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Abstract Internet of Things (IoT) platforms have become the building blocks of any automated system but they are more important in case of industrial systems where sensitive data are captured and handled by the information system. Therefore, it is imperative to deploy the right IoT platform to perform the computational and operational tasks in a better way. During the last few years, an array of IoT technologies/platforms with different capabilities and features were introduced in the markets. This abrupt rise created selection and decision-making issues particularly for the network engineers, designers, and industrial managers due to a lack of technical understanding and skill in this area. Therefore, we present an integrated assessment model focusing on evaluating and ranking IoT platforms in the industrial environment. It encompasses multiple methods such as the proposed model leverages a well-known data collection technique such as Delphi for data collection related to the criteria features. It adopts the Analytic Hierarchy Process (AHP) for giving weights to the criteria features. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been applied for the evaluation of the top twenty (20) Industrial IoT(IIoT) platform alternatives according to the proposed criteria. It selects the most rational choice of IoT platform that can be employed in the Industry 4.0 setting. The proposed integrated assessment model produces the most accurate and consistent outcomes. Hence, it is believed that it can be used as a guideline by different stakeholders like researchers, developers, network engineers, and policymakers for the assessment and deployment of IoT platforms in the industrial environment. It is believed that it is the first kind of multi-methods integrated assessment mode for the assessment, decision-making, and prioritization of IoT technologies in the industry 4.0 domain.

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Abstract The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems

ABSTRACTMoney laundering (ML) is a critical source of extracting the money illegally from the financial system. It is linked to various types of crimes, including corruption, exploitation of a specific community, drug use, and many others. Detection of ML operations is a difficult task on a global scale due to the large volume of financial transactions. However, it also allows criminals to use financial systems to carry out fraudulent transactions. It mainly concern minimizing the potentially risks associated with money laundering. Anti-money laundering-(AML) tools based on AI-driven applications are now tracking transactions to overcome this challenge. A total of 112 research papers are assessed to identify the literature’s gaps and suggest new directions for the research area accordingly. The findings of this systemic literature review work will not only open new paths for the research community, but will also assist the state agencies in developing an optimal AML system to counter these major issues and provide a healthy environment for their residents. This article seeks to assess the existing situation from various angles and open up new pathways for future research directions to investigate and build high levels of authenticity and security in the financial industry using artificial intelligence (AI).

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

The current COVID-19 pandemic has affected almost every aspect of life but its impact on the healthcare landscape is conspicuously adverse. However, digital technologies played a significant contribution in coping with the challenges spawned by this pandemic. In this list of applied digital technologies, the role of immersive technologies in battling COVID-19 is notice-worthy. Immersive technologies consisting of virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), metaverse, gamification, etc. have shown enormous market growth within the healthcare system, particularly with the emergence of pandemics. These technologies supplemented interactivity, immersive experience, 3D modeling, touching sensory elements, simulation, and feedback mechanisms to tackle the COVID-19 disease in healthcare systems. Keeping in view the applicability and significance of immersive technological advancement, the major aim of this study is to identify and highlight the role of immersive technologies concerning handling COVID-19 in the healthcare setup. The contribution of immersive technologies in the healthcare domain for the different purposes such as medical education, medical training, proctoring, online surgeries, stress management, social distancing, physical fitness, drug manufacturing and designing, and cognitive rehabilitation is highlighted. A comprehensive and in-depth analysis of the collected studies has been performed to understand the current research work and future research directions. A state-of-the-artwork is presented to identify and discuss the various issues involving the adoption of immersive technologies in the healthcare area. Furthermore, the solutions to these emerging challenges and issues have been provided based on an extensive literature study. The results of this study show that immersive technologies have the considerable potential to provide massive support to stakeholders in the healthcare system during current COVID-19 situation and future pandemics.

Objective: To study the adjuvant per-operative epidural administration of Triamcinolone in lumbar disc surgery to alleviate post-operative pain. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Oct 2021 to Sep 2022 Methodology: Hundred patients aged 20 to 80 years, of either gender, with a single-level prolapsed intervertebral disc and nerve root compression were included and underwent unilateral single-level decompression. A pre-operative MRI scan of the lumbo-sacral spine was done to confirm the diagnosis. All patients initially received conservative management (analgesics and physiotherapy) but failed to respond to it. They were divided into two groups by the lottery method. In Group A, an epidural steroid was administered, while Group B acted as the control. Pain was recorded pre-operatively at 24 hours and then at 1 and 2 weeks post-operatively using the Visual Analogue Scale. Results: The pre-operative median VAS of Groups A and B was not significant at pre-operation. After 24 hours of operation, the median VAS of Group A patients was VAS 3 (1), while in Group-B it was 2 (1), (p-value of 0.001). After 1 week, the median VAS of Group-A patients was 2 (2) and Group- B was 3 (1), with a significant p value of 0.024. After 2 weeks, the median VAS of Group-A patients was 1 (1) and Group B was 2 (1), with a significant p value of 0.015. Conclusion: Intra-operative epidural and foraminal administration of steroids in single-level lumbar disc surgery significantly reduce...

