Advances in Emerging Information and Communication Technology

The term “Clickbait” refers to content that has the express intention of grabbing the reader’s attention. It has become an annoyance for social media users because of the deception contained in the titles of Clickbait. Many studies detect Clickbait using deep learning (DL) and machine learning (ML) models. However, detecting Clickbait in Arabic titles was addressed by a few studies, all of which used ML techniques. This is where our research originated from. In our proposed work Baheta, which is an Arabic synonymous with lying, we suggest utilizing a deep learning model called long short-term memory (LSTM) to identify clickbait in Arabic headlines. In order to extract features from the text, we utilized Word2vec. In this study, we train the model on two Arabic datasets, the first is an unbalanced dataset and the second is a balanced dataset, which is about merging the unbalanced dataset with a fake news dataset. Word2vec provided the model with the best results, with a Macro-F value of 0.79 when applied to the unbalanced (raw) dataset. The LSTM model showed better performance with the unbalanced dataset, as it obtained a higher Macro-F value of 0.02 than that obtained by the LSTM with the balanced dataset.

A regulator has his doxa. One starts describing the main features of this doxa. But there are “breaches” of the doxa. The most interesting one is when the competitive equilibrium is unstable (the number of products sold should decrease). To show it, one uses a model already presented by the author (Bertrand competition, the demands being deduced from the customers’ utilities). A very interesting case is that of a start-up (the product of which can remain on the market or be retrieved). One distinguishes two “styles” of regulation, the “watchdog” one and the “permissive” one, relying on two criteria that are incommensurable. Some examples from the literature on the topic are commented.

Seen as a new concept of the new organization of production within manufacturing enterprises, Industry 4.0 is a new approach that highlights the combination of the management of real objects on the one hand and technological and digital means on the other hand, in order to produce a highly competitive product. Consequently, the selection of this theme serves as a catalyst for developing organizational management across strategic, tactical, and operational levels. This management of organizations tries to highlight the know-how to make humans in their role as collaborators with the machines, especially in the context of certain decisions knowing that there are tasks that can no longer be the subject of automation, which therefore improves the workforce and makes it free and autonomous within its scope.

This study proposes a solution to the shortage of rehabilitation physicians in China and the limited capabilities of most lower limb rehabilitation robots in addressing both sagittal and coronal rehabilitation. Specifically, a new lower limb rehabilitation robot based on a parallel structure was developed to facilitate humans’ lower limb movements in three-dimensional space through a terminal pedal. This study encompasses four aspects of content: (1) simplifying the human lower limb into a skeletal linkage mechanism to determine the maximum range of movement in a human’ sitting position and obtain the expression of lower limb kinematic equations; (2) mechanical structure design of the lower limb rehabilitation robot; (3) conducting kinematics analysis of the proposed parallel mechanism for the rehabilitation training module and obtaining the Jacobian matrix to compute the distribution of condition numbers under known parameters; and (4) conducting trajectory planning and simulation of the proposed mechanism.

Targeted advertising offers significant benefits to advertisers and in turn raises many issues, with privacy issues dominating the list. Big data analytics is the foundation of targeted advertising because it allows for the collection and processing of user data to enable the creation of user segments with varying sizes according to interests, location, or specific characteristics like age, gender, etc. In the past few years, there has been an increase in the number of individuals who are concerned about their privacy over how targeted advertising uses tracking information. Overall, the protection of the personal data of users with effective targeting is crucial for both advertising networks and mobile users. Mobile users would like to see interest-based advertisements when their data are not revealed to the outside world, such as ad networks. Hence, there is a need for such a mobile advertising system that can sustain the current business model of targeted mobile advertising (TMA) and can do so in a way that assists protect the personal information of users instead of just circumventing their privacy concerns. In this paper, we propose a blockchain-based private user-driven mobile advertising system, which we call BC-PUS. It takes advantage of the Ethereum blockchain and smart contracts to control and regulate the advertising process, in addition to preserving the privacy of users. The evaluation results show that the proposed BC-PUS has the required usability and reliability. In comparison with previous studies, our proposed system adopts all the characteristics: user-driven, privacy-preservation, data-encryption, personal data protection law (PDPL) compliance, low-cost, monetary incentive, and authentication that make it a fair and reliable mobile advertising system while the previous studies guarantee only the three characteristics: user-driven, privacy-preservation, and data-encryption.

