ABSTRACT Blockchain and artificial intelligence (AI) are transforming the financial industry by offering novel solutions to long‐standing challenges such as security risks, regulatory compliance, and operational inefficiencies. Despite significant progress, a systematic understanding of the intersection between blockchain and AI in financial risk mitigation is lacking. This study conducts a systematic literature review (SLR) to identify current applications, emerging trends, and security concerns at this intersection. Guided by four research questions, the review examines 134 peer‐reviewed publications sourced from IEEE Xplore, Springer, ScienceDirect, and Wiley databases. A PRISMA framework was used to ensure transparency in article selection. The analysis highlights key areas such as fraud detection, smart contract automation, decentralized identity verification, and regulatory challenges. Our findings contribute to the development of secure, AI‐enhanced blockchain solutions tailored to the financial sector. This review offers practical implications for policymakers, researchers, and financial institutions aiming to foster trust, resilience, and innovation in digital finance. The review's findings underline that the convergence of AI and blockchain offers both opportunities and emerging risks for financial institutions. Unlike previous reviews that examined these technologies separately, this study provides a consolidated understanding of how AI enhances blockchain's resilience and regulatory compliance in digital finance. The results also identify open challenges that should guide future empirical research, including model explainability, governance automation, and cross‐jurisdictional regulation.

Abstract Wikipedia information is extracted by DBpedia and linked to other web resources as Linked Open Data, which is an important contribution to the field of semantics. As part of its internationalisation endeavour, DBpedia now has 20 language chapters that have been mapped to it; nonetheless, there have been very few attempts from Urdu. This article outlines the procedures and highlights the efforts put forward as the first contribution to the manual creation of Urdu mappings with DBpedia Ontology classes. Our approach led to an increase in the number of mapped infoboxes, thus enhancing the DBpedia. The mapping procedure is broken down into two parts. The infobox template is first mapped to the DBpedia ontology's relevant class, and then the attributes of the infobox are mapped to the properties of that class. In addition, alongside other mapped languages, Urdu labels are included to the description of Ontology classes. We have covered around a thousand properties and attributes of Urdu with English DBpedia Ontology on DBpedia mapping server.

Abstract The human eye is responsible for the visual reorganization of objects in the environment. The eye is divided into different layers and front/back areas; however, the most important part is the retina, responsible for capturing light and generating electrical impulses for further processing in the brain. Several manual and automated methods have been proposed to detect retinal diseases, though these techniques are time-consuming, inefficient, and unpleasant for patients. This research proposes a deep learning-based CSR detection employing two imaging techniques: OCT and fundus photography. These input images are manually augmented before classification, followed by training of DarkNet and DenseNet networks through both datasets. Moreover, pre-trained DarkNet and DenseNet classifiers are modified according to the need. Finally, the performance of both networks on their datasets is compared using evaluation parameters. After several experiments, the best accuracy of 99.78%, the sensitivity of 99.6%, specificity of 100%, and the F1 score of 99.52% were achieved through OCT images using the DenseNet network. The experimental results demonstrate that the proposed model is effective and efficient for CSR detection using the OCT dataset and suitable for deployment in clinical applications.

Abstract The trend toward global software development (GSD) has grown tremendously in recent years because of the rapid acceleration in information and communication technologies (ICTs). The reason for the changing trend toward GSD is to develop high‐quality software with minimum cost and time with round the clock. Despite the benefits gained from GSD, there are certain challenges associated with it. Literature reveals that software integration is an integral challenge in the GSD domain and vendors face numerous difficulties in integrating the software components to build the final product. The main aim of this research study is to develop a software integration model (SIM) that will assist GSD vendors to address the factors linked with various stages of software integration. This model is basically designed for GSD vendor organizations to assess and improve their software integration‐related activities. However, it is also beneficial for GSD client organizations in getting knowledge about the status of the GSD vendor organization's software integration capabilities. The research design is composed of well‐known research strategies including systematic literature review (SLR), questionnaire survey, and case study approach. This research study yielded the SIM. We identified nine critical success factors (CSFs) and 10 critical barriers (CBs) in software integration through SLR‐1 by extracting the data from a sample of 105 papers. Similarly, we also identified a total of 132 practices/solutions, for the identified CSFs and CBs in software integration, through SLR‐2 from a sample of 40 papers. These identified factors and practices were validated from 96 global software industry experts/practitioners through online survey. The identified CSFs, CBs, and their associated practices have been arranged adequately across the six software integration levels of SIM. We have adapted SIM structure from capability maturity model integration (CMMI), implementation maturity model (IMM), and software outsourcing vendors readiness model (SOVRM). We also conducted six case studies in the software industry for SIM applicability and validity. The participants' feedback from the six case studies in GSD vendor's organizations portray that the SIM is beneficial for GSD vendors to assess their software integration capability.