Physical Layer-based Secret Key Generation (PLSKG) between the legitimate nodes from the reciprocal wireless channel is a vastly studied area of Physical Layer Security (PLS). PLSKG aims to secure the wireless link between the legitimate nodes by symmetrically encrypting the wirelessly transmitted information via a secret key that is extracted from the common randomness of the stochastic wireless channel. PLSKG encompasses the intermediate steps of channel sampling, quantization, information reconciliation, and privacy amplification. The PLSKG algorithms are evaluated in terms of quantifiers such as Key Generation Rate (KGR), Key Agreement Probability (KAP), and randomness. The practical PLSKG algorithms (level-crossing algorithms) extract a secret key by analyzing the channel samples and assigning bit sequences to the channel samples lying in different quantization regions. Level-crossing algorithms are lossy and extract a secret key from the central samples of matched excursions between the legitimate nodes. This results in a reduced KGR as there is a scarcity of such matched excursions considering the fast variations of the wireless link between the legitimate nodes. This paper proposes a Two-Round Channel Parsing (TRCP) algorithm that exploits the correlation between the excursion lengths of the channel samples in addition to the sample correlation. TRCP effectively utilize the channel samples by reducing the sample losses incurred by lossy quantizers exploring a new dimension of correlated excursions of the channel samples between legitimate nodes. Simulation results demonstrate that the proposed TRCP scheme enhances the KGR and KAP performance of the secret key and also passes the National Institute of Standards and Technology (NIST) test suite of randomness.

Abstract The supply chain management (SCM) of COVID-19 vaccine is the most daunting task for logistics and supply managers due to temperature sensitivity and complex logistics process. Therefore, several technologies have been applied but the complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use case due to its multiple features support like excursion notification, data sharing, connectivity management, secure shipping, real-time tracking and monitoring etc. All these features can only feasible through choosing and deploying the right IoT platform. However, selection of right IoT platform is also a major concern due to lack of experience and technical knowledge of supply chain managers and diversified landscape of IoT platforms. Therefore, we introduce a decision making model for evaluation and decision making of IoT platforms that fits for logistics and transportation (L&T) process of COVID-19 vaccine. This study initially identifies the major challenges addressed during the SCM of COVID-19 vaccine and then provides reasonable solution by presenting the assessment model for selection of rational IoT platform. The proposed model applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It also adopts Estimation-Talk-Estimation (ETE) approach for response collection during the survey. As, this is first kind of model so the proposed model is validated and tested by conducting a survey with experts. The results of the proposed decision making model are also verified by Simple Additive Weighting (SAW) technique which indicates higher results accuracy and reliability of the proposed model. Similarly, the proposed model yields the best possible results and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% and 94% respectively. The survey-based testing also suggests that this model can be adopted in practical scenarios to deal with complexities which may arise during the decision making of IoT platform for COVID-19 SCM process.

Abstract Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues against fraudsters. The confirmation of a user legitimation before the money transaction is highlighted by mechanisms and technologies from previous studies that may be helpful in user authentication. This study aims to identify the technologies for user authentication and the opportunity for their transformation to mobile money transaction security despite having all the legally required data for a transaction. This proposed review has identified the role of multifactor authentication techniques for authentication to mitigate the risk of fraudulent transactions—the analysis through 92 articles taken from famous publishers. The most relevant articles address authentication problems, of which 54 percent describe money transaction security, and the rest highlight the supporting technology for user authentication. The study platform described the technology-based approaches with the appreciation of new ideas for secure money transactions. The QR code and multifactor authentication are key terms that increased security by 46%. In addition, this also ensures the user is legitimate using advanced technologies and algorithms to predict and discover transaction risks and discourage fraudsters from trying.

Abstract Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks. The impacts of these security threats can be diminished by providing protection towards the different IoT security features. Different technological solutions have been presented to cope with the vulnerabilities and providing overall security towards IoT systems operating in numerous environments. In order to attain the full-pledged security of any IoT-driven system the significant contribution presented by artificial neural networks (ANNs) is worthy to be highlighted. Therefore, a systematic approach is presented to unfold the efforts and approaches of ANNs towards the security challenges of IoT. This systematic literature review (SLR) is composed of three (3) research questions (RQs) such that in RQ1, the major focus is to identify security requirements or criteria that defines a full-pledge IoT system. This question also focusses on pinpointing the different types of ANNs approaches that are contributing towards IoT security. In RQ2, we highlighted and discussed the contributions of ANNs approaches for individual security requirement/feature in comprehensive and detailed fashion. In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. In RQ3, different security mechanisms presented by ANNs especially towards intrusion detection system (IDS) in IoT along with their performances are comparatively discussed. In this research, 143 research papers have been used for analysis which are providing security solutions towards IoT security issues. A comprehensive and in-depth analysis of selected studies have been made to understand the current research gaps and future research works in this domain.

Verbs in Arabic Language are preceded with specific prepositions: each preposition is called sila for the verb it precedes to. A difference of opinion exists among exegetists and Quran scholars about these prepositions followed by a non-dedicated verb which are used at variance in rhetoric and syntax. Rhetorically, it refers to metaphorical as of majāz, allegory, symbolism and simile whereas syntactically it clears the position of verb and assists in giving the required meaning accordingly. This research aims at a stylistic, rhetoric and syntactic analysis of these preceding prepositions through an empirical study.

Objective: To study the efficacy of per-operative angiogram and Doppler ultrasound as an adjunct to assess the cerebral vessels during intracranial aneurysm clipping surgery. Study Design: Cross-sectional study Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Jan 2020 to Dec 2021. Methodology: Forty-eight diagnosed cases of subarachnoid haemorrhage secondary to ruptured aneurysm were included. All the patients who were not coiled-able (broad-based, multi-lobed, complex) were operated on (craniotomy and clipping of aneurysm). Intraoperative angiogram and Doppler ultrasound of cerebral vasculature were done in all cases to see the patency of vessels and the position of clips. Results: Mean age of the patients was 43.97±8.23 years. 24(50.00%) patients had anterior communicating artery aneurysms, 18(37.50%) had middle cerebral artery aneurysms, whereas 6(12.50%) were of an anterior cerebral artery. Craniotomy and clipping of aneurysm were done in all the cases; among them, 15(31.25%) were operated on early (between 48-96 hrs), whereas 33(68.75%) were operated on after 10-14 days. Per-operatively, cerebral angiogram and Doppler ultrasound were used to assess the cerebral vasculature and position of the clip. About of 5(10.00%) required clip readjustment, 4(8.33%) had parent vessel occlusion, 10(20.83%) required vasodilator therapy due to vasospasm, and 4(8.33%) had residual aneurysm neck. All these were rectified during the same procedure. Conclusion: Per-operative angiography and Doppler ultrasound are useful adjuncts in aneurysmal brain surgery to prevent complications like cerebral ischemia, recurrence, residual aneurysm and re-bleed.

