ABSTRACT The healthcare industry is undergoing a transformative shift with the integration of blockchain technology and Artificial Intelligence (AI). Traditional healthcare systems struggle with data security, lack of transparency, and inefficiencies in resource allocation, leading to increased risks and operational challenges. AI‐driven models provide intelligent solutions by enabling predictive diagnostics, fraud detection, and personalised treatment plans, while blockchain ensures data integrity, security, and decentralised access control. The synergy between AI and blockchain enhances decision‐making, optimises resource utilisation and fosters trust in healthcare systems by automating processes with greater transparency and security. AI‐powered analytics can extract meaningful insights from vast healthcare datasets, improving patient outcomes and streamlining supply chains. Meanwhile, blockchain's immutable ledger safeguards medical data, preventing breaches and ensuring regulatory compliance. This paper presents a comprehensive review of blockchain solutions in healthcare, exploring their impact on enhancing security, promoting transparency and improving energy efficiency. Additionally, this paper also presents insights on the integration of AI and blockchain in healthcare. It categorises existing blockchain‐based frameworks and highlights emerging trends, challenges, and future research directions. This review aims to serve as a foundational reference for researchers and practitioners developing secure, transparent, and intelligent healthcare systems.

ABSTRACT Planning is one of the most critical areas within Project Management, with adequate task scheduling and resource management being of vital importance, especially at the project's outset. This paper introduces an Artificial Intelligence designed for the automatic planning of photovoltaic plant (PV) construction projects, encompassing various tasks such as engineering, procurement, logistics, construction and commissioning, and including the substation and transmission line, scheduling a total of 100 tasks, which constitute a basic Engineering, Procurement and Construction project planning. The model is trained using a total of 50 real‐case project plans for PVs. The results demonstrate that the model successfully and effectively carries out photovoltaic project planning, marking a significant step towards digital transformation.

ABSTRACT News text classification is a technique of classifying news articles into some predefined classes. It helps consumers find news that piques their interest. Due to the growth of internet news content, effective automatic classification systems are required to handle and arrange massive volumes of news articles. Here, the AG's News Corpus (AG News) articles are classified through the hypergraph neural network along with the attention layer and quadratic support vector machine (AGNews_HAL_QSVM). This benchmark dataset was named after the ‘ComeToMyHead’ project by Alberto G. (AG) Leonardo. The dataset was gathered from Kaggle, and the LDA (Latent Dirichlet Allocation) was used to generate the topic‐specific data. Every topic will be regarded as a hyperedge in the hypergraph, and each topic's words will be regarded as a hypervertex. A hypergraph convolution neural network with an attention layer is used to extract the corpus' key features. For classification, the collected features are sent into a quadratic support vector machine. A complex deep‐learning model has been used to test the proposed model. At an accuracy of 91.2%, the suggested model performs better than the other state‐of‐the‐art algorithms. In order to improve automatic AGNews classification systems, this study presents a practical implementation of the proposed model for organising news content. It propels developments in public discourse, media, personalisation and policy.

Abstract In recent years, Internet of Things (IoT) systems have changed the way we live, work and do businesses in many areas, even those that until recently seemed unlikely. Some areas that can benefit from their application are, among others, healthcare, smart cities, industrial automation, smart agriculture, intelligent transportation systems, smart logistics, and emergency response. This research work proposes a novel alternative that allows the creation of IoT systems capable of making remote changes in devices' communication in a fast and agile manner. Our proposal gives way to some of the most common changes in communication made during the development and maintenance phase in IoT systems, like changing the destination of data transmission, sending the data to multiple destinations, and changing the frequency of sending data. Our solution, which is used in the programs, is loaded on the device. When the device starts, it connects to a configuration server in the background and listens for changes. The changes are sent to the configuration server using specified commands. When a change is detected, the command is processed, and the change in communication is applied without stopping the running program. We designed experiments to evaluate the complexity of the programs developed using our proposal and the actions needed to make a change.