Recent years have seen a substantial increase in interest in sustainable and responsible finance (SRF) as a result of growing concern over environmental, social, and governance (ESG) concerns. This research article’s goal is to give a thorough summary of SRF, its problems, and its viewpoints. The article starts off by outlining SRF and its purpose. Following that, it covers the numerous SRF subtypes and factors that have led to their development. The essay also analyzes SRF’s prospects and problems, and it concludes by outlining SRF's potential future.

Endoscopic view limitation is a common issue in clinical surgery. This study proposes an end-to-end unsupervised deep learning network for endoscopic view expansion to address this issue. The network includes two sub-networks: an alignment sub-network that calculates the local correlation between image regions based on feature maps, regresses the relative offset between image vertices based on correlation, and solves homography transformation that can align endoscopic images; and a fusion sub-network that reconstructs panoramic images with multi-scale features while preserving image structural information and eliminating artifacts. The experiment shows that the proposed network can form high-quality endoscopic panoramic images under low texture, viewpoint and depth of field changes, and tissue deformation. By expanding the field of vision, the area of vision expansion near the main anatomical structures can reach more than 150%. With this technology, it is not only convenient for real-time monitoring and guidance during surgery but also for medical image processing and analysis, which can better help doctors diagnose and treat diseases.

Aiming at solving the problem of traditional intelligent wheelchair-stretcher robot perception, this paper adopts the flexible pressure sensor to measure the contact pressure between the trunk and the robot and analyzes the correlation between pressure characteristics and pose angles using the gray correlation and multivariable correlation theory, realizing regression prediction of pose angles from the pressure characteristics and providing the technical solution for the intelligent perception of the wheelchair-stretcher robot. The experimental results and evaluation indicated the effectiveness of the force/position perception method.

The digitization of human resources management has been on the agenda for some time now in the priorities of public administrations. Now it counts as one of the most important strategic imperatives. This implies a change both in the working methods and in the skills of the HR collaborators, marking a significant technological revolution for Moroccan public administrations. It calls into question the traditional way in which the HR activities are performed. Faced with many challenges, the HR function aims to be both an administrative expert and a strategic partner. To achieve this, the adoption of a human resources information system seems the most appropriate digital solution for HR managers. This approach is seen as a means to overcome challenges and improve the performance of HR services. In this context, our research aims to highlight the impact of the adoption of an HRIS on the performance of Moroccan public administration, aligning with the digital strategies developed by Morocco.

Dementia affects a large number of people globally, and the number is expected to rise further in the future. This study concentrates on the potential of digital art therapy as a non-pharmacological approach to cater to the growing demand for constructing interactive elements and procedures for people with dementia (PWD). The research develops a framework for identifying the emotional and physical demands of PWD, forming an overview for a deeper understanding of their necessities. The benefits of art therapy for PWD are explored, drawing upon earlier research, and encompass improved emotional state, reduced restlessness, and increased interpersonal engagement. The study underscores the importance of utilizing digital art therapy in an inventive manner as an essential component of the curative journey, underscoring its capacity to enhance PWD’s perception of safety, social engagement, cognitive ability, and physical health. The research also proposes a psychophysiological experiment to gauge the effects of traditional art therapy versus digital art therapy on the emotional stimulation of PWD. The final section of the paper builds an argument for incorporating creative digital art therapy into the standard of care provided to PWD, emphasizing the ways it can improve their quality of life and foster a society that is more understanding and sympathetic.

