%, 94.63%) respectively. The ResNet-18 model was adopted as the best model for gender detection and determination because high results were obtained. Simulation results showed acceptable results with high accuracy in diagnosis, where the highest gender determination rate was obtained through hand X-ray analysis at 94.63%.

Electronic health records are essential and sensitive since they include vital information and are routinely exchanged across several parties, such as hospitals and private clinics. These data must remain accurate, current, secret, and available only to authorized parties. Integrating these data improves the accuracy and cost-effectiveness of the present health data administration framework. Electronic Medical Records (EMRs) are now kept utilizing the structure of the client/server via whom patient data information is maintained in the hospital. Multiple hospitals use the same database to track a single patient. These limitations prevent a custom health system from providing various associated experts and patients with a cohesive, integrated, secure, and confidential medical history. Modern healthcare systems are distinguished by their complexity and expense. However, this may be mitigated by enhanced health record management and Blockchain technology. The Blockchain’s data availability, confidence, and security characteristics have a bright future in healthcare services, giving solutions to the issues of the traditional customer/server architecture EMR management platform: intricacy, confidence, dependability, compatibility, and anonymity. An e-health record management based on Internet of Medical Things (EHRM-IoMT) is proposed in this paper. This paper explores and analyzes Blockchain efficiency and customer/server paradigms. The findings show that a patient-centred strategy may achieve remarkable success utilizing Blockchain. Moreover, the immutable and accurate data of persons in Blockchain may enable healthcare practitioners to better forecast and aid with diagnosis utilizing the IoMT via machine learning and artificial intelligence.