Explore the vast range of titles available.

In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based on real-time object detection systems for weapons auto-detection in video surveillance systems. We propose an early weapon detection framework using state-of-the-art, real-time object detection systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely reducing the number of false alarms in order to employ the model in real-life applications. The model is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth. The model can be employed as a precautionary system to prevent robberies by implying the model in outdoor surveillance cameras.

Blockchain technologies have received considerable attention from academia and industry due to their distinctive characteristics, such as data integrity, security, decentralization, and reliability. However, their adoption rate is still scarce, which is one of the primary reasons behind conducting studies related to users’ satisfaction and adoption. Determining what impacts the use and adoption of Blockchain technologies can efficiently address their adoption challenges. Hence, this systematic review aimed to review studies published on Blockchain technologies to offer a thorough understanding of what impacts their adoption and discuss the main challenges and opportunities across various sectors. From 902 studies collected, 30 empirical studies met the eligibility criteria and were thoroughly analyzed. The results confirmed that the technology acceptance model (TAM) and technology–organization–environment (TOE) were the most common models for studying Blockchain adoption. Apart from the core variables of these two models, the results indicated that trust, perceived cost, social influence, and facilitating conditions were the significant determinants influencing several Blockchain applications. The results also revealed that supply chain management is the main domain in which Blockchain applications were adopted. Further, the results indicated inadequate exposure to studying the actual use of Blockchain technologies and their continued use. It is also essential to report that existing studies have examined the adoption of Blockchain technologies from the lens of the organizational level, with little attention paid to the individual level. This review is believed to improve our understanding by revealing the full potential of Blockchain adoption and opening the door for further research opportunities.

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

Cybersecurity procedures and policies are prevalent countermeasures for protecting organizations from cybercrimes and security incidents. Without considering human behaviors, implementing these countermeasures will remain useless. Cybersecurity behavior has gained much attention in recent years. However, a systematic review that provides extensive insights into cybersecurity behavior through different technologies and services and covers various directions in large-scale research remains lacking. Therefore, this study retrieved and analyzed 2210 articles published on cybersecurity behavior. The retrieved articles were then thoroughly examined to meet the inclusion and exclusion criteria, in which 39 studies published between 2012 and 2021 were ultimately picked for further in-depth analysis. The main findings showed that the protection motivation theory (PMT) dominated the list of theories and models examining cybersecurity behavior. Cybersecurity behavior and intention behavior counted for the highest purpose for most studies, with fewer studies focusing on cybersecurity awareness and compliance behavior. Most examined studies were conducted in individualistic contexts with limited exposure to collectivistic societies. A total of 56% of the analyzed studies focused on the organizational level, indicating that the individual level is still in its infancy stage. To address the research gaps in cybersecurity behavior at the individual level, this review proposes a number of research agendas that can be considered in future research. This review is believed to improve our understanding by revealing the full potential of cybersecurity behavior and opening the door for further research opportunities.

The rapid proliferation of Industry 4.0 technologies has created an urgent need for intelligent and reliable predictive maintenance (PdM) systems. While multi-criteria decision-making (MCDM) frameworks like the Best–Worst Method (BWM) offer structured approaches for prioritizing maintenance tasks, their traditional reliance on subjective expert opinion limits their scalability and adaptability in dynamic industrial settings. This study addresses these limitations by introducing a robust, data-driven framework that integrates machine learning (ML) with BWM. This study presents a framework integrating ML models with BWM, an MCDM technique. While prior work has explored ML for fault detection/classification and hybrid MCDM + ML approaches, our innovation lies in automating BWM weight calculation via ML-derived feature importances, transforming tacit expert knowledge (traditionally subjective) into explicit, data-driven criteria weights aligned with Knowledge Management (KM) principles. The proposed methodology moves beyond a single-model proof-of-concept to present a comprehensive validation blueprint for industrial deployment. The framework’s efficacy is demonstrated using the standard Case Western Reserve University (CWRU) dataset, where rigorous cross-validation and statistical significance testing identified the optimal model, offering a compelling balance of high stability and efficiency for adaptive systems. Furthermore, simulations demonstrated the framework’s real-time viability, with low processing latency, and its resilience to concept drift through an adaptive retraining strategy. By integrating the empirically validated model’s feature importances into the BWM, this work establishes an objective, data-driven, and adaptive system for prioritizing maintenance, thereby advancing the transition toward autonomous and self-optimizing industrial ecosystems.