The dating between Electronic Human Resources (E-HR) control and the combination of Enterprise Resource Planning (ERP) systems inside the medical institution sector represents a strategic project for optimizing human aid control and enhancing the quality of care. E-HR encompasses all human resources control practices based on facts and communique technology, together with the intranet, to automate administrative duties and enhance the institution's online reputation. ERP, on the other hand, is an included software that manages all commercial enterprise processes, facilitating facts sharing among different departments and presenting a comprehensive view of the agency. This article focuses on the implementation of E-HR tools, using the case have a look at of ERP integration at the Hassan II University Hospital Center, a Moroccan Hospital sanatorium . It also presents analyses and pointers for ERP integration within the clinic context and the improvement of human resources.

This article presents the development of a computer vision module for UAVs engaged in firefighting scenarios. The module features two deep neural networks trained on a customized database, which contains three classes of images: fire, smoke, and person. The aim is to give first responders details such as the number of human victims, their state and their positions, and the type of fuel that keeps the fire going, which helps firefighters to better prioritize their actions in a fire scenario and make the intervention safer for them as well. A faster RCNN and an SSD are used in order to detect these three classes, and the best model is then used to help first responders. The model achieves a precision of 0.58 for 50 IoU, 0.68 for the fire class, 0.68 for the person class, 0.50 for the smoke class because of smoke opacity. Even though the confidence score was high in detections, having false detections, especially with the smoke class, made a low precision overall.

The logistics function in public hospitals is undoubtedly important. We are talking about the good reception, the optimization of the transport of goods and materials, and the management of care units and the pharmacy. In other words, planning and organizing the patient’s journey ensures the expected quality. In fact, the need to gain in terms of performance has led public services in several developing countries to introduce management systems inspired by the industrial model, such as lean management.Lean supply chain management in the public sector is a vast field of reflection among researchers and decision-makers. This article aims to contribute theoretically to lean management and to demonstrate its contribution to the supply chain in public hospitals.

Coffee is a significant commercial crop that is grown throughout the world, and it is second only to crude oil in terms of trade volume. For many farmers, it serves as their primary source of daily income. Thus, the primary issues influencing agricultural and economic output in many nations are controlling coffee leaf diseases and ensuring the quality of coffee bean products. One of the most well-known diseases affecting coffee leaves is Rust, followed by Phoma, Cercospora, and Miner. For disease detection and identification, farmers and professionals often use their unaided eyes to observe the plants. However, this strategy could be time-consuming, expensive, and unreliable. Due to the rising interest in using deep learning in farming, numerous studies have demonstrated that image classification is very reliable in recognizing plant diseases. Over the past few years, researchers have attempted to produce deep-learning solutions for cultivation in terms of disease and species classification using convolutional neural networks (CNNs). Therefore, we proposed a framework called Coffee Leaf Quadruple Classifier (CLQC) that is divided into three individual stages and each stage contains four deep learning models used for coffee leaf disease classification. These models are VGG16, EfficientNetB0, DenseNet121, and RestNet152V2, which were selected due to their accurate classification of coffee leaf diseases. The evaluated results indicate that by using deep convolutional models on a set of data, preprocessing, and a variety of supervised deep learning strategies across three distinct phases, the EfficientNetB0 model outperformed other models in all three stages. It achieved 99.91% accuracy in the first stage, 99.45% accuracy in the second stage, and 99.95% accuracy in the third stage.

The relationship between work and family life is one of the main challenges facing individuals, families, professionals, and society today. Of course, the need for this linkage is not new. What has changed are the conditions under which individuals must combine the demands of these two dimensions and the ways in which they are linked.As a result, organizations must become more involved in managing their human resources by adapting their HR approach and offering conditions that allow employees to reconcile their personal and professional lives. Telework remains the most common measure used by organizations for this purpose.