Abstract Nowadays, economic growth is influenced by many domestic and foreign global issues, concerning the decrease of industrial output for the rise in economic inequality and the weakening of the overall condition in the countries. The role of economic growth is based on human capital and investment. Therefore, in this paper, the human capital and economic management system (HC‐EMS) has been proposed to enhance the country's quality based on the capital structure investment. HC explores the role of command and its significance in the production and creation aspects of human capital such that an area can grow creatively. HC‐EMS analysed critical monitoring systems for developing human resources and the high‐tech and creative economic sectors and presented suggestions for change. Besides, there is a need to prove the shortcomings and benefits of human resource development systems and statistical approaches for gathering and analysing information. The impact of financial services and financial transactions may vary; therefore, it is critically important for fostering economic growth. The experimental result suggests that the proposed HC‐EMS achieves the highest growth development ratio than other existing methods.

The current era witnesses the notable transition of society from an information-centric to a human-centric one aiming at striking a balance between economic advancements and upholding the societal and fundamental needs of humanity. It is undeniable that the Internet of Things (IoT) and artificial intelligence (AI) are the key players in realizing a human-centric society. However, for society and individuals to benefit from advanced technology, it is important to gain the trust of human users by guaranteeing the inclusion of ethical aspects such as safety, privacy, nondiscrimination, and legality of the system. Incorporating explainable AI (XAI) into the system to establish explainability and transparency supports the development of trust among stakeholders, including the developers of the system. This article presents the general class of vulnerabilities that affect IoT systems and directs the readers’ attention toward intrusion detection systems (IDSs). The existing state-of-the-art IDS system is discussed. An attack model modeling the possible attacks is presented. Furthermore, since our focus is on providing explanations for the IDS predictions, we first present a consolidated study of the commonly used explanation methods along with their advantages and disadvantages. We then present a high-level human-inclusive XAI framework for the IoT that presents the participating components and roles. We also hint upon a few approaches to upholding safety and privacy using XAI that we will be taking up in our future work. An attack model based on the study of possible attacks on the system is also presented in the article. The article also presents guidelines to choose a suitable XAI method and a taxonomy of explanation evaluation mechanisms, which is an important yet less visited aspect of explainable AI.

In recent years, the deepening of the role of emotions in education has increased, since it has been shown that emotions influence what we learn and retain, in different areas such as learning a second language. Likewise, the analysis of emotional data in learning environments has been implemented through various methods, such as the collection of vital signs, where brain-computer interfaces provide a means to capture emotional metrics from brain activity. Furthermore, public data sets in this domain are scarce. Consequently, this work presents the initial approach to generate an Emotional Dataset, for emotional analysis based on the collection of emotions, through an Emotiv Insight as a Brain Computer Interface. Also, through the Babbel application for learning second languages. As a result of this study, it will be possible to obtain the general and projected structure for the resulting data set, based on the generated approach.

Abstract Unmanned aerial vehicle based precision agriculture is a predominant research area. The modern flying ad‐hoc network leverages the advanced low latency vehicular communication and intelligent computing paradigms that help the ecosystem to grow up to the next level. In this work, we propose an ecosystem for precision agriculture that leverages the use of the opportunistic MQTT protocol in an edge‐enabled intelligent drone network for sensing and performing crop prediction using an intelligent ensemble machine learning model. The proposed approach leverages the edge computing system that requires low energy devices and also exploits the ultra‐low latency opportunistic message transfer methodology. The experimental results show the maximum of 0.9 message delivery ratio and a minimum of 600 ms latency is achieved by opportunistic MQTT protocol in an ultra‐low latency sparse network scenario. A weighted ensemble model is deployed onto the edge enabled devices or the drones. An accuracy of 96.5% is achieved in predicting the type of crops that can be grown in the soil about the selected area of interest.

In the healthcare network, the Internet of Things (IoT) devices are connected to the network for enabling remote monitoring of patients’ health. IoT Device (IoTD) security, however, is a serious concern because typical security measures might not be appropriate for IoTD, making them naturally vulnerable to physical and copying attacks. Therefore, device authentication is a very essential security concern for IoT networks. Additionally, the storage and processing power of these devices are constrained. To address all these requirements, physically unclonable functions (PUFs) for device authentication is a potential strategy. In this article, an advanced lightweight authentication scheme for IoTD is proposed by using PUF. This scheme provides robust authentication without storing any sensitive information on the device’s memory and establishes the session key exchange process simultaneously. Moreover, this scheme preserves device privacy by including a temporary identity, which is updated at the end of each session. The effectiveness of this novel model is assessed, and results demonstrate that it is more effective and secure than many existing schemes.