This systematic study seeks to evaluate the use and impact of transformer models in the healthcare domain, with a particular emphasis on their usefulness in tackling key medical difficulties and performing critical natural language processing (NLP) functions. The research questions focus on how these models can improve clinical decision-making through information extraction and predictive analytics. Our findings show that transformer models, especially in applications like named entity recognition (NER) and clinical data analysis, greatly increase the accuracy and efficiency of processing unstructured data. Notably, case studies demonstrated a 30% boost in entity recognition accuracy in clinical notes and a 90% detection rate for malignancies in medical imaging. These contributions emphasize the revolutionary potential of transformer models in healthcare, and therefore their importance in enhancing resource management and patient outcomes. Furthermore, this paper emphasizes significant obstacles, such as the reliance on restricted datasets and the need for data format standardization, and provides a road map for future research to improve the applicability and performance of these models in real-world clinical settings.

Author Gender Identification (AGI) is an extensively studied subject owing to its significance in several domains, such as security and marketing. Recognizing an author’s gender may assist marketers in segmenting consumers more effectively and crafting tailored content that aligns with a gender’s preferences. Also, in cybersecurity, identifying an author’s gender might aid in detecting phishing attempts where hackers could imitate individuals of a specific gender. Although studies in Arabic have mostly concentrated on written dialects, such as tweets, there is a paucity of studies addressing Modern Standard Arabic (MSA) in journalistic genres. To address the AGI issue, this work combines the beneficial properties of natural language processing with cutting-edge deep learning methods. Firstly, we propose a large 8k MSA article dataset composed of various columns sourced from news platforms, labeled with each author’s gender. Moreover, we extract and analyze textual features that may be beneficial in identifying gender-related cues through their writings, focusing on semantics and syntax linguistics. Furthermore, we probe several innovative deep learning models, namely, Convolutional Neural Networks (CNNs), LSTM, Bidirectional LSTM (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). Beyond that, a novel enhanced BERT model is proposed by incorporating gender-specific textual features. Through various experiments, the results underscore the potential of both BERT and the textual features, resulting in a 91% accuracy for the enhanced BERT model and a range of accuracy from 80% to 90% accuracy for deep learning models. We also employ these features for AGI in informal, dialectal text, with the enhanced BERT model reaching 68.7% accuracy. This demonstrates that these gender-specific textual features are conducive to AGI across MSA and dialectal texts.

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

Integration between information systems is critical, especially in the healthcare domain, since interoperability requirements are related to patients’ data confidentiality, safety, and satisfaction. The goal of this study is to propose a solution based on the integration between queue management solution (QMS) and the electronic medical records (EMR), using Health Level Seven (HL7) protocols and Extensible Markup Language (XML). The proposed solution facilitates the patient’s self-check-in within a healthcare organization in UAE. The solution aims to help in minimizing the waiting times within the outpatient department through early identification of patients who hold the Emirates national ID cards, i.e., whether an Emirati or expatriates. The integration components, solution design, and the custom-designed XML and HL7 messages were clarified in this paper. In addition, the study includes a simulation experiment through control and intervention weeks with 517 valid appointments. The experiment goal was to evaluate the patient’s total journey and each related clinical stage by comparing the “routine-based identification” with the “patient’s self-check-in” processes in case of booked appointments. As a key finding, the proposed solution is efficient and could reduce the “patient’s journey time” by more than 14 minutes and “time to identify” patients by 10 minutes. There was also a significant drop in the waiting time to triage and the time to finish the triage process. In conclusion, the proposed solution is considered innovative and can provide a positive added value for the patient’s whole journey.