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Money 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). [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

With the advent of internet-enabled and hybrid technologies, information is becoming increasingly accessible to the general public. Smartphones and other gadgets are used extensively by people to share and promote ideas, in a variety of ways. Human interaction and communication has become more reliable and effective through advanced computing technologies. Quantum computing is an emerging paradigm that will change the lives of individuals and the operations of organizations. Quantum computers solve problems at high speed by operating in a superposition state in which the state can be either zero or one at the same instant. Quantum sensors can be used efficiently in technological research to make accurate measurements and collect data that provide new insights into the behavior of nanomaterials. The use of quantum computing could also speed up the manufacturing process of devices with remarkable properties such as superconductivity, high strength or improved signal performance. Quantum computing has the ability to dramatically speed up the development process of various organizations and increase their efficiency and effectiveness. The security and reliability of data and communication is improved by quantum computing techniques such as key generation and entanglement dispersion. Companies use cryptographic algorithms to protect their data. However, with the advent of quantum computing, cryptographic methods that rely on numerical aspects are no longer sufficient to protect data. Quantum computing is an emerging field that is being applied to various problems that previously could not be solved using conventional methods. Quantum computing plays an important role in the field of information processing, where information is precisely analyzed. Various quantum technologies and algorithms are used to secure company data. This paper provides a systematic review of the literature on the principles of quantum computing. The SLR focuses on achieving four aims "identifying a variety of quantum IoT devices, analyzing their importance in different industries, highlighting the challenges of quantum technology, and presenting various techniques used by researchers to overcome different problems". Quantum cryptography is identified as a key strategy for improving the security of IoT systems and ensuring the security and consistency of information. [ABSTRACT FROM AUTHOR]

The development of smart and innovative software applications in various disciplines has inspired our lives by providing various cutting-edge technologies spanning from online to smart and efficient systems. The proliferation of innovative internet-enabled tools has transformed the nation into a globalized world where individuals can participate on various platforms, collaborate in activities, communicate on issues, and exchange information safely and consistently. Coordination and cooperation are essential in software development. It gathers all software developers in one space, encouraging them to discuss goals and work rationally to accomplish the project goal. In recent years, continuous software development and deployment have become increasingly common in software engineering. Continuous software engineering (CSE) is a method that involves a variety of strategies to increase the regularity of novel and modified software versions. CSE enables a continuous learning and improvement process through rapid software update iteration by combining continuous integration and delivery. Continuous integration is a method that has arisen in order to remove gaps between development and deployment. Software engineers must handle uncertainty and alter stakeholders' requirements, which is possible through continuous software developmental strategies that manage the overall software cycle and produce high-quality software applications. The proposed study is a systematic review related to continuous software development and deployment and focuses on achieving four aims: (1) To explore the impacts of continuous development on software, (2) to pinpoint various tools used to carry out this process, (3) to highlight the challenges faced in adopting continuous approaches for development and (4) to analyze the phases of continuous software engineering. [ABSTRACT FROM AUTHOR]

The term "Internet of things (IoT)" refers to a network in which data from all connected devices may be gathered, analyzed, and modified as per requirements to offer new services. IoT devices require a constant Internet connection to exchange data. The volume and speed of data continue to grow quickly with the expansion of IoT devices nowadays. IoT systems frequently use messaging protocols to exchange IoT data. IoT security must be established using advanced techniques as it is vulnerable to many threats. The primary objectives of IoT security are to protect customer privacy, data integrity, and confidentiality, as well as the security of assets and IoT devices and the accessibility of services provided by an IoT ecosystem. In this regard, the IoT must meet user demands while consuming the least number of resources, including money, vitality, and time. The proposed research work is organized into numerous categories to make it easier for researchers and readers to solve and understand security problems in IOT devices. The categories "Features" are identified from available literature, and a specific criterion is adopted for choosing alternatives. The entropy approach to determine criterion relevance by calculating features weights is utilized. The second method "EDAS" approach is used and the alternatives are sorted chronologically based on the criterion weights for easy identification and selection of an effective alternative. Finally, all alternatives are precisely analyzed and ranked. Using our research method, various appropriate features are extracted and are evaluated to solve security issue within IoT devices. The most significant features are ranked to help researchers and manufacturers to focus on security-related issues in IoT devices. [ABSTRACT FROM AUTHOR]

Art design is a method of conveying human feelings and emotions, particularly via the use of visual structures such as paintings or sketches. Every element of our life, including arts and crafts, has been positively affected by the introduction of novel technologies such as artificial intelligence (AI) and machine learning (ML). In today's modern environment, these technologies have altered the techniques of art creation, consumption, and distribution. In today's environment, ML and human emotions are the two most important aspects for interactive and high‐quality art design. Whereas traditional learning systems can be extremely effective in the teaching and learning process of art‐related subjects, AI and ML can be very effective in the teaching and learning process of art‐related subjects for the advancement of learners' artistic skills. The productive and active role of AI and ML in the developments and advancements of art design has been given a very comprehensive and detailed overview in this study. Following a detailed examination of the existing techniques, distinct characteristics have been identified. Six of the most widely utilized features were chosen from the literature to execute the analytical hierarchy process (AHP). For ranking the options based on the weights derived by AHP, the TOPSIS algorithm is used. The option with the best performance came in first, whereas the one with the worst performance came in last. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The advancement in the field of artificial intelligence (AI) has revolutionized every field of life, including the learning of second languages. These intelligent devices are capable of effectively and efficiently utilizing the time and energy of both learners and teachers. Students can learn at their own pace and at their own skill level. They can learn and practice in an interactive and fruitful environment thanks to intelligent chatbots and voice assistants. Students no longer require humanized teachers as a result of the use of these new methodologies; instead, they can learn more effectively by interacting with computer‐assisted systems. With the integration of information and communication technology (ICT) and AI, new technologies like computer‐assisted language learning (CALL) and mobile‐assisted language learning (MALL) are playing a very crucial role in the learning of the English language. Due to the various AI‐based applications and technologies available, learners are unable to use the most valuable and effective ones. This paper focuses on the role of AI in the learning of the English language. The study will help learners in the selection of efficient and effective AI‐powered paradigms for the teaching and learning process of the English language. Various features have been selected from the identified ones, and then, on the basis of these features, different AI‐grounded paradigms for English learning are ranked using analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The alternative with the highest performance value is ranked at the top of all available alternatives, while the one with the lowest performance score is placed at the last. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Improvements in communication and networking technologies have transformed people's lives and organizations' activities. Web 2.0 innovation has provided a variety of hybridized applications and tools that have changed enterprises' functional and communication processes. People use numerous platforms to broaden their social contacts, select items, execute duties, and learn new things. Context: Crowdsourcing is an internet-enabled problem-solving strategy that utilizes human–computer interaction to leverage the expertise of people to achieve business goals. In crowdsourcing approaches, three main entities work in collaboration to solve various problems. These entities are requestors (job providers), platforms, and online users. Tasks are announced by requestors on crowdsourcing platforms, and online users, after passing initial screening, are allowed to work on these tasks. Crowds participate to achieve various rewards. Motivation: Crowdsourcing is gaining importance as an alternate outsourcing approach in the software engineering industry. Crowdsourcing application development involves complicated tasks that vary considerably from the micro-tasks available on platforms such as Amazon Mechanical Turk. To obtain the tangible opportunities of crowdsourcing in the realm of software development, corporations should first grasp how this technique works, what problems occur, and what factors might influence community involvement and co-creation. Online communities have become more popular recently with the rise in crowdsourcing platforms. These communities concentrate on specific problems and help people with solving and managing these problems. Objectives: We set three main goals to research crowd interaction: (1) find the appropriate characteristics of social crowd utilized for effective software crowdsourcing, (2) highlight the motivation of a crowd for virtual tasks, and (3) evaluate primary participation reasons by assessing various crowds using Fuzzy AHP and TOPSIS method. Conclusion: We developed a decision support system to examine the appropriate reasons of crowd participation in crowdsourcing. Rewards and employments were evaluated as the primary motives of crowds for accomplishing tasks on crowdsourcing platforms, knowledge sharing was evaluated as the third reason, ranking was the fourth, competency was the fifth, socialization was sixth, and source of inspiration was the seventh. [ABSTRACT FROM AUTHOR]

The metaverse is touted as an exciting new technology amalgamation facilitating next-level immersive experiences for users. However, initial experiences indicate that a host of privacy, security and control issues will need to be effectively resolved for its vision to be realized. This paper highlights the security issues that will need to be resolved in the metaverse and the underlying enabling technologies/platforms. It also discussed the broader challenges confronting the developers, the service providers and other stakeholders in the metaverse ecosystem which if left unaddressed may hamper its broad adoption and appeal. Finally, some ideas on building a viable Zero-Trust Architecture (ZTA) model for the metaverse are presented. [ABSTRACT FROM AUTHOR]

We know that in today's advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Seamless mobility is always one of the major requirements of modern-day communication. In a heterogeneous and massive IoT environment, efficient network-based mobility protocol such as proxy mobile IPv6 (PMIPv6), is potentially a good candidate for efficient mobility as well as resource utilization efficiency. Several extensions are devised for performance in the research domain. However, a multi-criterion decision-based resourceefficient PMIPv6 extension is required to achieve efficiency when network resources are overloaded. In this research, a multi-criterion decision-based PMIPv6 scheme is devised that provides better performance when the Local Mobility Anchor (LMA) or Mobile Access Gateway (MAG) is overloaded. The objective is achieved by monitoring the load status of MAG or LMA and based on their status, the proposed scheme adapts itself to provide seamless mobility in addition to optimal efficiency. The proposed scheme is compared with the existing LMA and MAG-based mobility management protocol extensions. Based on the analysis of the comparison, the obtained results prove that providing a decision-based PMIPv6 scheme is better for service continuity as well as optimal performance in the context of required buffering, handover efficiency, and necessary signaling cost. [ABSTRACT FROM AUTHOR]

The creation of intelligent applications for numerous types of industry sectors has been accelerated by recent advancements and decision support systems. Wu (Wu [33]) has presented a network security posture assessment paradigm for effective decision-making by the utilization of binary semantic analysis. The high-dimensional, sparse, and noisy big data atmosphere of the sports sector may be learned and selected by the suggested paradigm to integrate novel features. Additionally, the research looked at the benefits of big data analytics in evaluating IAP education for college students and presented a brand-new assessment approach grounded on big data analytics. [Extracted from the article]

Several people around the world have died from the coronavirus (COVID-19) disease. With the increase in COVID-19 cases, distribution, and deaths, much has occurred regarding the ban on travel, border closure, curfews, and the disturbance in the supply of services and goods. The world economy was severely affected by the spread of the virus. Every day, new discussions and debates started, and more people were in fear. Occasionally, unconfirmed information is shared on social media sites as if it were accurate information. Sometimes, it becomes viral and disturbs people's emotions and beliefs. Fake news and rumors are widespread forms of unconfirmed and false information. This type of news should be tracked speedily to prevent its negative impact on society. An ideal system is the dire need of modern-day society to evaluate the Internet rumors on COVID. Therefore, the current study has considered a probabilistic approach for evaluating the Internet rumors about COVID. The fuzzy logic tool in MATLAB was used for experimental and simulation purposes. The results revealed the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

With the development and advancement of information technology, artificial intelligence (AI) and machine learning are applied in every sector of life. Among these applications, music is one that has gained attention in the last couple of years. AI-based innovative and intelligent techniques are revolutionising the music industry. It is very convenient for composers to compose music of high quality using these technologies. Artificial intelligence and music (AIM) is one of the emerging fields used to generate and manage sounds for different media like the Internet, games, etc. Sound effects in games are very effective and can be made more attractive by implementing AI approaches. The quality of the sounds in the game directly impacts the productivity and experience of the player. With computer-assisted technologies, game designers can create sounds for different scenarios or situations like horror and suspense and provide gamers with information. The practical and productive audio of a game can guide visually impaired people during other events in the game. For the better creation and composition of music, a good quality of knowledge about musicology is essential. Due to AIM, there are a lot of intelligent and interactive tools available for the efficient and effective learning of music. Learners can be provided with a very reliable and interactive environment based on artificial intelligence. The current study has considered presenting a detailed overview of the literature available in the area of research. The study has demonstrated literature analysis from various perspectives, which will provide evidence for researchers to devise novel solutions in the field. [ABSTRACT FROM AUTHOR]

The phenomenon of big data has occurred in many fields of knowledge, one of which is astronomy. One example of a large dataset in astronomy is that of numerically integrated time series asteroid orbital elements from a time span of millions to billions of years. For example, the mean motion resonance (MMR) data of an asteroid are used to find out the duration that the asteroid was in a resonance state with a particular planet. For this reason, this research designs a computational model to obtain the mean motion resonance quickly and effectively by modifying and implementing the Symbolic Aggregate Approximation (SAX) algorithm and the motif discovery random projection algorithm on big data platforms (i.e., Apache Hadoop and Apache Spark). There are five following steps on the model: (i) saving data into the Hadoop Distributed File System (HDFS); (ii) importing files to the Resilient Distributed Datasets (RDD); (iii) preprocessing the data; (iv) calculating the motif discovery by executing the User-Defined Function (UDF) program; and (v) gathering the results from the UDF to the HDFS and the.csv file. The results indicated a very significant reduction in computational time between the use of the standalone method and the use of the big data platform. The proposed computational model obtained an average accuracy of 83%, compared with the SwiftVis software. [ABSTRACT FROM AUTHOR]

In this paper, Haar collocation algorithm is developed for the solution of first-order of HIV infection CD4+ T-Cells model. In this technique, the derivative in the nonlinear model is approximated by utilizing Haar functions. The value of the unknown function is obtained by the process of integration. Error estimation is also discussed, which aims to reduce the error of numerical solutions. The numerical results show that the method is simply applicable. The results are compared with Runge-Kutta technique, Bessel collocation technique, LADM-Pade and Galerkin technique available in the literature. The results show that the Haar technique is easy, precise and effective. [ABSTRACT FROM AUTHOR]

A secure delegation has a key role in collaborative and dynamic systems, and it brings new challenges to security mechanisms. Secure delegation requires a flexible model that eliminates the challenges of access control and usage control services and provides separation of duty for static and dynamic circumstances. In this study, we propose a security service model that provides delegation of rights by restricting the delegation of sensitive permission as well as restriction from unauthorized access to resources. It also ensures that the delegation process hides information and privacy from competitors in an effective manner. The proposed model removes the administrative burden and establishes an automated delegation system. In addition, the model focuses on privacy and divides the duties with the fewest possible procedures. Moreover, exercising the model's effectiveness with various circumstances and types of delegates has also been demonstrated. [ABSTRACT FROM AUTHOR]

One such problem is how to select an effective ML algorithm when there are a number of ML algorithms for a cyber-attack detection system for IoT security. Recently, the IoT security research community has endeavoured to build anomaly, intrusion, and cyber-attack traffic identification models using machine learning algorithms for IoT security analysis. Identifying attack traffic is very important for the security of Internet of Things (IoT) in smart cities by using machine learning (ML) algorithms. [Extracted from the article]

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu's thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers. [ABSTRACT FROM AUTHOR]

Flying Ad-hoc Network (FANET) is a new class of Mobile Ad-hoc Network in which the nodes move in three-dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by the pre-defined mechanism which involves no human personnel. Due to the high mobility of nodes and dynamic topology, link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with the highest threshold value by maximizing Packet Delivery Ratio and minimizing End-to-End Delay. In this paper, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing and Link Stability Estimation-based Routing FANET's existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features of collaborative data forwarding and link stability. The simulation results have shown the improved performance of the proposed DLSA routing protocol in contrast to the selected existing ones DPTR and LEPR in terms of E2ED, PDR, Network Lifetime, and Transmission Loss. The Average E2ED in milliseconds of DLSA was measured 0.457 while DPTR was 1.492 and LEPR was 1.006. Similarly, the Average PDR in %age of DLSA measured 3.106 while DPTR was 2.303 and LEPR was 0.682. The average Network Lifetime of DLSA measured 62.141 while DPTR was 23.026 and LEPR was 27.298. At finally, the Average Transmission Loss in dBm of DLSA measured 0.975 while DPTR was 1.053 and LEPR was 1.227. [ABSTRACT FROM AUTHOR]

Energy infrastructure is well thought-out to be one of the complex infrastructures due to its convoluted configuration and automatic control among all of the systems. With such systems, various connections are made for the purpose of configurations. The energy system infrastructure aims to analytically evaluate each element of the system based on fundamental energy branches according to the customer demand. Developing a novel critical evaluation approach for complex energy infrastructure is pertinent to the evaluation of mixed energy system infrastructure. Considering the functional relationships between elements and their infrastructures, a system is needed to overcome the limitations of the current systems. By doing the efficient modeling of enhancing the performance infrastructure of critical energy infrastructure enable better quantitative evaluation of system. The purpose of the proposed study is to develop an evaluation approach for enhancing the performance of critical energy infrastructure. With the help of the proposed approach, efficient multifeature model for enhancing the performance of critical energy infrastructure was experimentally calculated. The experimental setup of the proposed study was done in the Super Decision tool for an efficient multifeature model for enhancing the performance. Results of the experiments reveal the effectiveness of the proposed research. [ABSTRACT FROM AUTHOR]

This article deals with the climate parameters and their possible impact on river discharge in the Hindu Kush region using fuzzy logic approach. The study area possesses a diverse geographical setting and is generally an unexplored region in term of research on climatic variability. All the hydro-climatic variables such as temperature, rainfall, river discharge, and snowfall are extremely challenging to quantity in case of climate change. There should be a comprehensive study to model and measure the significant four significant hydro-climatic parameters relevant to climate change scenario i.e. rainfall, temperature, snowfall, and discharge (runoff). In this study, a fuzzy logic based-rule model was applied to assists the climatic change variability and trend prediction for all four parameters. Specifically the fuzzy logic rule-based model was used for climate change trends detection in uncertain circumstances. A case study focused on the Swat River Basin in Northern Pakistan was used for validation and calibration of this fuzzy technique in MATrix LABoratory (MATLAB). After modelling, the entire four parameters were further divided into three subparameters. Similarly, in the shape of membership functions a total of fifty-three rules have been generated for fuzzy analysis excluding the duplicate values. After fuzzy modelling, it was concluded from the integrated analysis that an extremely close and high climatic trend association has been verified amongst discharge, temperature, rainfall, and snowfall, whereas the rest of the analysis shows fluctuation. [ABSTRACT FROM AUTHOR]

Peer-to-peer networking is a disseminated architecture application which partition the workload among peers. The peers are distributed with equal privileges pertaining the equipotent application. The peers are making portion of their resources including disk storage, processing power, bandwidth, which is in a straight line available to the participants of the network deprived of central management by host. The decision support system in the design of peer-to-peer computing based on lightweight blockchain plays an important role for the smooth activities of the peer-to-peer computing. Early decision making in peer-to-peer computing for the lightweight blockchain can save time, cost, bandwidth, effort, and other resources. A flawless system is the dire need for enhancing the computation efficiency of the peer-to-peer computing. The aim of the proposed research is to offer a decision support system for the uses of lightweight blockchain design for peer-to-peer computing. The SuperDecisions tool was used to plot the hierarchy of situations of the uses of blockchain for the design of peer-to-peer computing. The system enhance the early decision making for the uses of lightweight blockchiarn for effective peer-to-peer computing. The results of the proposed study elaborates the significant use of lightweight blockchain against peer-to-peer computing. [ABSTRACT FROM AUTHOR]

The communication industry is rapidly growing with the passage of time and the number of communication devices is increasing. This increase of communication devices put the devices and their communication into a high risk and security challenges. Intruders tries for capturing important information from such communication devices and are using for their own benefits. To build an effective and accurate decision support system (DSS) for the convergence of Artificial Intelligence (AI) and Internet of Things (IoT) can secure the systems for secure and smooth point-to-point communication. Multi-criteria decision support systems play an important role in decision making for a particular situation based on several security criteria. Decision making based on multi-criteria is one of the exciting issues faced by practitioners and researchers for the convergence of AI and IoT. Numerous DSS are available for making decisions which have the possibility to adopt activities of the decision making. The planned study presented a DSS for the secure convergence of AI and IoT for devices. The experimental work of the proposed study was carried out in the SuperDecisions tool for plotting the hierarchy of security situations based on goal, security criteria, and alternatives. [ABSTRACT FROM AUTHOR]

In this work, the Haar collocation scheme is used for the solution of the class of system of delay integral equations for heterogeneous data communication. The Haar functions are considered for the approximation of unknown function. By substituting collocation points and applying the Haar collocation technique to system of delay integral equations, we have obtained a linear system of equations. For the solution of this system, an algorithm is developed in MATLAB software. The method of Gauss elimination is utilized for the solution of this system. Finally, by using these coefficients, the solution at collocation points is obtained. The convergence of Haar technique is checked on some test problems. [ABSTRACT FROM AUTHOR]

Numerous raised areas are established in the field of fog computing (FC), applied for various purposes, and are evaluated for running analytics on various devices including devices of internet of things and many others in a disseminated way. FC progresses the prototype of cloud computing to network edge leading various possibilities and services. FC improves processing, decision, and intervention to take place through devices of IoT and communicate essential details. The idea of FC in healthcare based on frameworks of IoT is exploited by determining dispersed delegate layer of comprehension between the cloud and sensor hubs. The clouds suggested systems improved to overcome several challenges in ubiquitous frameworks of medical services such as energy efficiency, portability, adaptableness, and quality issues by accommodating right to take care of definite weights of the distant medical services group and sensor networks. The proposed research work has considered the analytic network process (ANP) for selection and ranking of FC-based IoT for health monitoring systems. The approach works in situation when complexity arises for health monitoring. Results of the study show the success of the research for facilitating healthcare. [ABSTRACT FROM AUTHOR]

In the last decade, the Internet of Things (IoT) has grown to connect a large number of smart entities, devices, and components. These connected entities provide a wide range of services to improve the current society of end customers. The Industrial Internet of Things (IIoTs) are revolutionary systems that have linked manufacturing processes with Internet access in order to preciously increase quality of services. These systems have minimized the costs of production through collaboration with electronic objects, accumulating computing, advanced analytics, and smart perception techniques. A demanding analysis of the strengths and limitations of computational models of IIoT is an essential part of the industry and before deciding which approach to use and implement for enhancing usability. Therefore, the goal of this study is to provide feedback and information to the research community and identify patterns in recommendations for future research in the context of process, development, and monitoring of additional technologies of computational models for IIoT. This paper has presented a comprehensive summary of the existing literature on IIoT for providing details about modern industrial revolutions in the context of IIoT. Associated materials were searched and filtered for identification of relevant materials to the proposed study. These materials have been collectively studied with in-depth analysis and then summarized to condense the information of computation models for the readers as well as entrepreneurs. The study will facilitate research community and practitioners to develop novel techniques, algorithms, and tools to automate and facilitate IIoT. This will develop the field of IIoT and will enhance its usability. [ABSTRACT FROM AUTHOR]

Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences. The study also highlights crowdsourcing usage in the field of software engineering. All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed. The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The revolution in Internet of Things (IoT)-based devices and applications has provided smart applications for humans. These applications range from healthcare to traffic-flow management, to communication devices, to smart security devices, and many others. In particular, government and private organizations are showing significant interest in IoT-enabled applications for smart homes. Despite the perceived benefits and interest, human safety is also a key concern. This research is aimed at systematically analyzing the available literature on smart homes and identifying areas of concern or risk with a view to supporting the design of safe and secure smart homes. For this systematic review process, relevant work in the most highly regarded journals published in the period 2016-2020 (a section of 2020 is included)was analyzed. A final set of 99 relevant articles (journal articles, book sections, conference papers, and survey papers) was analyzed in this study. This analysis is focused on three research questions and relevant keywords. The systematic analysis results and key insights will help researchers and practitioners to make more informed decisions when dealing with the safety and security risks of smart homes, especially in emergency situations. [ABSTRACT FROM AUTHOR]

The influential stage of Internet of Things (IoT) has reformed all fields of life in general but specifically with the emergence of artificial intelligence (AI) has drawn the attention of researchers into a new paradigm of life standard. This revolution has been accepted around the globe for making life easier with the use of intelligent devices such as smart sensors, actuators, and many other devices. AI-enabled devices are more intelligent and capable of doing a specific task which saves a lot of resources and time. Different approaches are available in the existing literature to tackle diverse issues of real life based on AI and IoT systems. The role of decision-making has its own importance in the AI-enabled and IoT systems. In-depth knowledge of the existing literature is dire need of the research community to summarize the literature in effective way by which practitioners and researchers can benefit from the prevailing proofs and suggest new solutions for solving a particular problem of AI-enabled sensing and decision-making for the IoT system. To facilitate research community, the proposed study presents a systematic literature review of the existing literature, organizes the evidences in a systematic way, and then analyzes it for future research. The study reported the literature of the last 5 years based on the research questions, inclusion and exclusion criteria, and quality assessment of the selected study. Finally, derivations are drawn from the included paper for future research. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things (IIoHT) is the expansion of the Internet of Health Things (IoHT) which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT. [ABSTRACT FROM AUTHOR]

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise. [ABSTRACT FROM AUTHOR]

Billions of devices are connected via the Internet which has produced various challenges and opportunities. The increase in the number of devices connected to the Internet of things (IoT) is nearly beyond imagination. These devices are communicating with each other and facilitating human life. The connection of these devices has provided opening directions for the smart applications which are one of the growing areas of research. Among these opportunities, security and privacy are considered to be one of the major issues for researchers to tackle. Proper security measures can prevent attackers from interrupting the security of IoT network inside the smart city for secure data traffic. Keeping in view the security consideration of data traffic for smart devices and IoT, the proposed study presented machine learning algorithms for securing the data traffic based on a firewall for smart devices and IoT network. The study has used the dataset of "Firewall" for validation purposes. The experimental results of the approach show that the hybrid deep learning model (based on convolution neural network and support vector machine) outperforms than decision1 rules and random forest by generating a recognition rate of 95.5% for the hybrid model, 68.5% for decision rules, and 78.3% accuracy for random forest. The validity of the proposed model is also tested based on other performance metrics such as f score, error rate, recall, and precision. This high accuracy rate and other performance values show the applicability of the proposed hybrid model to secure data traffic purposes in smart devices. This can be used in many research areas of the smart city for security purposes. [ABSTRACT FROM AUTHOR]

Crowdsourcing is a complex task-solving model that utilizes humans for solving organizational specific problems. For assigning a crowdsourced task to an online crowd, crowd selection is carried out to select appropriate crowd for achieving the task. The efficiency and effectiveness of crowdsourcing may fail if irrelevant crowd is selected for performing a task. Early decisions regarding selection of a crowd can ultimately lead to successful completion of tasks. To select most appropriate crowd from crowdsourcing, this paper presents a decision support system (DSS) for appropriate selection of crowd. The system has been implemented in the Superdecision tool by plotting hierarchy of goals, criteria, and alternatives. Various calculations have been done for performing the proposed research. Results of the study reveal that the proposed system is effective and efficient for selection of crowd in crowdsourcing by performing various pairwise computation of the study. [ABSTRACT FROM AUTHOR]

During the last two decades significant work has been reported in the field of cursive language's recognition especially, in the Arabic, the Urdu and the Persian languages. The unavailability of such work in the Pashto language is because of: the absence of a standard database and of significant research work that ultimately acts as a big barrier for the research community. The slight change in the Pashto characters' shape is an additional challenge for researchers. This paper presents an efficient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques. These feature extraction techniques include, tools such as zoning feature extractor, discrete cosine transform, discrete wavelet transform, and Gabor filters and histogram of oriented gradients. A hybrid feature map is developed by combining the manifold feature maps. This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language. Recognition results are generated for the proposed model based on a manifold and hybrid feature map. An overall accuracy rates of 63.30%, 65.13%, 68.55%, 68.28%, 67.02% and 83% are generated based on a zoning technique, HoGs, Gabor filter, DCT, DWT and hybrid feature maps respectively. Applicability of the proposed model is also tested by comparing its results with a convolution neural network model. The convolution neural network-based model generated an accuracy rate of 81.02% smaller than the multi-class support vector machine. The highest accuracy rate of 83% for the multi-class SVM model based on a hybrid feature map reflects the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Internet of Health Things (IoHT) is an extension of the Internet of Things (IoT), which plays an important role in the observation, consultation, monitoring, and treatment process of remote exchange data processes in healthcare. The integration, computation, and interoperability are facilitated through various sensors, controllers, and actuators. Security is considered as one of the important factors for the communication of these devices to smoothly run the activities of healthcare. The current literature associated to the security of IoHT cover diverse aspects, though there is dire need of the knowledge, which can thoroughly show the review of current state‐of‐the‐art work in a systematic way. To overcome this limitation, the planned study offerings a systematic literature review (SLR) for the IoHT security and provenance. This review will support researchers to take benefit from the present literature and devise novel solutions considering the current study as evidence in IoHT. This paper focused on the security analysis and provenance for the IoHT and answering the defined questions in the current study. [ABSTRACT FROM AUTHOR]

In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain-wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students' domain-wise learning style preferences for better content adaptation. We have developed a web-based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style-based model, and the experimental results show that personalization based on incorporating discipline-wise learning style variations becomes more effective. [ABSTRACT FROM AUTHOR]

During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities. This hybrid model consists of; convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like; Raspberry Pi, Amazon Web services cloud, and GSM modems for remotely accessing and controlling of the home appliances. An android application is developed and deployed on Amazon Web Services (AWS) cloud for the remote monitoring of home appliances. A GSM device and Message queuing telemetry transport (MQTT) are integrated for communicating with the connected IoT devices to ensure the online and offline communication. For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model. The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light. Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34% compared to the conventional recognition model (k nearest neighbours) recognition rate of 76%. [ABSTRACT FROM AUTHOR]

Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters' shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of offline Pashto characters. This database consists of 11352 character images (258 samples for each 44 characters in a Pashto script). Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation. [ABSTRACT FROM AUTHOR]

An aquaponic system is considered to be a sustainable food production solution that follows circular economy principles and the biomimetic natural system to reduce input and waste. It is the combination of two mainly productive systems, a recirculating aquaculture system consists of fish and crustaceans farmed in a tank and hydroponic cultivation consists of vegetable cultured in medium other than soil. Both these systems are well-known around the globe by their performance of production, quality, and verified food safety. An aquaponic system is an industrious mechanism which incorporates impeccably with sustainable growth of intensive agriculture. The existing literature regarding the aquaponic production covers different species of vegetables and fish, a variety of layouts of system, and climate conditions. However, there is a lack of knowledge that can systematically present the existing state-of-the-artwork in a systematic manner. So to overcome this limitation, the proposed research presents a systematic literature review in the field of urban aquaponics. This systematic literature review will help practitioners to take help from the existing literature and propose new solutions based on the available evidence in urban aquaponics. [ABSTRACT FROM AUTHOR]

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]