Blockchain technology provides a secure and reliable platform for managing data in various application areas, such as supply chain management, multimedia, financial sector, food sector,Internet of Things (IoT), healthcare, and many more. The recent emergence of blockchain with IoT provides significant growth in the healthcare industry to improve security, privacy, efficiency, and transparency with more business opportunities. Nevertheless, conventional healthcare schemes suffer from various security attacks like collusion, phishing, masquerade, etc. Therefore, a privacy-preservingDistributed Application (DA)is proposed in this paper using blockchain technology to create and maintain healthcare certificates. Here, the distributed application provides an interface between the blockchain network and system objects like healthcare centers, verifiers, and regular authorities to generate and issue medical documents. In addition, it also ensures security by specifying rules using various smart contracts. To evaluate the performance of the proposed scheme, various experimental tests are conducted using the Etherscan tool for measuring operation cost, latency, and processing time. Here, the efficiency of the proposed system is also compared to the existing systems in terms of latency, throughput, and response time. The experimental results and comparative analysis show that the proposed work is more efficient than the existing techniques.

Model inversion attack (MIA) is a cyber threat with an increasing alert even for deep-learning-based recognition systems (DLRSs). By targeting a DLRS under a scenario of attacker access to the model structure and parameters, MIA generates a data clone for a certain targeted class label. To avoid the possible threats of such MIA-generated data clones, this research work proposes a social IoT approach to a collaborative cyber-defense among the online recognition systems (RSs) sharing the targeted class label. Since, the generation of an MIA-clone is by targeting an RS model and using its structure, parameters, and class labels output scores in an iterative optimization process, the generated clone is partially inherent to the targeted model. Thus, it is expected for an MIA-clone to show a different performance on a secondary RS wherein the same targeted class label is included. It is because, in the MIA generation of the clone, not only the targeted class label but also other class labels, and model parameters and structure affect the process, while the second model has just the targeted class label in common with the target model. Deploying the Social Internet of Recognition Systems (SIoRS), the proposed technique utilizes a collaborative recognition by SIoRC which plays the role of a complementary recognition besides the targeted RS. The recognition output by the targeted RS is further verified by the SIoRS complementary recognition result. To avoid the MIA-targeted data clones, the verification of recognition is by the log-likelihood ratio test between the targeted RS and the SIoRS complementary recognition confidence scores. The proposed technique is evaluated by statistical analysis on deep face RSs in 10000 Monte Carlo runs for each of the conventional, dc-generative adversarial network (GAN) and$\alpha $-GAN integrated MIA techniques in targeting two different user identities. The$Z$scores of the fitted normal distribution of the log-likelihood ratios indicate almost 100% detection rate of clones generated by conventional MIA and 95.23% and 86% of clones, respectively, generated by DC-GAN and$\alpha $-GAN integrated deep MIA techniques.

Abstract The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait‐related walking activities are very important for the analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human‐centered humanoid robot system, and many clinical diagnoses, for example, Parkinson, pathological gait, freezing of gait, etc. at an early stage. For experiment analysis, 10 different lower limb activities are being considered of healthy and crouch walking subjects. A total of 25 healthy and 10 crouch walk subjects are considered for experiment purposes of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB‐Depth (RGB‐D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN‐LSTM (CNN‐Long Short Term Memory) are used for the classification of these rehabilitation exercises. The RGB‐D data is obtained using a Microsoft Kinect v2 sensor on a 100 Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN‐LSTM models respectively.

Abstract Predicting patients with major depression (MDD) is currently a difficult task. Magnetic resonance imaging (MRI) data analysis may provide insight into individual patient responses, allowing for more customized treatment decisions. Due to the absence of brain MRI data for MDD patients, a transfer learning (TL) method developed is used using calculation criteria. Combining an Inception‐v3 neural network with a typical pre‐trained neural network, the move learning‐based Inception‐v3 was proposed for the classification of MDD MRI datasets. An experiment was performed on the classification of eight semantic emotions (defined by IMAPS). Compared to other methods, the proposed method performs high efficiency for 90–10% and 80–20% (positive and negative classes), normal (N), unnormal (UN), and average/total sets, and for 70–30%, accuracy (A) is 92.90%, area under the curve (AUC) is 94.23%, and average precision score (APS) is 95.75%. Individual patients' responses to emotional stimulation can be predicted using the proposed methods, which can provide guidance in diagnosis and prognosis.

