Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications.

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

The Internet of Things (IoT) widely supports the smart healthcare field; combined with computer vision, machine, and deep learning techniques; it provides fast and accurate services for automated patient discomfort monitoring/detection systems. Traditional patient monitoring systems are commonly composed of wearable sensors and vision-based methods. In this article, an IoT-based noninvasive automated patient's discomfort monitoring/detection system is presented and implemented, using a deep-learning-based algorithm. The system is based on an IP camera device; the patient's body's movement and posture are detected without using any wearable devices. The Mask-RCNN method is employed for the extraction of different key points on the patient body. These detected key points are then transformed into six major body organs using association rules of data mining. Furthermore, for analyzing the patient's discomfort, detected key point coordinates information is measured. Finally, the distance and the temporal threshold are applied to classify movements as either associated with normal or discomfort conditions. These key points information is also used to determine the postures of the patient lying on the bed. The patient's body position and posture are continuously monitored, based on which comfort and discomfort level are discriminated. For experimental evaluation, different video sequences are recorded covering two patient's beds. The experimental results show the proposed system's worth by achieving a true-positive rate of 94% and a false-positive rate of 7%.

Advancement in the Internet of Medical Things (IoMT), along with machine learning, deep learning, and artificial intelligence techniques, initiated a world of possibilities in healthcare. It has an extensive range of applications: when connected to the Internet, ordinary medical devices and sensors can collect valuable data, deep learning, and artificial intelligence techniques utilize this data and give an insight of symptoms, trends and enable remote care. Recently, Covid-19 pandemic outbreak caused the death of a large number of people. This virus has infected millions of people, and still, the rate of infected people is increasing day by day. Researchers are endeavoring to utilize medical images and deep learning-based models for the detection of Covid-19. Various techniques have been presented that utilize X-Ray images of the chest for the detection of Covid-19. However, the importance of regional-based convolutional neural networks (CNNs) is currently confined. Thus, this research aimed to introduce an IoT-based deep learning framework for early assessment of Covid-19. This framework can reduce the working pressure of medical experts/radiologists and contribute to the pandemic control. A deep learning-based model, i.e., faster regions with CNNs (Faster-RCNN) with ResNet-101, is applied on X-Ray images of the chest for Covid-19 detection. It uses region proposal network (RPN) to perform detection. By employing the model, we achieve a detection accuracy of 98%. Therefore, we believe that the system might be capable in order to assist medical expert/radiologist, to verify early assessment toward Covid-19.

Wireless Body Area Network (WBAN) is an interconnection of small bio-sensor nodes that are deployed in/on different parts of human body. It is used to sense health-related data such as rate of heart beat, blood pressure, blood glucose level, Electro-cardiogram (ECG), Electro-myography (EMG) etc. of human body and pass these readings to real-time health monitoring systems. WBANs is an important research area and is used in different applications such as medical field, sports, entertainment, social welfare etc. Bio-Sensor Nodes (BSNs) or simply called as Sensor Nodes (SNs) are the main backbone of WBANs. SNs normally have very limited resources due to its smaller size. Therefore, minimum consumption of energy is an essential design requirement of WBAN schemes. In the proposed work, Energy-efficient Harvested-Aware clustering and cooperative Routing Protocol for WBAN (E-HARP) are presented. The presented protocol mainly proposes a novel multi-attribute-based technique for dynamic Cluster Head (CH) selection and cooperative routing. In the first phase of this two-phased technique, optimum CH is selected among the cluster members, based on calculated Cost Factor (CF). The parameters used for calculation of CF are; residual energy of SN, required transmission power, communication link Signal-to-Noise-Ratio (SNR) and total network energy loss. In order to distribute load on one CH, E-HARP selects new CH in each data transmission round. In the second phase of E-HARP, data is routed with cooperative effort of the SN, which saves the node energy by prohibiting the transmission of redundant data packets. To evaluate the performance of the proposed technique, comprehensive experimentations using NS-2 simulation tool has been conducted. The results are compared with some latest techniques named as EH-RCB, ELR-W, Co-LAEEBA, and EECBSR. The acquired results show a significant enhancement of E-HARP in terms of network stability, network life time, throughput, end-to-end delay and packet delivery ratio.