Biometric authentication is gaining the interest of private, public, consumer electronics and corporate security systems. For the protection of cyberspace from hackers and other harmful people, biometric security is growing more and more popular among organizations, individuals and enterprises. The word “cyber security” refers to the procedures, techniques, and tools used to safeguard data, network system, computer networks and software from potential attacks online. Online financial service delivery is referred to as “cyber banking.” As the trend of exchanging things has changed, internet banking has grown. Despite the benefits, there have been instances of security threat-related issues with Internet banking. To identify persons, biometric security verifies their physical attributes and behavioral traits. For identification verification, it is the most reliable and effective physical security method. According to biometric authentication, people can be recognized precisely based on their innate behavioral or physical traits. Numerous security measures have been implemented throughout the entire Internet banking service to address these issues. Globally, cybercrime has deep roots and poses a significant threat to the occurrence of criminal or terrorist behavior. Without being addressed by a single authority, these risks can compromise security on the inside as well as the outside. If the cybercrime goes unnoticed, both money and personal data are lost. Internet services and information infrastructure have previously been targeted in assaults. Online fraud and hacker attacks are only two examples of the daily computer-related crimes that take place. The Internet of Things (IoT) is the most reliable foundation for facilitating high-quality, comfortable human living. IoT has had a substantial impact across a range of application domains. Smart gadgets are more vulnerable to hackers because of their rapid development and trust in wireless mechanics for data transport. As a result, the rate of cybercrime is rising daily. Artificial Intelligence (AI)-based cybersecurity emerges because of technological advancement and poses a risk to public safety, personal property rights, and privacy protection for people. The study elaborates on the key features of biometrics system in conventional and Islamic banking to counter the risk of cybersecurity and provide high safety and security to the banking industry. For this systematic literature review, the most suitable and most relevant 101 articles from the reputed online libraries are selected. This analysis absorbed four research questions and pertinent keywords from the period of 2009 to 2022 (a part of 2023 was included).

Abstract Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Objective: To determine the efficacy of pre-operative meningioma embolization in terms of operating time and intraoperative bleeding. Study Design: Prospective longitudinal study. Place and Duration of Study: Department of Neurosurgery, Combined Military Hospital, Rawalpindi Pakistan, from Jan to Dec 2020. Methodology: Twelve diagnosed cases of intracerebral meningioma were included in the study. The patients were diagnosed cases of meningioma as per pre-operative signs and symptoms, radiological confirmation was done with magnetic resonance imaging, and all were subjected to pre-operative embolization of the tumour with catheter advancement in the arterial system and embolizing the target vessels with polyvinyl alcohol particles, followed by craniotomy and excision of the tumour. Results: The mean age of the patients was 47±672. 24 years. The mean operating time (from the incision to closure) was 96.17±10.23 minutes. In contrast, the intraoperative blood loss was 325.33±15.43 ml. Six (50.00%) individuals had parasagittal meningioma, 4(33.33%) were of cerebral convexity, while 2(16.66%) had lesions at sphenoid wing. Conclusion: Meningioma is a quite vascular tumour, and the operative outcome can be improved by pre-operative angioembolization. The reduced vascularity makes the resection easier by decreasing the per-op bleed and operating time. Keywords: Intraoperative bleed, Meningioma, Operating time, Pre-operative embolization.

Abstract The main purpose of this work is to address the problem of robust grid voltage sensor fault‐tolerant control for a single‐phase two‐level rectifier. First, considering a rectifier with disturbances and grid voltage sensor fault, the standard switched system model is constructed. Based on identical transformation, the original system is converted into an augmented system. In the augmented system, the information of the grid voltage sensor fault is included in the state vector. Second, an unknown input observer is designed for the augmented system for sensor fault estimation. The observer error is totally robust against independent deterministic disturbances. In addition, the effect of the disturbance caused by unknown disturbances or a modelling error is minimised with respect to a prescribed H∞ performance index. Finally, the sensor fault‐tolerant control is realised by fault compensation technique, which protects the rectifier from serious performance degradation in the presence of the grid voltage sensor fault. The proposed fault‐tolerant control strategy requires no supplementary hardware and all the inputs involved in the algorithm are directly available. Simulation and experimental results are exhibited to illustrate the capability of the proposed method to tolerate the grid voltage sensor fault.

Globally, with the maturity of information technology, society is now in the information age, magnifying the significance of integrating innovative applications in different financial and other regulatory arenas. The emergence of financial technology (FinTech) based applications has transformed the traditional banking and regulatory systems and enhanced customer satisfaction by providing a balanced environment for protecting its customers from risky behavior or other potential disruptions. Besides these critical applications of FinTech in the financial industry, it is not developed in the Gulf Cooperation Council (GCC) region as in China, United States, and other developed countries. In order to bridge the gaps in the extant by identifying the critical factors involved, this research work presents a systematic analysis of the available literature reported during the period ranging from 2016 to 2021. This systematic mapping of the extant was performed by selecting five different research questions. The key objectives of this systematic research work are, (1) To identify the barriers that restrict the rise of FinTech in the GCC region, (2) Analyze the behavior of different communities regarding the adaptation of FinTech by evaluating the case studies reported, (3) the impacts of FinTech on different communities in the GCC region. (4) The findings of this research work will not only help the state development bodies by encouraging its stakeholders to use FinTech-driven applications in banking, markets, etc. but it will also help the people in maintaining long-term connection with the Ministries of Foreign Affairs, the Dutch regulators, Economic Affairs, the Dutch Central Bank (DNB), as well as with the Authority on Financial Markets (AFM), and (5) this study work will present new research directions for the research community to explore in the near future.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions.

Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned Aerial Vehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad hoc networks and envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview based on the study of biological inspired routing protocols (Moth-and-Ant and Bee Ad-Hoc) routing protocols.

Communication has become a lot easier in this era of technology, development of high-speed computer networks, and the inexpensive uses of Internet. Therefore, data transmission has become vulnerable to and unsafe from different external attacks. Every communication body wants to secure their data while communicating over the Internet. The internet has various benefits but the main demerit is the privacy and security and the transmission of data over insecure network or channel may happen. Various techniques used for secure communication in order to address these issues, steganography plays an important role. Steganography is the process of obfuscation that makes something incomprehensible and unclear. Different image steganography research methods are proposed recently but each has their advantages and disadvantages and still have necessity to develop some better image steganography mechanisms to achieve the reliability between the basics criteria of image steganography. Therefore, the proposed work, in this paper, is based on the Least Significant Bit (LSB) substitution method. The LSB substitution method can minimize the error rate in embedding process and can achieve greater reliability in criteria, using novel algorithm based on value difference. In this paper, we proposed a novel technique in steganography within the digital images such is RGB, Gray Scale, Texture, Aerial images to achieve higher security, imperceptibility, capacity, and robustness as compared with existing methods. The experimental outcomes of the suggested approach prove further developed strength and justify the feasibility of our research. Through numerical simulations, we observed that the proposed strategy outperformed the next-best current methodology by 5.561 percent in terms of PSNR Correlation score. Additionally, the proposed approach achieved a 6.43 percent better score in PSNR with a variable measure of code inserted in similar images with distinct dimensions. Furthermore, encrypting the same amount of information in images of varying sizes resulted in approximately 6.77 percent improvements. Embedding different sizes of a particular secret message in a different image (such as Gray, Texture, Aerial and RGB images) came out with about 5.466 percent of better score.

The Internet of Thing (IoT) is one of the most imperative technology for all organizations that’s play a vital role in many operations, using communication networks, for exchange of data in order to perform a useful task. However, security of IoT devices and data is a major concern. This research work prioritizes the alternatives of security authentication features from studied articles. The multi-objective optimization method based on the ratio analysis (MOORA) is useful in multi-criteria decision making (MCDM) for ranking the alternatives. The statistical aggregation (SA) method has been used to assign weights to security authentication features in comparison to entropy method. In this paper, we identify weights for authentication features using the proposed SA method. Moreover, we evaluate the accuracy rates of the proposed model using entropy method. Finally, we ranked out the alternatives of authentication features using the MOORA approach. In fact, the entropy weight values came against the initial value of the objects in which the accuracy was 15% which is not suitable to this problem while the accuracy of the SA is 85%. Hence, the accuracy improvement is approximately 70 % using the SA method. This method is applicable for finding the weights of the objects based on initial values by MCDM approaches. We study the key security authentication which is the preserving of confidentiality, integrity, and availability that are the prime objectives for security of an IoT device. Furthermore, challenges are preserving the selected attributes through any approach, as discussed in the literature, adds to the complexity of IoT device security. We identify the future challenges to improve the security of IoT devices.

Security is one of the most critical aspects of software quality. Software security refers to the process of creating and developing software that assures the integrity, confidentiality, and availability of its code, data, and services. Software development organizations treat security as an afterthought issue, and as a result, they continue to face security threats. Incorporating security at any level of the Software Development Life Cycle (SDLC) has become an urgent requirement. Several methodologies, strategies, and models have been proposed and developed to address software security, but only a few of them give reliable evidence for creating secure software applications. Software security issues, on the other hand, have not been adequately addressed, and integrating security procedures into the SDLC remains a challenge. The major purpose of this paper is to learn about software security risks and practices so that secure software development methods can be better designed. A systematic literature review (SLR) was performed to classify important studies to achieve this goal. Based on the inclusion, exclusion, and quality assessment criteria, a total of 121 studies were chosen. This study identified 145 security risks and 424 best practices that help software development organizations to manage the security in each phase of the SDLC. To pursue secure SDLC, this study prescribed different security activities, which should be followed in each phase of the SDLC. Successful integration of these activities minimizing effort, time, and budget while delivering secure software applications. The findings of this study assist software development organizations in improving the security level of their software products and also enhancing their security efficiency. This will raise the developer’s awareness of secure development practices as well.

With the increasing requirements for power system transient stability assessment, the research on power system transient stability assessment theory and methods requires not only qualitative conclusions about system transient stability but also quantitative analysis of stability and even development trends. Judging from the research and development process of this direction at home and abroad in recent years, it is mainly based on the construction of quantitative index models to evaluate its transient stability and development trend. Regarding the construction theories and methods of quantitative index models, a lot of results have been achieved so far. The research ideas mainly focus on two categories: uncertainty analysis methods and deterministic analysis methods. Transient stability analysis is one of the important factors that need to be considered. Therefore, this paper proposed an optimized algorithm based on deep learning for preventive control of the transient stability in power systems. The proposed algorithm accurately fits the generator’s power and transient stability index through a deep belief network (DBN) by unsupervised pre-training and fine-tuning. The non-linear differential–algebraic equation and complex transient stability are determined using the deep neural network. The proposed algorithm minimizes the control cost under the constraints of the contingency by realizing the data-driven acquisition of the optimal preventive control. It also provides an efficient solution to stability and reliability rules with similar safety into the corresponding control model. Simulation results show that the proposed algorithm effectively improved the accuracy and reduces the complexity as compared with existing algorithms.