Smart monitoring of off-road vehicles is cursed by their complex and expensive IoT sensors technologies. High dependence on the cloud/fog computation, availability of the network, and expert knowledge make it handicap in the rural off-network areas. Use of edge devices, such as smartphones, attributed by computation capabilities is the solution that is yet to be developed at commercial level (Fawwaz and Chung 2020) and (Zhengweiet al., 2021). Additionally, the user's growing demand for economic and user-friendly technology motivates to shift from costly and complex sensors to economic. In this article, we present the hybridized computational intelligence methodology to develop an edge-device-enabled AI technology for health monitoring and diagnosis (HM&D) of the off-road vehicles, taking use of super economic microphones as sensors. Smartphones are benefited by integrated microphones, and thus, the App-based developed technology is generalized for all vehicles from old to new. Enhanced selection and log-scaled mutation genetic algorithms is used to evolve the structure of the artificial neural network toward an optimally lightweight structure. Each evolved lightweight ANN structure is trained by scaled conjugate gradient back-propagation training algorithm to optimize corresponding weights and biases. The comparative results with currently reported genetic algorithms for edge computation prove it a breakthrough technology for edge-device-enabled HM&D of off-road vehicles (Yanet al., 2020).

Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) have great potential for efficient application to a network of Internet of Things (IoT) at a high data rate. When the IoT network is among the underwater sensory devices known as the Internet of Underwater Things (IoUT), the electromagnetic wave cannot play the role of baseband signal due to rapid fall-off inside the water. Thus, acoustic OFDM is a reliable replacement for conventional OFDM inside the water. A blind structure for MIMO acoustic OFDM using Independent Component Analysis (ICA) brings even further advantages in data rate and energy consumption by avoiding the required pilot and preamble data. This research work presents a blind MIMO Acoustic OFDM blind transceiver for IoUT based on Probabilistic Stone’s Blind Source Separation (PS-BSS). The proposed technique has multiple times lower complexity compared to the ICA-based technique while maintaining a comparable efficiency. As observed in the results carried out with 100 Monte Carlo runs of transmission of random data bits over a highly sparse channel that is the common case of an underwater environment, the proposed PS-BSS-based technique dominates the ICA-based one, and as the sparseness of the channel decreases, its efficiency is comparable to the ICA-based technique. Thus, in the case of a highly sparse channel, the proposed technique is superior in both aspects of efficiency and complexity, while over lower sparseness, due to its comparative efficiency, it can be hired as an optimum technique fulfilling a fair tradeoff between efficiency and complexity.

This research investigates the reasons for the lack of enrollment of women in science careers, a strong trend in Spain and many other countries. One of the branches of knowledge in which this lack of women is most noticeable in computing. The hypothesis is that parents' academic training could influence their children's academic training and orientation towards scientific specialities. This analysis includes the first two grades of hihg school. In the Spanish Educational System, students choose their speciality in these two final stages, so their university future is conditioned. It has designed a survey that includes questions about parents' training and employment. One hundred sixty-eight students answered thus survey of the Baccalaureate and Technical / Professional Schools in Spain. The results and conclusions set out the necessary bases to design a tool that simulates students' behaviour when they enrol. And identify the premises considered during the choice of studies. It would be interesting to get a system that encourages the motivation of women for scientific careers.

Human Augmentation is a field of science that seeks to improve human capabilities and productivity through the application of technologies. The prevention of occupational risks is one of the areas where such technologies may make a valuable contribution. In this paper, we introduce CreaMe, a platform with the ability to create immersive virtual experiences. The objective is to create training content quickly and at a low cost by employing human augmentation technologies to transfer theoretical principles into real work. Data from the pilot experience are also presented. We collected over 280 minutes of virtual experiences by using CreaMe in 3 different training lessons. Following the pilot experience, we analysed variables on the evolution of knowledge about occupational risks, improvement of general user knowledge, user satisfaction with the new virtual experience, and the user's ability to use the virtual environment. We can point out that 83.18% of users successfully completed tasks, and 92.13% of users rated post-experience satisfaction very positively. Also, CreaMe was deemed a tool that can contribute to the prevention of occupational risks and, therefore, to workers' wellbeing, health, and productivity in any industry. The development of CreaMe will also allow for the application of human augmentation in the prevention of occupational risks from any work that may pose a hazard to the worker.

There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.

Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the so-called Monte-Carlo Tree Search family of algorithms, which provide the possibility of storing and reusing previously calculated results to improve precision in the calculation of future outcomes. However, developers and researchers working in this area tend to have to carry out software developments from scratch to use their designs or improve designs previously created by other researchers. This makes it difficult to see improvements in current algorithms as it takes a lot of hard work. This work presents JGraphs, a toolset implemented in the Java programming language that will allow researchers to avoid having to reinvent the wheel when working with Monte-Carlo Tree Search. In addition, it will allow testing experiments carried out by others in a simple way, reusing previous knowledge.

This special issue entitled "cognitive machie intelligence for Cyber-Physical Systems" addresses under-researched and controversial topics on new emerging themes of cyber-physical systems (CPS).

Intelligent security systems have evolved enormously in the last few years. Most of these security systems use a group of physics sensors and algorithms for data analysis and communication systems to notify security alarms. Many security systems that are included in doors can detect intruders when they have already opened the door, but not while intruders are forcing upon the door. However, some security systems include preventive systems, which can detect intruders before they open the door. These preventive systems are usually based on video cameras (image processing) or in-presence sensors, which can generate many false positives, for instance, when a person is next to the door for a few seconds, even if this person is not manipulating the door. This research work proposes a novel force door detection system. The system includes a specific device for monitoring door small vibrations and movements; it analyzes these data using neural networks to detect accurately if someone is forcing upon the door. Artificial intelligence must be able to categorize data records without confusing when someone is forcing upon the door with other actions, like knocking on the door.

Learning Analytics has acquire significance on the educational field due to it discovers hidden patterns once the data is processed and integrated in real case of use. Additionally it offers to stakeholder's support to improve their task into educational context. Authors integrated Learning Analytics techniques with a Decision Support System in a proposal to help Educational Institution's administrators in developing decision making process and collaterally improve student performance. In this paper, authors define a Decision Support System and describe its usage in a Educational Institution. The functionalities of Learning Analytics are presented and the integration to help departments administrators to improve decision impact.

The high amount of information that modern multi-criteria group decision making environments must handle requires the development of novel methods. These methods should be able to work with high amounts of alternatives while providing the experts a comfortable framework that they can use to carry out this complex decisions. In this paper, a novel method that tries to solve this issue is presented. Our method makes use of fuzzy ontologies in order to allow the experts to focus on what is more important: the weight that should be given to each criteria. This way, they do deal directly with the high amount of alternatives. Experts decide the importance of each criteria and the alternatives ranking is calculated automatically using the fuzzy ontology.

People rely on other people's opinions to make decisions, especially if they belong to their circle of trust. In addition, there are lots of websites of recognized prestige that provide people opinions about different products and services, which are read by millions of people before making a decision. That is why systems for sentiment analysis are becoming increasingly important to automatically process the information and determine feelings of users. They analyze their written words, usually conditioned by the characteristics of microblogging platforms, in which a large number of messages are published every day, providing a great source of information, impossible to be managed manually. In this work, we show a proposal to analyze the feeling that Twitter users have towards different hotels or hotel chains through a platform that could be easily adapted to other contexts. The goal is to create q a structure based on independent and interchangeable components that will make it possible to conduct studies in a more uniform, open and transparent way.

The lack of anonymity when being examined is a problem for students and teachers alike. So far, student identification in examination processes consists in a physical mark that unmistakably represents the student. New technologies provide us with methods that can handle digital data in an efficient and secured way. Android Operating System for mobile devices has had a steady increase in the number of users during the last six years. On the other hand, due to its popularity the trend for Smartphone devices is to increase their functionalities by adding new technologies such as Near Field Communication (NFC). With the joint use of these technologies, personal data such as name or student identification number can be replaced by a small tag fixed on the examination papers, physically identical to those of the other students in that examination process. This paper describes a framework that allows the student to keep its anonymity throughout the entire process of the examination including correction and exam review.

Students usually find difficult to estimate if their effort in learning courses finds the instructor expectations. They tend to estimate "if they are doing right" by comparing themselves with peers, but this is difficult in online learning scenarios. Grade estimation methods are usually early warning systems targeted to teachers, and few systems are targeted to the students. A4Learning (Alumni Alike Activity Analytics) combines visual feedback methods and visual analytics techniques to provide students with a method to self-estimate their grade by comparing themselves to students from previous course editions. This article details the proposed visualization and presents the validation of the tool with volunteer teachers.