Journal of Ambient Intelligence and Humanized Computing最新文献

Smart agriculture has become one of the most popular technologies for farmers due to its simplicity, ease of deployment, high efficiency, and low overheads. But due to an exponential increase in smart-farming data generation, it is necessary to design secure storage interfaces, that can be scaled for multiple farms. Existing storage models either showcase high security, or high storage efficiency, but a very few models enhance both these parameter sets. Such models are highly complex, and reduce the scalability when applied to large-scale scenarios. To overcome these limitations, this text proposes design of a highly efficient and secure agriculture-record-storage model via reconfigurable blockchains. The proposed model initially uses a multiple crop pattern prediction system via Binary Cascaded Convolutional Neural Network (BC CNN), and deploys a single chained Proof-of-Trust (PoT) based blockchain, that is tuned w.r.t. context of the farms. The prediction is done via weather conditions and soil types. This assists in identification of different crop types, and selection of high trust miner nodes, that can preserve privacy during communication and storage operations. As the blockchain is scaled, a Grey Wolf Optimization (GWO) based model is deployed, which assists in splitting the underlying chain into multiple sidechains. This split is done based on QoS and Security optimizations, which is estimated via temporal miner performance under different farm types. The GWO Model also assists in estimating long-term and high-capacity storage chains, which can be used for archival operations. Due to which, the proposed model is able to improve mining speed by 9.4%, while reducing the energy consumption by 3.5% for different mining operations. The model also defines an indexing strategy for different shards, which assists in increasing data access speed by 12.8% for long-term data sets. Due to these enhancements, the proposed model is capable of deployment for large-scale scenarios.

The aim of this study is to present a novel application of Levenberg–Marquardt backpropagation (LMB) to investigate numerically the solution of functional differential equations (FDE) arising in quantum calculus models (QCMs). The various types of discrete versions of FDM in QCMs are always found to be stiff to solve due to involvement of delay and to overcome the said difficulty, we proposed intelligent computing platform via LMB networks. In order to generate dataset for LMB networks, firstly, the FDEs in QCMs are converted into recurrence relations, then these recurrence systems are solved numerically on a specific input grids in case of both types of FDEs with q-exponential function as well as stable with decreasing behavior characteristics. The training, testing and validation samples based processes are employed to construct LMB networks by exploiting approximation theory on mean square error sense for obtaining the solutions of both types of FDEs. The exhaustive conducted simulation studies for solving FDEs in QCMs via absolute error and mean squared error endorse the accuracy, potential, convergence, stability and worth of proposed technique, which further certified through viable training state parameters, outcomes of error histograms, values of regression/correlation indices.

Abstract

In this work, a wheel-based architecture for 50-gigabit per second next-generation passive optical network stage 2 (50G-NGPON2) represents a promising solution for beyond fifth generation networks. A two-dimensional modified fixed right shifting (2D-MFRS) code is designed and implemented in the proposed architecture to enhance the system capacity and security. The results show that the transmission of 50 Gbps per channel signals over 50–200 km fiber offers high receiver sensitivities of − 17.6 dBm in downlink and − 17.7 dBm in uplink direction with less power penalty of 0.8 dB at the bit error rate of 10^–9. In comparisons with existing optical code division multiple access codes, the proposed architecture using 2D-MFRS code supports upto maximum 260 end subscribers, but also ensures superior performance against the fiber linear and non-linear effects. The simulation results show that the proposed wheel based architecture with 1:128 split ratio drastically improves the fiber reach upto 310 km in uplink and 280 km in downlink direction, compared to other existing passive optical networks (PONs). It is also revealed that the proposed design offers preferable results in terms of high gain and output signal to noise ratio with low noise figure as compared to existing 50 gigabit per second time division multiplexing PON, 50G-NGPON2 and conventional PON. The comparative literature reveals the superiority of proposed design over other existing topologies.

The complex q-rung orthopair fuzzy 2-tuple linguistic set (Cq-ROFTLS), which merges the concepts of complex q-rung orthopair fuzzy sets (Cq-ROFS) and 2-tuple linguistic terms, offers significant advantages in dealing with uncertain and imprecise information during decision-making by effectively representing two-dimensional information within a single set. Notably, the Cq-ROFTLS introduces phase terms that empower experts to express their perspectives flexibly, particularly enhancing its capacity to address periodic elements. To address uncertainty, this approach employs complex values to quantify both membership and non-membership degrees within 2-tuple linguistic environment. Additionally, this research introduces the generalized Maclaurin symmetric mean (MSM) aggregation operator, specifically designed for Cq-ROFTL information. This introduces the Cq-ROFTLMSM and its dual form, the Cq-ROFTL Dual MSM (Cq-ROFTLDMSM), each carrying valuable properties. In cases where the importance of input factors varies, the study proposes the Cq-ROFTL weighted MSM (Cq-ROFTLWMSM) and its dual form, the Cq-ROFTL weighted dual MSM (Cq-ROFTLWDMSM). These operators not only make their debut but also showcase their properties and applications. They flexibly adjust to the significance of inputs, leading to a more refined decision-making process. The methodology extends to address multi-attribute group decision-making (MAGDM) within the Cq-ROFTL framework using the Complex Proportional Assessment (COPRAS) method. The introduction of new aggregation techniques further enhances this approach. A practical illustration involving the selection of the optimal bio-energy production technology (BPT) highlights the real-world effectiveness of the methodology. Through thorough comparisons and a focused exploration of advantages, the study effectively validates the merits of this approach.

In today's highly developed era of information technology, online education is gradually becoming an important teaching mode. Online education provides convenient learning resources and flexible learning methods through online platforms, allowing students to learn according to their own schedule and learning needs. However, compared to traditional education, online education faces some challenges, one of which is how to accurately assess students' learning status. Design an online education student learning status evaluation model based on dual improved neural networks with the aim of improving student learning effectiveness. Using systematic clustering statistical methods to preliminarily analyze the influencing factors of online education students' learning status, and construct an initial evaluation index system; Using the Apriori algorithm to filter the initial indicators, a final online education student learning status evaluation index system is constructed. Using wavelet denoising method to remove noise from evaluation index data, a dual improved radial basis function neural network model is constructed as input. Determine the number of hidden layers in the network using the K-means clustering algorithm, thereby determining the network structure; Based on the optimal network structure, the state transition algorithm is used to adjust the network parameters, and the trained neural network is used for online education student learning state evaluation, outputting the final evaluation result of online education student learning state. The experimental results show that the contribution rate of the model's indicator information reaches 93%, which can accurately evaluate the learning status of online education students based on the optimal model structure and parameters. The above results demonstrate that the constructed model can help teachers and students understand students' learning needs and difficulties in real-time, and provide corresponding teaching support and guidance to promote personalized teaching and improve students' learning experience and outcomes.

An ambient agent may fail to achieve its goal due to the dynamism and nondeterminism of the environment. Based on the concepts of Abilities and Discovery Protocols, we present a context aware failure recovery mechanism for Belief-desire-intention agents. Using a based STRIPS Planner, our approach recovers the failed plan by repairing or replacing faulty actions. Compared to existing works generates the recovery plans dynamically at runtime according to the current context. Indeed, agent abilities are generated and maintained dynamically. To our acknowledge, this is the first time that a dynamic set of abilities has been used in failure recovery.

This paper is the first of a two-part study aiming at building a low-cost visible-light eye tracker (ET) for people with amyotrophic lateral sclerosis (ALS). The whole study comprises several phases: (1) analysis of the scientific literature, (2) selection of the studies that better fit the main goal, (3) building the ET, and (4) testing with final users. This document basically contains the two first phases, in which more than 500 studies, from different scientific databases (IEEE Xplore, Scopus, SpringerLink, etc.), fulfilled the inclusion criteria, and were analyzed following the guidelines of a scoping review. Two researchers screened the searching results and selected 44 studies (-value = 0.86, Kappa Statistic). Three main methods (appearance-, feature- or model- based) were identified for visible-light ETs, but none significantly outperformed the others according to the reported accuracy -p = 0.14, Kruskal–Wallis test (KW)-. The feature-based method is abundant in the literature, although the number of appearance-based studies is increasing due to the use of deep learning techniques. Head movements worsen the accuracy in ETs, and only a very few numbers of studies considered the use of algorithms to correct the head pose. Even though head movements seem not to be a big issue for people with ALS, some slight head movements might be enough to worsen the ET accuracy. For this reason, only studies that did not constrain the head movements with a chinrest were considered. Five studies fulfilled the selection criteria with accuracies less than (2^{circ }), and one of them is illuminance invariant.

With the rapid development and wide proliferation of sensor devices and the Internet of Things (IoT), machine learning algorithms processing and analysing one or more modalities of sensory signals have become an active research field given its numerous applications, particularly in the domestic intelligent environment (DIE). In the past decades, the research on sensing and interactive devices of DIE and deep learning (DL) based methods have become strikingly popular. Several missions, such as the pro- cessing and analysis of sensing signals related to domestic instruments and the control of certain devices to act upon the results, comprise the main working targets in DIE. The goal of this review is to provide a brief overview of the aforementioned sensors, their related DL algorithms and their applications. To comprehend the ideas behind the use of various devices found in domestic intelligent instruments, we first summarize the available information. Then, to quantify and adapt the residents’ knowledge of the household environment, we review data-driven learning techniques based on the aforementioned sensor-based devices and introduce robotic applications that provide helpers and action outputs in the environment. Finally, we investigate the commonly utilized datasets relevant to DIE and human activ- ity recognition (HAR) and explore the challenges and prospects of their applications in the DIE field.

This paper presents a rich class of information theoretical measures designed to enhance the accuracy of portfolio risk assessments. The Mean-Variance model, pioneered by Harry Markowitz, revolutionized the financial sector as the first formal mathematical method to risk-averse investing in portfolio optimization theory. We analyze the effectiveness of this with the models that replace expected portfolio variance with measures of information (uncertainty of the portfolio allocations to the different assets) and five major practical issues. The empirical analysis is carried out on the historical data of Indian financial stock indices by application of portfolio optimization problem with information measures as the objective function and constraints derived from the return and the risk. Our findings indicate that the information measures with parameters can be used as an adequate supplement to traditional portfolio optimization models such as the mean-variance model.