Pakistan has been a victim of energy crisis since last few decades. This energy crisis has adversely affected country’s socio-economic development and continues to do so. The continuously increasing demand–supply gap has negatively impacted the economic stability of the country. With the recent awareness and development of alternate energy resources like wind and solar, the current energy crisis can be minimized. However, proper planning is essential for successful execution of these renewable energy projects. This study aims to identify the suitable sites for wind farms in District Gwadar, Balochistan using Geographical Information Systems (GIS) and Web-based Spatial Decision Support System (SDSS). In this study, multi-criteria decision making is applied which assists breaking down the site selection complexity. Multi-Criteria evaluation methods provides different set of procedures that facilitate decision making by analyzing different alternatives. The underlying geospatial and ICT technologies used in this analysis form the core component of the planning process. Gwadar is currently drawing investor’s attention due to its geographical location, deep seaport, and proposed China–Pakistan Economic Corridor (CPEC). This research is useful for stakeholders of Wind Energy to explore the wind potentials using GIS as an interactive decision-making tool during the pre-feasibility stage.Furthermore, this research has considered the environmental, social and economic aspects during the decision-making process of wind farm development. This is the strength of multi-criteria evaluation as differently weighted scenarios provide different output, depending on the factors considered of highest importance. A detailed analysis of the sites in terms of their wind potential and energy generation capacity has also been reported in this study. This long coastline of Balochistan with huge wind energy potential has not been explored yet and therefore this study will assist researchers to further explore this area and can have a positive impact on CPEC.

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.

The upcoming generation of communications can provide richer mobility, high data rate, reliable security, better quality of services, and supporting mobility requirements in the Internet of Things (IoT) environment. Integrating modern communication with IoT demands more secure, scalable, and resource-efficient mobility solutions for better business opportunities. In a massive 6G-enabled IoT environment, modern mobility solutions such as proxy mobile IPv6 (PMIPv6) have the potential to provide enhanced mobility and resource efficiency. For supporting richer mobility, a cost-effective and resource-efficient mobility solution is required in a massive 6G-enabled IoT environment. The main objective of the presented study is to provide a resource-friendly mobility solution for supporting the effective integration of future communication in the massive IoT domain. In that context, a location-based, resource-efficient PMIPv6 extension protocol is proposed to provide resource efficiency in terms of required signaling, packet loss, and handover latency. To compare and analyze the proposed model’s effectiveness, mathematical equations are derived for the existing as well as for the proposed solution, and such equations are implemented. Based on the comparison among existing and proposed solutions, the results show that the proposed location-based service-oriented proxy mobile IPv6 extension is resource efficient for supporting mobility in 6G-enabled IoT.

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.

The over-population and the limited energy resources have puzzled the government and private organizations to think about the providence of non-stop energy resources to the industries, hospitals, smart homes, and shopping malls to ensure normal routine activities. To accept this challenge, some researchers put their efforts into generating energy from renewable energy resources (solar, fossil fuels, wind turbines, geothermal energy, and many others) to fulfill the needs of life. While some researchers worked on the efficient utilization of the available energy resources to save the energy for future generations. Inspiring from the second approach, this research work has proposed a systematic allocation of energy resources using the slice-based mechanism in a smart grid environment. This research framework using a hybrid model comprises long short-term memory (LSTM), and a support vector machine (SVM), where the LSTM classifies different energy requests (for allocation of energy resources) while the SVM accomplishes the statistical analysis (to estimate the number of solar energy resources allocated and for a specific interval of time). This need-based allocation of energy resources will not only assist in saving energy resources for future use, but will also improve the life of the power grid and other electric appliances (due to over-usage and burning). The applicability of this model is validated by testing it on a real-time scenario like slice failure conditions, slice overflow conditions, a huge number of requests, and alternate slice allocation conditions. Furthermore, the incoming request classification is also validated based on its accurate identification using a confusion matrix, varying number of hidden layers, accuracy, and time consumption. The outperformance of the selected based on these scenarios and validation metrics reflects the applicability of this framework. Moreover, this framework will assist in reducing overbilling charges and energy savage for future generations due to its need-based allocation of energy resources assignment capabilities.

Software project management inspires and urges the spirit of software developing team members which continues until project completion. Obviously, success of every project based on right selection of team members that ensures to meet the desired requirements of any software developing project. The fundamental aim of current study is to extract and prioritize issues faced by vendors of global software development (GSD) organizations during the selection of right team with having aim to complete the project successfully. As a methodology, a systematic literature review (SLR) used for data extraction and categorization, a questionnaire survey adopted for data validation, and a hierarchical analytical process (AHP) used for prioritizing extracted findings. A total of 12 issues are extracted and grouped into 3 categories (association, teamwork, and fascination). The overall result showed that “association” is the most important category as compare to other categories. Similarly, communication and coordination issues, team's consistency and stability issues, and lack of expertise issues, etc are highlighted as the most critical issues during selection of right people for the right project from vendors' perspective.

Outsourcing has been getting a significant growth for the last few years. Organizations tend to outsource Information Technology (IT), primarily to take advantage of the availability of qualified, trained and skilled workforce in low cost countries across the globe. Outsourcing of IT and software maintenance seem very promising, but a number of factors, risks, and challenges associated with the outsourcing process that make the sourcing decision very complicated. The present study aimed at gaining in-depth understanding of the three aspects of outsourcing, namely; perceived benefits of IT outsourcing, influencing factors of IT outsourcing and software maintenance offshoring. The findings of the current study will lead us to develop a sourcing framework for outsourcing decision as well as a decision support system for software maintenance. A systematic literature review is performed that presents perceived benefits of IT outsourcing, the influencing factors of IT outsourcing and software maintenance. Furthermore, the identified factors are analyzed based on their occurrences in literature as well as chi square test is performed to derive the significant differences amongst the factors based on decades. Similarly, critical success factors are derived both for IT outsourcing and software maintenance offshoring. Our article shows that how the critical success factors impact the IT as well the software maintenance in global delivery perspective. The findings of the current study will help the IT experts and decision makers in making suitable sourcing decisions.