In the OWASP Top 10 Version 2021, the Broken Access Control is ranked first position. That means it is the most exploited vulnerability today by attackers. Because if the attacker can break the access control, he can take administrator privileges and compromise the entire web application. After that, he can launch any type of attack for his purpose. That was the cause of why it was most targeted. In this search, we will present the Broken Access Control vulnerability. First, we talk about its history and then present an overview. After that, we show some research related to our search. Then we reviewed the web application vulnerabilities that attackers may exploit to break the access control. Finally, we talk about protection and security that should be taken against attackers who exploit this vulnerability.

Expressing uncertainty on social media differs from formal language, allowing authors to write in their preferred styles. Investigative activities face challenges in recognizing the level of confidence conveyed in social media texts. Despite existing corpora in other domains, limited attention has been given to the aspect of uncertainty in microblogging. This research focuses on analyzing sentiments expressed on Twitter while considering the semantic uncertainties present within tweets, particularly in relation to the Covid-19 pandemic. A tweet classification algorithm is developed to assess uncertainty and sentiment. The tweets are categorized as “certain” or “uncertain,” with further subcategories of uncertainty including “question,” “condition,” “hope,” and “belief.” The performance obtained from the algorithm demonstrates its effectiveness. Our study found uncertainty in around one-third of tweets, primarily in the form of questions. With regard to sentiments, neutrality was dominant, followed by positivity, while the belief category leaned toward positivity. The research highlights the significance of recognizing uncertainty on social media using contextual semantic cues rather than traditional indicators. Additionally, exploring sub-classes of uncertainty provides valuable insights for managing uncertainty in social media texts. Careful consideration of relevant semantic categories in sentiment analysis, excluding biased categories, is crucial. Based on the findings, refining the “belief” category by considering nuanced types, such as doubt, hesitation, and presumption, is recommended. This refinement would benefit domains focused on truth discovery and investigation. Furthermore, studying the correlation between uncertainty expression and the truth value of statements is suggested, providing deeper insights into how uncertainty influences credibility and truthfulness.

With the increasing prevalence of dementia, there is a growing need for interactive products and services that cater to the unique needs of this population. This chapter explores the transformative potential of co-design and design thinking in empowering individuals with dementia. While advancements have been made, understanding the core needs of people with dementia remains a challenge. To address this, design teams are increasingly advocating for co-design approaches that involve stakeholders, including individuals with dementia, throughout the design process. Co-design workshops, despite their challenges, have emerged as valuable platforms for collaboration and knowledge sharing. By actively engaging individuals with dementia, caregivers, and other stakeholders, designers gain deeper insights into their needs, preferences, and limitations. The chapter emphasizes the importance of embracing co-design and design thinking while explaining that this collaborative approach empowers individuals, promotes independence, and fosters a supportive environment.

This study introduces a novel endovascular intervention robot, especially the delivery mechanism. It is a master-slave robot based on the finger delivery movement of the doctor and developed by the principle of bionics, which can realize the simultaneous translation and rotation motion. The roller design, including the structure and material, is very significant to realize the guidewire delivery function. The delivery mechanism’s mechanical structure and working principle are introduced first in this paper. Secondly, based on the appropriate constitutive model of rubber, the finite element method is used to analyze the influence of the rubber material property on the contact behavior and the friction-based driving force. We propose the indicators to evaluate the delivery performance of the robot. Finally, the simulation results provide an essential reference for selecting the proper outer material for the roller.

Nowadays, with most of the world operating remotely, online banking is very popular in Saudi Arabia. However, fraudsters often set up fake websites or apps to obtain bank account information, which they use to scam and steal money. They may create fake transactions or manipulate genuine ones to transfer funds to another account they own. This widespread problem requires solutions from banks to reduce the incidence of bank fraud. Our proposed system aims to tackle this issue by analyzing user behavior, identifying unusual behavior, and alerting users to stop the process if necessary. In this research, we applied two different models with two alternative datasets: one is real dataset, while the second simulated dataset. This research evaluated the performance of two different models: first is hybrid neuro-fuzzy model based on combination of deep neural networks and fuzzy logic algorithm (DNF), while the second is deep neural network (DNN) model. The result of our experiment showed that the DNN model achieved the highest accuracy by reaching 99.95%, while the DNF model is faster which seems to be more acceptable in real-time transactions.

The paper focused on the advanced intelligent control using hybrid HQ stereo cameras and RPLIDAR sensor systems applied to the navigation in unknown environments of autonomous mobile robots. The main concepts in the technological duality between HQ stereo cameras and RPLIDAR sensors, the software used, the mobile mechatronic system to which the equipment was mounted, and the methods used for the research and testing part of object/obstacle recognition are presented. The simulations and experiments performed were validated through the obtained results, by analyzing in detail the results. The conclusions and proposals related to the research are consistent with the experimental results.

The protection of data requires the implementation of security and privacy measures. A computer system’s first line of defiance is user authentication. Authentication mechanisms include a variety of mechanisms, and one of the newest and most sophisticated is biometric authentication. By using their biometrics, such as a face scan, fingerprint, voice recognition, gait recognition, and eye movement, biometric authentication uses the unique characteristics of each individual to verify and authenticate them. This paper focuses on eye movement. In addition to being robust against spoofing, continuous authentication, and authentication unconscious without specific action by the user, eye movement has other advantages as well. To identify individuals, this system developed an authentication model based on eye movement features.

Artificial Intelligence (AI) is one of the most active fields in science and engineering. It is used in several applications related to computer networks and their security. One of the main goals of AI is to allow machines to work automatically, intelligently and with minimal human intervention. Machine Learning (ML) is a subset of AI that works great for a wide variety of problems. It is used to solve problems with the aim of producing better results than existing traditional techniques. However, it encounters limitations due to the increase in data. This motivated researcher to find an effective alternative called Deep Learning (DL) which gives more accurate results in the presence of massive amounts of data. In this article, we are interested in the field of AI, more specifically DL, to design a biometric system capable of guaranteeing Automatic Facial Recognition to detect and authenticate an individual in real time. Common facial authentication methods involve extracting facial features to compare them to images stored in a database to find a suitable match.

Brains are complex. This organ stores our knowledge, interprets our senses, moves our bodies, and governs our thoughts, emotions, memories, vision, touch, respiration, hunger, body temperature, motor skills, and every other bodily action. To help doctors, we studied and reviewed the most frequent and difficult disorders. The study will focus on using artificial intelligence to diagnose epilepsy, dementia, Parkinson’s disease, and brain tumors. In this paper, we demonstrated how AI approaches can be used in the diagnostic process for a number of prevalent brain diseases and disorders. These include brain tumors, epilepsy, and dementia, particularly the Alzheimer’s disease stage and Parkinson’s disease. The most common dataset sources used in brain research, brain imaging modalities, and neuropsychological tests are then described and separated into open-access and private dataset categories. The most popular performance measures are discussed at the end of this work.

In Saudi Vision 2030 policy, the Saudi government has placed a high value on technical advancement. Therefore, due to Vision 2030 by Saudi Arabia’s government that aims at diversifying its economy and creating more jobs in the country, the importance of the national cybersecurity policy is heightened. As a result, there is always a risk of data security breaches. The effective examination of cloud environment vulnerabilities was presented in many phases. Security measures for such files are lacking in both efficiency and ease of use. A hacker may readily take advantage of this. The most widely utilized technique for data protection is cryptography. Technology that is “practically resilient and impossible to assault” becomes critical. With the use of cryptographic algorithms, this study shows how to improve the security of data stored and shared over the network. Additionally, confidentiality, integrity, and availability (CIA) measures will be ensured in the cloud environment; the most challenging issue is how to protect data given that this information might be kept anywhere in the cloud. So, this study explains the cryptography techniques used by Cloud Service Providers (CSP). The idea of this work is to safeguard information using hybrid encryption while still being efficient using high-level language. This paper aids governments, businesses, and programmers in keeping their data secure in order that it can be utilized in delicate situations.

The Security of individuals, companies and society remains a major issue in controlling access to protected environments. Machine Learning techniques, particularly Depp Learning, are being used to solve this problem and are producing better results than existing techniques. Our work involves the design of an automatic facial recognition system for real-time detection and authentication. Current facial authentication methods involve extracting facial features and comparing them with those stored in a database to find a suitable match.

This paper explores the application opportunities of industry 4.0 technologies with a particular focus on enhancing sustainability and resource efficiency within industrial settings. The main objective of this paper is to design and implement a laboratory-based demonstration that explores an industrial robotic arm’s power consumption and energy efficiency from an Industry 4.0 perspective. The demonstration aims to highlight the potential benefits of horizontal and vertical integration of data, enabling improved information flow across industrial environments. A six-degrees-of-freedom industrial robotic arm is programmed to assemble a four-component product. During this process, data is collected to analyse the effects of payload and operational speed on the product assembly cycle time and energy consumption. The findings from this research lead to a discussion about leveraging data, which is typically confined within the robotic arm, to improve energy efficiencies through Industry 4.0 methods and technologies. Consequently, this results in the development of smarter robotics with enhanced energy efficiency.

Electronic authentication (or “e-authentication”) is one of the key topics in the field of cybersecurity. It is the electronic verification process for identifying an entity. As person using a mobile/computer itself or a mobile/computer program. Authenticate the real user behind attempts to access these systems through secret ways as well. On the other hand, e-authentication applications are widely embraced nowadays. It is a security concern if one has to handle of such kind of service. For example, the present network employs a card-based security system to verify legitimacy of the user accessing, but it is neither reliable nor temporal. The aim of our system is to provide user secure login systems more compliable and reliable. This paper will be useful to eliminate many problems inherent in traditional login techniques in E-government.

Low-code development has gained significant recognition in industry and academia. However, lack of reusability is inherent to existing low-code development platforms. Based on the literature review and practical evaluation, this paper highlights the importance of platform extensibility. Modern technologies are reviewed, among which the essential components are selected for an extensible and open-source low-code development platform. The challenges of designing platform architecture and developing synchronized editable textual and visual representations are ready for us to take in future directions.

Bitcoin is the first and most commonly used digital cryptocurrency in the world. It is also used for electronic transaction though it does not exist physically like hard notes. For investors, it is now been regarded as an investment opportunity. As it is volatile highly in nature, therefore it is required to have good prediction algorithms. On the basis of those algorithms, one can make investment decisions. Cryptocurrency is expanding and getting more attention among people, therefore, a precise prediction of bitcoin price is becoming an important feature in the digital financial market, although there are many studies that have leveraged machine learning for more accurate prediction for bitcoin in which different data structures and data features are adopted. In this paper, we have used the daily price which is the closed price. A comparison has been made between LSTM and deep hybrid neural network (a combination of LSTM and CNN). We found that LSTM outperforms than other algorithms.

In this paper, we propose a blockchain-based platform for decentralized energy marketplaces where prosumers and grid operators can directly exchange flexibility. The proposed platform facilitates the exchange of excess electricity for compensation while ensuring privacy for prosumers and verifiability for grid operators. Specifically, we use digital machine identities (dMIDs) to enable prosumers to verifiably communicate the provision details, ensuring that grid operators can trust the provided flexibility. We use zero-knowledge proofs (ZKP) to verify sensitive data from dMIDs to ensure the privacy of prosumers. Flexibility is represented as a digital certificate written as a smart contract that facilitates the communication and exchange of information about flexibility. This implementation seeks to uncover potential cost savings, create new value streams, and enhance operational efficiency in the renewable energy sector.