Abstract Cloud computing is characterized as a shared computing and communication infrastructure. It encourages the efficient and effective developmental processes that are carried out in various organizations. Cloud computing offers both possibilities and solutions of problems for outsourcing and management of software developmental operations across distinct geography. Cloud computing is adopted by organizations and application developers for developing quality software. The cloud has the significant impact on utilizing the artificial complexity required in developing and designing quality software. Software developmental organization prefers cloud computing for outsourcing tasks because of its available and scalable nature. Cloud computing is the ideal choice utilized for development modern software as they have provided a completely new way of developing real‐time cost‐effective, efficient, and quality software. Tenants (providers, developers, and consumers) are provided with platforms, software services, and infrastructure based on pay per use phenomenon. Cloud‐based software services are becoming increasingly popular, as observed by their widespread use. Cloud computing approach has drawn the interest of researchers and business because of its ability to provide a flexible and resourceful platform for development and deployment. To determine a cohesive understanding of the analyzed problems and solutions to improve the quality of software, the existing literature resources on cloud‐based software development should be analyzed and synthesized systematically. Keyword strings were formulated for analyzing relevant research articles from journals, book chapters, and conference papers. The research articles published in (2011–2021) various scientific databases were extracted and analyzed for retrieval of relevant research articles. A total of 97 research publications are examined in this SLR and are evaluated to be appropriate studies in explaining and discussing the proposed topic. The major emphasis of the presented systematic literature review (SLR) is to identify the participating entities of cloud‐based software development, challenges associated with adopting cloud for software developmental processes, and its significance to software industries and developers. This SLR will assist organizations, designers, and developers to develop and deploy user‐friendly, efficient, effective, and real time software applications.

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation.

Regional and cultural diversities around the world have given birth to a large number of writing systems and scripts, which consist of varying character sets. Developing an optimal character recognition for such a varying and large character set is a challenging task. Unlimited variations in handwritten text due to mood swings, varying writing styles, changes in medium of writing, and many more puzzle the research community. To overcome this problem, researchers have proposed various techniques for the automatic recognition of cursive languages like Urdu, Pashto, and Arabic. With the passage of time, the field of text recognition matured, and the number of publications exponentially increased in the targeted field. It is very difficult to find all the techniques developed, calculate the time and resource consumptions, and understand the cost–benefit tradeoffs among these techniques. These tradeoffs resist making this technology able for practical use. To address these tradeoffs, this article systematic analysis to identify gaps in the literature and suggest new enhanced solution accordingly. A total of 153 of the most relevant articles from 2008 to 2022 are analyzed in thissystematic literature review (SLR)work. This systematic review process shows (1) the list of techniques suggested for cursive text recognition purposes and its capabilities, (2) set of feature extraction techniques proposed, and (3) implementation tools used to design and simulate the empirical studies in this specialized field. We have also discussed the emerging trends and described their implications for the research community in this specialized domain. This systematic assessment will ultimately help researchers to perform an overview of the existing character/text recognition approaches, recognition capabilities, and time consumption and subsequently identify the areas that requires a significant attention in the near future.

In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.

Cloud computing adoption provides various advantages for companies. In particular, hybrid cloud shares the advantages of both the public and private cloud technologies because it combines the private in-house cloud with the public on-demand cloud. In order to obtain benefits from the opportunities provided by the hybrid cloud, organizations want to adopt or develop novel capabilities. Maturity models have proved to be an exceptional and easily available method for evaluating and improving capabilities. However, there is a dire need for a robust framework that helps client organizations in the adoption and assessment of hybrid cloud. Therefore, this research paper aims to present a taxonomy of the challenging factors faced by client organizations in the adoption of hybrid cloud. Typically, such a taxonomy is presented on the basis of obtained results from the empirical analysis with the execution of analytical hierarchy process (AHP) method. From the review of literature and empirical study, in total 13 challenging factors are recognized and plotted into four groups: “Lack of Inclination,” “Lack of Readiness,” “Lack of Adoption,” and “Lack of Satisfaction.” The AHP technique is executed to prioritize the identified factors and their groups. By this way, we found that “Lack of Adoption” and “Lack of Satisfaction” are the most significant groups from the identified challenging factors. Findings from AHP also show that “public cloud security concern” and “achieving QoS” are the upper ranking factors confronted in the adoption of hybrid cloud mechanism by client organizations because their global weight (0.201) is greater than those of all the other reported challenging factors. We also found out 46 practices to address the identified challenges. The taxonomy developed in this study offers a comprehensive structure for dealing with hybrid cloud computing issues, which is essential for the success and advancement of client and vendor organizations in hybrid cloud computing relationships.

Software component plays a significant role in the functionality of software systems. Component of software is the existing and reusable parts of a software system that is formerly debugged, confirmed, and practiced. The use of such components in a newly developed software system can save effort, time, and many resources. Due to the practice of using components for new developments, security is one of the major concerns for researchers to tackle. Security of software components can save the software from the harm of illegal access and damages of its contents. Several existing approaches are available to solve the issues of security of components from different perspectives in general while security evaluation is specific. A detailed report of the existing approaches and techniques used for security purposes is needed for the researchers to know about the approaches. In order to tackle this issue, the current research presents a systematic literature review (SLR) of the present approaches used for assessing the security of software components in the literature by practitioners to protect software systems for the Internet of Things (IoT). The study searches the literature in the popular and well-known libraries, filters the relevant literature, organizes the filter papers, and extracts derivations from the selected studies based on different perspectives. The proposed study will benefit practitioners and researchers in support of the report and devise novel algorithms, techniques, and solutions for effective evaluation of the security of software components.