In the modern digital era, software systems are extensively adapted and have become an integral component of human society. Such wide use of software systems consists of large and more critical data that inevitably needs to be secured. It is imperative to make sure that these software systems not only satisfy the users' needs or functional requirements, but it is equally important to make sure the security of these software systems. However, recent research shows that many software development methods do not explicitly include software security measures during software development as they move from demand engineering to their final losses. Integrating software security at each stage of the software development life cycle (SDLC) has become an urgent need. Tackling software security, various methods, techniques, and models have been suggested and developed, however, only a few of them provide strong evidence for building secure software applications. The main purpose of this research is to study security measures in the context of the development of secure software (SSD) during the study of systematic mapping (SMS). Based on the inclusion and exclusion criteria, 116 studies were selected. After the data extraction from the selected 116 papers, these were classified based on the quality assessment, software security method, SDLC phases, publication venue, and SWOT analysis. The results indicate that this domain is still immature and sufficient research work needs to be carried out particularly on empirically evaluated solutions.

Over the last few decades, the development in the field of navigation and routing devices has become a hindering task for the researchers to develop smart and intelligent guiding mechanism at indoor and outdoor locations for blind and visually impaired people (BVIPs). The existing research need to be analysed from a historical perception including early research on the first electronic travel aids to the use of modern artificial vision models for the navigation of BVIPs. Diverse approaches such as: e-cane or guide dog, infrared-based cane, laser based walker and many others are proposed for the navigation of BVIPs. But most of these techniques have limitations such as: infrared and ultrasonic based assistance has short range capacities for object detection. While laser based assistance can harm other people if it directly hit them on their eyes or any other part of the body. These trade-offs are critical to bring this technology in practice.To systematically assess, analyze, and identify the primary studies in this specialized field and provide an overview of the trends and empirical evidence in the proposed field. This systematic research work is performed by defining a set of relevant keywords, formulating four research questions, defining selection criteria for the articles, and synthesizing the empirical evidence in this area. Our pool of studies include 191 most relevant articles to the proposed field reported between 2011 and 2020 (a portion of 2020 is included). This systematic mapping will help the researchers, engineers, and practitioners to make more authentic decisions for finding gaps in the available navigation assistants and suggest a new and enhanced smart assistant application accordingly to ensure safety and accurate guidance of the BVIPs. This research work have several implications in particular the impact of reducing fatalities and major injuries of BVIPs.

Natural scene text classification is considered to be a challenging task because of diversified set of image contents, presence of degradations including noise, low contrast/resolution and the random appearance of foreground (font, style, sizes and orientations) and background properties. Above all, the high dimension of the input image’s feature space is another major problem in such tasks. This work is aimed to tackle these problems and remove redundant and irrelevant features to improve the generalization properties of the classifier. In other words, the selection of a qualitative and discriminative set of features, aiming to reduce dimensionality that helps to achieve a successful pattern classification. In this work, we use a biologically inspired genetic algorithm because crossover employed in such algorithm significantly improve the quality of multimodal discriminative set of features and hence improve the classification accuracy for diversified natural scene text images. The Support Vector Machine (SVM) algorithm is used for classification and the average F-Score is used as fitness function and target condition. First after preprocessing input images, the whole feature space (population) is built using a multimodal feature representation technique. Second, a feature level fusion approach is used to combine the features. Third, to improve the average F-score of the classifier, we apply a meta-heuristic optimization technique using a GA for feature selection. The proposed algorithm is tested on five publically available datasets and the results are compared with various state-of-the-art methods. The obtained results proved that the proposed algorithm performs well while classifying textual and non-textual region with better accuracy than benchmark state-of-the-art algorithms.

Global Software Development (GSD) has been an emerging trend in the development of software globally, for the last two decades. Information Technology (IT) outsourcing includes application development, application maintenance, infrastructure management and business process outsourcing. Software maintenance aims to keep the IT system operational and to fulfill the client requirements. The maintenance is considered the longest phase of software life cycle that consumes about 60-70% of the total software budget. Maintenance of software is not only time consuming but also requires a significant human resources’ ratio. Mostly, software acquisition and maintenance consume a big portion of the total IT budget. The current study aims to evaluate the findings of the systematic literature review and to derive a list of critical success factors regarding offshore outsourcing decision of application maintenance. Thus, an empirical study is performed to validate the influencing factors that were identified by using systematic literature review. These factors are further validated by 93 outsourcing experts from 30 different countries. The collected data through online survey is analyzed based on variables such as respondents experience level, respondents’ locations (continents), experts’ positions. Similarly, the data is analysed based on Chi square test (linear by linear association) and Spearman Rank Correlation. Additionally, the identified factors through survey and systematic literature review are ranked by two different methods. Consequently, a project assessment model is proposed, based on the critical success factors for the sourcing decision of application maintenance.

The Internet-of-drones (IoD) environment is a layered network control architecture designed to maintain, coordinate, access, and control drones (or Unmanned Aerial vehicles UAVs) and facilitate drones’ navigation services. The main entities in IoD are drones, ground station, and external user. Before operationalizing a drone in IoD, a control infrastructure is mandatory for securing its open network channel (Flying Ad Hoc Networks FANETs). An attacker can easily capture data from the available network channel and use it for their own purpose. Its protection is challenging, as it guarantees message integrity, non-repudiation, authenticity, and authorization amongst all the participants. Incredibly, without a robust authentication protocol, the task is sensitive and challenging one to solve. This research focus on the security of the communication path between drone and ground station and solving the noted vulnerabilities like stolen-verifier, privileged-insider attacks, and outdated-data-transmission/design flaws often reported in the current authentication protocols for IoD. We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. Its security has been verified formally using Random Oracle Model (ROM), ProVerif2.02 and informally using assumptions and pragmatic illustration. The performance evaluation proved that the proposed protocol is lightweight compared to prior protocols and recommended for implementation in the real-world IoD environment.

