The Cyber-Physical and Intelligent Robotics Laboratory has been digitally recreated, and it includes all the key elements that allow 6-axis industrial robots to perform PTP, LIN, and CIRC motions. Furthermore, the user can create a program with these motion types. The human–machine interface is also integrated into our system. It can also assist SMEs in developing their in-house training. After all, training on an industrial robot unit does not entail installation costs within the facility. Nor are there any maintenance and servicing costs. Since the lab is digital, additional robot units can be added or removed. Thus, areas for training or production can be pre-configured within each facility. Because of the customizability and virtual education format, there is no room capacity problem, and trainees can participate in the exercises in parallel. Exercises were also conducted to evaluate the program’s impact on teaching, and the results showed that using machine units can improve teaching. Even today’s digital labs cannot physically convey the sense of space or the relative weights of different elements in virtual space. Even with these features, individuals can operate a machine more effectively than relying solely on traditional, non-interactive demonstration materials.

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

Melanoma skin cancer is a global public health threat due to its increasing rates and the possibility of severe outcomes if not adequately addressed. Melanoma is caused by ultraviolet radiation and, among its two stages, malignant is more dangerous than benign. The diagnosis of melanoma is typically based on visual inspection and manual methods carried out by experienced physicians. However, this method is usually slow and has a high probability of error. Deep-learning-based models can offer better and low-cost treatments for people with melanoma. This study aimed to develop a deep-learning model to classify melanoma skin cancer in its early stages. This study presents a modified deep-learning model, named DeepMelaNet, to correctly classify skin cancer images as benign or malignant. The proposed classifier achieved an accuracy of 93.40%, a precision of 98%, a recall of 94%, and an F1 score of 93% on a dataset of 10,000 melanoma skin cancer images, offering a practical solution that can help healthcare professionals in early skin cancer prediction.

Rapid innovation leading to better patient outcomes have been driven by recent breakthroughs in the Internet of Things (IoT), which have drastically changed the healthcare sector. In order to highlight the importance of IoT in healthcare applications, this article presents a user-friendly and integrated approach for bibliometric analysis. Traditional bibliometric methods often rely solely on Web of Science (WoS) or Scopus, limiting the scope of analysis. To address this issue, the proposed approach uses the R program Bibliometrix to combine data from seven databases, namely Scopus, WoS, IEEE, ACM Digital Library, PubMed, Science Direct, and Google Scholar (GS). After having developed an inclusion/exclusion criterion, 2,990 journal papers published between 2011 and 2022 were subjected to a thorough literature review and bibliometric analysis. This study demonstrates that the healthcare industry is highly interested in the IoT, as well as the rapid growth of research into IoT healthcare applications, blockchain, Artificial Intelligence (AI), 5G telecoms, and data analytics. Authentication methods, fog computing, cloud-IoT integration, cognitive smart healthcare, and other essential topics are further examined by employing co-citation network analysis. In addition to illuminating potential avenues for further investigation, these results provide academics with a comprehensive picture of where IoT research in healthcare is standing at the moment. The output of the conducted analysis shows that there has been a dramatic uptick in publishing since 2017, with most of the articles appearing in prestigious journals related to computer science. By integrating data from multiple databases, the proposed methodology represents a significant advancement in bibliometric analysis, enabling a more comprehensive exploration of IoT's impact on healthcare, and facilitating a deeper understanding of the emerging trends and critical themes in this rapidly evolving field.

Cardiovascular disease has been more common throughout the past several decades. Cardiovascular disease detection methods use machine learning algorithms to assess data and provide accurate cardiac diagnosis. An accurate and comprehensive assessment of cardiovascular risk is essential to improve cardiovascular protection and reduce the frequency and severity of heart attacks and strokes. This paper proposes a machine learning-based autonomous strategy for the diagnosis of cardiovascular disease. Some preprocessing methods were applied to improve the results and accuracy. Finally, lazy prediction was used to find the best model by applying a neural network and two ensemble models. The best accuracy of 99% was obtained with the HistGradientBoosting (ensemble) classifier, which obtained respectable results with a higher accuracy rate. This model can enhance the ability to predict cardiovascular disease with better accuracy.

Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset.