516 research outputs found

    A New WRR Algorithm for an Efficient Load Balancing System in IoT Networks under SDN

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    The Internet of Things (IoT) connects various smart objects and manages a vast network using diverse technologies, which present numerous challenges. Software-defined networking (SDN) is a system that addresses the challenges of traditional networks and ensures the centralized configuration of network entities to manage network integrity. Furthermore, the uneven distribution of IoT network load results in the depletion of IoT device resources. To address this issue, traffic must be distributed equally, requiring efficient load balancing to be ensured. This requires the development of an efficient architecture for IoT networks. The main goal of this paper is to propose a novel architecture that leverages the potential of SDN, the clustering technique, and a new weighted round-robin (N-WRR) protocol. The objective of this architecture is to achieve load balancing, which is a crucial aspect in the development of IoT networks as it ensures the network’s efficiency. Furthermore, to prevent network congestion and ensure efficient data flow by redistributing traffic from overloaded paths to less burdened ones. The simulation results demonstrate that our N-WRR algorithm achieves highly efficient load balancing compared to the simple weighted round-robin (WRR), and without the application of any load balancing method. Furthermore, our proposed approach enhances throughput, data transfer, and bandwidth availability. This results in an increase in processed requests

    Two-layer ensemble of deep learning models for medical image segmentation. [Article]

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    One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    An empirical evaluation of m-health service users’ behaviours: A case of Bangladesh

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Mobile health (m-health) services are revolutionising healthcare in the developing world by improving accessibility, affordability, and availability. Although these services are revolutionising healthcare in various ways, there are growing concerns regarding users' service quality perceptions and overall influence on satisfaction and usage behaviours. In developing countries, access to healthcare and low healthcare costs are insufficient if users lack confidence in healthcare service quality. Bangladesh's Directorate General of Health Services (DGHS) provides the only government-sponsored m-health service available to the entire population. DGHS's m-health service, available since 2009, is yet to be evaluated in terms of users' perceptions of the quality of service and its impact on satisfaction and usage. Hence, this study developed a conceptual model for evaluating the associations between overall DGHS m-health service quality, satisfaction, and usage behaviours. This study operationalised overall m-health service quality as a higher-order construct with three dimensions- platform quality, information quality, and outcome quality, and nine corresponding subdimensions-privacy, systems availability, systems reliability, systems efficiency, responsiveness, empathy, assurance, emotional benefit, and functional benefit. Moreover, researchers in various service domains, including- healthcare, marketing, environmental protection, and information systems, evaluated and confirmed the influence of social and personal norms on satisfaction and behavioural outcomes like- intention to use. Despite this, no research has been conducted to determine whether these normative components affect m-health users' service satisfaction and usage behaviours. As a result, this study included social and personal norms along with overall service quality into the conceptual model to assess the influence of these variables on users' satisfaction and m-health service usage behaviours. Data was collected from two districts in Bangladesh- Dhaka and Rajshahi, utilising the online survey approach. A total of 417 usable questionnaires were analysed using partial least squares structural equation modelling to investigate the relationships between the constructs in Warp PLS. The study confirms that all three dimensions of service quality and their corresponding subdimensions influence users' overall perceptions of DGHS m-health service quality. Moreover, overall DGHS m-health service quality has a significant direct association with satisfaction and an indirect association with usage behaviours through satisfaction. While social norms do not influence satisfaction and usage behaviours within the DGHS m-health context, personal norms directly influence users' satisfaction and indirectly influence usage behaviours through satisfaction. Theoretically, the study contributes by framing the influence of users' overall m-health service quality perceptions, social and personal norms on their actual usage behaviours rather than the intention to use. It also extends the existing knowledge by assessing and comparing m-health users' continuous and discontinuous behaviours. Methodologically this study confirms the usefulness of partial least squares structural equational modelling to analyse a complex model including a higher order construct (i.e., overall perceived service quality). Practically, the study demonstrates the importance of users' satisfaction in addition to service quality, as service quality only affects usage behaviours through satisfaction in the current study context. Additionally, knowing that personal norms significantly influence service satisfaction motivates providers of m-health services to strive to enhance users' personal norms toward m-health service to enhance service satisfaction and usage. Overall, the study will help enhance patient outcomes and m-health service usage

    Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

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    Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks

    A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

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    A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi criteria analysis challenges and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore the uncertainty and supply chain performance lead to improved users confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance while development performance can be under development.Comment: Revie

    Network selection based on chi-square distance and reputation for internet of things

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    The internet of things (IoT) has become one of the most important technologies of the 21st century. The IoT environment is composed of heterogeneous IoT communication networks. These technologies are complementary and need to be integrated to meet the requirements of different types of IoT applications that require the mobility of the IoT device under different IoT communication networks. In this paper, the vertical handover decision method is considered to select the appropriate network among different IoT technologies. So, IoT devices, equipped with several radio technologies, can select the most suitable network based on several criteria like quality of service (QoS), cost, power, and security. In this work, a multi-attribute decision-making algorithm (MADM) based on techniques for order preference by similarity to an ideal solution (TOPSIS) that uses chi-square distance instead of Euclidean distance is proposed. The network reputation is added to reduce the average number of handoffs. The proposed algorithm was implemented to select the best technology depending on the requirements of the different IoT traffic classes. The obtained results showed that our proposition outperforms the traditional MADM algorithms

    Cybersecurity model for smart city in Indonesia based on actor-network theory

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    The development of information and communication technology has spread throughout the world, including Indonesia. There are many benefits, but the risks cannot be avoided. Communication is growing massively in cyberspace and thus poses a security threat to smart city services. In Indonesia, cybersecurity has not become a priority and is even considered unimportant. Telecommunications equipment and public service application systems do not yet have sufficient security standards. Policies that regulate all aspects as a standard reference for implementing cybersecurity in smart cities are still needed. Therefore, the purpose of this study is to propose a cybersecurity model in smart cities in Indonesia. In this study, unstructured interviews were conducted as a method for collecting the data. Seven respondents, namely the Director, Head of Cyber and Code Control Section, Head of Operations, and four staff in the Jakarta smart city were chosen as respondents. The initial factors for implementing cybersecurity in Indonesia were identified and analysed qualitatively. Actor-Network Theory (ANT) is used as a theoretical framework to explore the practices of actors involved in implementing cybersecurity in JSC. Moment of translation ANT identified and recommended factors related to cybersecurity. Adapting the Governance Risk and Compliance Model by Jirasek, the identified factors are embedded. In the end, a bureaucracy-based cybersecurity model for smart cities was proposed.The proposed model focuses on people, technology, and process and follows the flow of the government bureaucracy in Indonesia. The bureaucracy-based cybersecurity model for smart cities has three main parts, namely: (1) Cyber Security Stakeholders, (2) Legal Fundamentals of Cyber Security Management, and (3) Security Management. The contributions of this research include (1) critical success factors to guide the development of smart cities with cybersecurity elements, (2) a cybersecurity development model for the government agencies to design and implement cybersecurity in Smart city projects in Indonesia, and (3) evidence that ANT as a useful analytical tool to understand the interaction of social systems and technology, especially related to cybersecurity

    Ranking Agility Factors to Reliably Sustain a Green Industrial Supply Chain Using the Fuzzy Analytic Network Process and Ordinal Priority Approach

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    Suppliers can achieve high levels of supply chain sustainability by improving the related factors. An agile supply chain can support sustainability. Identifying and ranking agility factors in the SAIPA company in Iran to reach a sustainable and green supply chain is the primary purpose of this study. SAIPA is an automotive company with an extensive supply chain. The data were quantitative, and the collection was completed by reviewing the literature and questioning experts. The FANP and the OPA methods were the tools used to analyze the data. These methods are proper for facing multiple-criteria decision-making problems, as in the case of this paper. We first identified the factors (capabilities, enablers, and attributes) using a literature review. After that, we gathered the data for ranking analysis by collecting the opinions of SAIPA’s organizational experts using a pairwise comparison questionnaire for the FANP and a prioritizing list for the OPA. Both methods showed that “Quickness” is the capability with the highest priority. “Customer Sensitivity” was the most critical enabler, and “Accurate customer-based measures” was the most significant attribute of the FANP analysis. The OPA results showed that “Information Management” was the first enabler, and “Efficient funds transfer” took first place among all the attributes. Managers should pay more attention to these factors to develop agile supply chains in the SAIPA company. The results also showed that the methods proposed for multi-attribute decision-making problems like the FANP have shortcomings, such as difficulties completing the pairwise comparison matrix due to burdensome data collection in cases similar to the one in this study with many factors
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