Agile methods are extensively adapted by software development organizations due to the competitive benefits it offers. In recent years global software development (GSD) projects practice agile methods as prominent methods to deliver the software in increments with utmost user satisfaction and affordable cost. Beside the use of agile methods, the software industry has also considered the green aspect of software, to be in line with the demands of the organizations and the world technological ecosystem. The green and sustainable feature of software should focus both the energy and resource efficiency key factors. This phenomenon of embedding the green flavor in software has emerged a new research area, green software engineering, that promises the development of eco-friendly software with minimum energy and use of less computing resources, to trim down the adverse effects on both society and environment. The principal objective of this research study is to design and develop a multi-level Green-Agile Maturity Model (GAMM) to assess the GSD vendors’ agile maturity in terms of green software development. The model has been built in four phases. In phase I and II, systematic literature review (SLR) was performed to identify the success factors and risk factors that either supports or hinders the green and sustainable software development respectively by practicing the agile methods in GSD. The results have been validated from 106 relevant experts, dealing with agile and green software projects, through questionnaire survey. The experts’ demographic represents 25 different countries. We also identified the industry practices through SLR and survey, to address our identified critical factors. Phase III of this research deals with development of the GAMM by categorizing the identified factors into seven Green-Agile maturity levels. A similar approach has been used in other models such as Capability Maturity Model Integration (CMMI), Implementation Maturity Model (IMM) and Software Outsourcing Vendors Readiness Model (SOVRM). In phase IV of this research, five case studies were conducted at GSD organizations, to evaluate the structure and efficacy of the GAMM, while as a major contribution, this paper presents our developed model, the GAMM, which aims to assess the green-agile maturity of the GSD vendors in terms of green and sustainable software development.

Green cloud computing gains popularity over the last decade due to the extensive use of cloud computing. The trend towards cloud computing is spreading at a fast rate. Cloud computing providers extend their data centers at regular intervals. However, cloud providers face severe issues about sustaining the environment because big data centers are responsible for consuming a vast amount of energy and producing a carbon footprint. The solution to address such an issue is green cloud computing. In the literature, a considerable amount of research has been done to determine the challenges and practices in adopting green cloud computing. Still, the field is immature and needs further research to identify the challenges and practices faced and addressed by various stakeholders. This research focuses on identifying the challenges faced by client organizations in adopting the green cloud and the associated practices for addressing the challenges. The challenges and patterns were identified via a systematic literature review from the relevant literature. After thorough conduction of the systematic literature review (SLR), we have identified 9 challenges and 63 practices from 68 papers in the cloud domain. In the future, we plan to develop a comprehensive framework by using some robust soft computing techniques like Fuzzy logic or AHP, etc., to assist the clients in adopting the green cloud environment.

In software engineering field, requirement change management is a challenging job. Ignoring incoming changes results in customer displeasure. It may also result in late product transportation. Managing requirement changes in poor way is the main cause of product failure. It has more diverse effect in global software outsourcing. In software quality requirement change management, it is necessary to address success factors in order to accomplish the requirements of the customers. In this paper, systematic literature review approach is used for documentation and scrutinization of success factors. Total sixteen success factors were recognized having great impact on quality software requirement change management. Our identified success factors like ‘Proper Requirement Change Management’, ‘Rapid Delivery’, ‘Quality Software Product, Access to Market’, ‘Project Management’, ‘Skills and Methodologies’, ‘Low Cost/Effort Estimation’, ‘Clear Plan and Road Map’, ‘Agile Processes’, ‘Low Labor Cost’, ‘User Satisfaction’, ‘Communication/Close Coordination’, ‘Proper Scheduling and Time Constraints’, ‘Frequent Technological Changes’, ‘Robust Model’, ‘Geographical juncture/Cultural differences’ are the crucial factors that affect software quality requirement change. Company size and different database have been used for the analysis of success factors. The databases/search engine used are Google scholar, Science Direct, IEEE Explore and Springer for the exploration of success factors. Companies are analyzed on the basis of their size such as small, medium and large.

With the rapid miniaturization in sensor technology, ruddervator, arduino, and multi-rotor system, drone technology has fascinated researchers in the field of network security. It is of critical significance given the advancement in modern strategic narratives. This has special relevance to drone-related operations. This technology can be controlled remotely by an invisible yet credible operator sitting to a powerful intelligence computer system (PICS) or an airborne control and command platform (AC2P). The two types of drones (reconnaissance and attacking) can communicate with each other and with the PICS or AC2P through wireless network channels referred to as Flying Ad Hoc Network or Unmanned Aerial Vehicular Network (FANET or UAVN). This mode of communication is not without some inconvenience. For instance, when the line of sight is broken, communication is mainly carried out through satellite using GPS (Global Positioning System) signals. Both GPS and UAVN/FANET use open network channels for data broadcasting, which are exposed to several threats, thus making security risky and challenging. This risk is specifically eminent in monitoring data transmission traffic, espionage, troop movement, border surveillance, searching, and warfare battlefield phenomenon, etc. This issue of security risk can be minimized conspicuously by developing a robust authentication scheme for IoD deployment military drones. Therefore, this research illustrates the designing of two separate protocols based on the aggregate signature, identity, pairing cryptography, and Computational Diffie-Hellman Problem (CDHP) to guarantee data integrity, authorization, and confidentiality among drones and AC2P/PICS. More importantly, the outdated data transmission flaw has also been tackled, which is of obvious concern to the past designed protocols. The security of the proposed designs is formally verified using a random oracle model (ROM), a real-or-random (ROR) model, and by informally using pragmatic illustration and mathematical lemmas. Nonetheless, the performance analysis section will be executed using the algorithmic big-O notation. The results show that these protocols are verifiably protected in the ROM and ROR model using the CDHP.