Network topology is one of the major factors in defining the behavior of a network. In the present scenario, the demand for network security has increased due to an increase in the possibility of attacks by malicious users. In this paper, a blockchain-based system is suggested for securely discovering and storing networks. Techniques such as cloud-based storage systems are not efficient and are lacking in trust, privacy, security, and data control. The blockchain-based technique suggested in this paper is capable of resolving these challenges. Experiments were performed using Mininet, Cisco Packet Tracer, and Ethereum blockchain with the network inference algorithm. This algorithm is capable of inferring the network topology even when only partial information regarding the network is available. The results obtained clearly show that the network is resistant to malicious users and various external attacks, making the network robust.

The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

In this paper, we proposed LCX-MAC (local coordination X-MAC) as an extension of X-MAC. X-MAC is an asynchronous duty cycle medium access control (MAC) protocol. X-MAC used one important technique of short preamble which is to allow sender nodes to quickly send their actual data when the corresponding receivers wake up. X-MAC node keeps sending short preamble to wake up its receiver node, which causes energy, increases transmission delay, and makes the channel busy since a lot of short preambles are discarded, as these days Internet of Things (IoT) healthcare with different sensor nodes for the healthcare is time-critical applications and needs a quick response. A possible improvement over X-MAC is that local information of each node will share with its neighbour node. This local information exchanged will cause much less overhead than in the nodes which are synchronized. To calculate the effect of this the local coordination on X-MAC in this paper, we built an analytical model of LCX-MAC that incorporates the local coordination in X-MAC. The analytical results show that LCX-MAC outperformed X-MAC and X-MAC/BEB in terms of throughput, delay, and energy.

The spam detection is a big issue in mobile message communication due to which mobile message communication is insecure. In order to tackle this problem, an accurate and precise method is needed to detect the spam in mobile message communication. We proposed the applications of the machine learning-based spam detection method for accurate detection. In this technique, machine learning classifiers such as Logistic regression (LR), K-nearest neighbor (K-NN), and decision tree (DT) are used for classification of ham and spam messages in mobile device communication. The SMS spam collection data set is used for testing the method. The dataset is split into two categories for training and testing the research. The results of the experiments demonstrated that the classification performance of LR is high as compared with K-NN and DT, and the LR achieved a high accuracy of 99%. Additionally, the proposed method performance is good as compared with the existing state-of-the-art methods.

Feature-based software birthmark is an essential property of software that can be used for the detection of software theft and many other purposes like to assess the security in end-to-end communication systems. Research on feature-based software birthmark shows that using the feature-based software birthmark joint with the practice of software birthmark estimation together can deliver a right and influential method for detecting software piracy and the amount of piracy done by a software. This can also guide developers in improving security of end-to-end communication system. Modern day software industry and systems are in demand to have an unbiased method for comparing the features-based birthmark of software competently, and more concretely for the detecting software piracy and assessing the security of end-to-end communication systems. In this paper, we proposed a mathematical model, which is based on a differential system, to present feature-based software birthmark. The model presented in this paper provides an exclusive way for the features-based birthmark of software and then can be used for comparing birthmark and assessing security of end-to-end communication systems. The results of this method show that the proposed model is efficient in terms of effectiveness and correctness for the features-based software birthmark comparison and security assessment purposes.

Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

Identifying the employee version regarding the necessities of customers and the status of the technological initiatives is one of the latest strategies of the online banking companies. As many of the studies are limited to collect the opinion of the customer, the other side of the coin is not coming to lime light. So, the present study intended to capture the opinion of the employees regarding two vital aspects -- the ability of the organization to follow the pioneers of the industry with respect to adapting online banking practices and the hurdles that the online banking organizations are facing nationally and internationally. Two research questions are framed accordingly and the opinion of the employee regarding the questions is collected. A total of 90 employees of three reputed banks are selected at random and data is collected from them using the study tool, questionnaire. The collected is analyzed using simple statistical techniques and portrayed for better understanding. For the first question, answer is taken as yes and no and for the second research question, the average of the opinions regarding the social, economic, political and legal environments is considered. Thus, the opinion of the employees about Nigerian technology market is portrayed using pictorial representation. From the figures, it is affirmed that strategies have been being implemented in Nigeria by following the forerunners of the online banking industry. At the same time, it is also opined by majority of employees that the compartmentalized working of the sectors has become a big problem for the development of the online banking. On the whole, it is proved that synchronized services of all the sectors, aimed at customer oriented collaborative strategies can convince the customers to use online banking ventures and hence to upgrade themselves

The exponential growth of information has prompted the introduction of new technologies such as Semantic Web and Common Sense knowledge bases. To connect the different knowledge presentations together is a primary requirement, and ontologies are central we need for this transformation. In this paper we introduce ConceptOnto which is an ontology based on the ConceptNet knowledge base with extension of some of the other properties in some of the more acclaimed upper ontologies. Our goal in the creation of ConceptOnto is readability for humans, and maximizing the functionality while saving the generality of the ontology.