In the era of information society and 5G networks, cars are extremely important mobile information carriers. In order to meet the needs of multi-scenario business requirements such as vehicle assisted driving and in-vehicle entertainment, cars need to interact with the outside world. This interconnection and data transmission process is usually called vehicular communication (V2X, Vehicle-to-Everything). Device-to-device (D2D) communication not only has partial nature of communication, but also alleviate the current problem of spectrum scarcity of resources. The application of D2D communication in V2X can meet the requirements of high reliability and low latency, but resource reuse also brings interference. Software-defined networking (SDN) provides an optimal solution for interoperability and flexibility between the V2X and D2D communication. This paper reviews the integration of D2D and V2X communication from the perspective of SDN. The state-of-the-art and architectures of D2D-V2X were discussed. The similarity, characteristics, routing control, location management, patch scheduling and recovery is described. The integrated architecture reviewed in this paper can solve the problems of routing management, interference management and mobile management. It also overcome the disconnection problem between the D2D-V2X in terms of SDN and provides some effective solutions.

There is a need to identify requirements engineering (RE) practices that are important to global software development (GSD) project success. The objective of this paper is to report our recent empirical study results which aimed to identify the RE practices that are important to GSD projects. This study used an online survey questionnaire to elicit data from 56 RE experts of GSD projects. The survey included 66 RE practices identified by Sommerville et al. for non-GSD projects. The participants were asked to rank each RE practice on a four-point scale to determine the degree of importance of each practice in the context of GSD projects. This research identified a set of six key RE practices that mainly focuses on GSD project stakeholders, scope, standards and requirements traceability management. One common theme that is evident from the RE experts’ feedback analysis is the standardization of requirements documents to reduce requirements inconsistencies and improve communication in diverse and distributed GSD project environments Our results show that not all 66 RE best practices are important for GSD projects. We believe that a good understanding of the identified RE practices is vital in developing and implementing the situation-specific RE processes for GSD projects.

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015 - 2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly.

Internet of Things (IoT) devices are operating in various domains like healthcare environment, smart cities, smart homes, transportation, and smart grid system. These devices transmit a bulk of data through various sensors, actuators, transceivers, or other wearable devices. Data in the IoT environment is susceptible to many threats, attacks, and risks. Therefore, a robust security mechanism is indispensable to cope with attacks, vulnerabilities, security, and privacy challenges related to IoT. In this research, a systematic literature review has been conducted to analyze the security of IoT devices and to provide the countermeasures in response to security problems and challenges by using mobile computing. A comprehensive and in-depth security analysis of IoT devices has been made in light of mobile computing, which is a novel approach. Mobile computing's technological infrastructures such as smartphones, services, policies, strategies, and applications are employed to tackle and mitigate these potential security threats. In this paper, the security challenges and problems of IoT devices are identified by a systematic literature review. Then, mobile computing has been used to address these challenges by providing potential security measures and solutions. Hardware and software-based solutions furnished by mobile computing towards the IoT security challenges have been elaborated. To the best of our knowledge, this is the first attempt to analyze the security issues and challenges of IoT in light of mobile computing and it will open a gateway towards future research.

White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.

Efficient resource optimization is critical to manage the velocity and volume of real-time streaming data in near-real-time data warehousing and business intelligence. This article presents a memory optimisation algorithm for rapidly joining streaming data with persistent master data in order to reduce data latency. Typically during the transformation phase of ETL (Extraction, Transformation, and Loading) a stream of transactional data needs to be joined with master data stored on disk. To implement this process, a semi-stream join operator is commonly used. Most semi-stream join operators cache frequent parts of the master data to improve their performance, this process requires careful distribution of allocated memory among the components of the join operator. This article presents a cache inequality approach to optimise cache size and memory. To test this approach, we present a novel Memory Optimal Index-based Join (MOIJ) algorithm. MOIJ supports many-to-many types of joins and adapts to dynamic streaming data. We also present a cost model for MOIJ and compare the performance with existing algorithms empirically as well as analytically. We envisage the enhanced ability of processing near-real-time streaming data using minimal memory will reduce latency in processing big data and will contribute to the development of high-performance real-time business intelligence systems.

Application maintenance consumes a considerable amount of an organization's time and resources each year. Almost 60% of IT budget is spent alone on application maintenance. The reason of offshore outsourcing of application maintenance is not only the reduction of maintenance cost but to free up the resources and to keep the focus on core products. Offshore outsourcing is a common business strategy that is used by companies to achieve cost savings about 20-50%. However, the decision making process of application maintenance is a complex phenomenon. It is based on a set of influencing factors, clients' requirements and nature of the project. Hence, the current study is aimed at the in-depth investigation of the complex sourcing decision process of application maintenance. Accordingly, a systematic literature review is performed to determine the influencing factors and critical success factors that will be used by the decision makers for the evaluation of projects before making the outsourcing decisions. A total of 15 influencing factors out of 52 selected papers were identified. Based on the defined criteria, amongst the identified factors, only 10 factors were ranked as critical success factors, which are employees' skills, cost, legal requirements, infrastructure, communication, knowledge transfer, maturity level, project management, language barrier and frequent requirements changes. Consequently, a sourcing model was proposed based on the identified critical success factors that help the IT managers and domain experts in making appropriate outsourcing decisions.

Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it's curable at an initial stage to reduce the patient's mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.