Intelligence Paradigm Research Articles

An insight on UAV/drone autonomous navigation methods and applications: a review

In the past decade, unmanned aerial vehicles (UAVs) are gaining a lot amount of importance in the field of research and development. These UAVs have applications ranging from surveillance, the cinematography in movies, communication, transportation, agriculture, delivery of goods, and rescue operations in various scenarios. The navigation in drones is always a significant challenge as it depends on multiple aspects of technologies, which include both hardware and software. This survey paper lists the importance of UAVs and their various applications. The paper encompasses both the indoor and outdoor aspects of navigation. It describes various properties for navigating, like drone control architecture, deployed hardware-technologies, implemented algorithmic models, drone trajectory planning, and drone communications technologies requirements. Additionally, we also focus on the role of artificial intelligence paradigms for autonomous drone navigation. Vision-based navigation of UAVs using locality sensors and cameras has also discussed with the safety aspects of a drone's successful operation. This paper presents a review of the essential aspects of drone navigation methods and their applications. In the end, this survey reports the essential requirements of UAVs in civil applications from a navigation point of view.

A Computational Intelligence Paradigm with Human Computer Interface Learning

The cognitive Science is the leading technology which works on the principle of Neuroscience. Human Computer Interface is a challenging approach in neurosciences, which is the leading method to handle the brain activities to control external communications with the electronic devices for physically challenged human beings. The various HCI applications are developed with this advance technology. This helps in various patients which are physically challenged or facing the lock in syndrome, a condition where limbs are not functioning to full extent. Therefore, this paper is the review paper to the various EEG signal classification techniques using different taxonomy with techniques like linear, nonlinear, stableubstable, static-discriminant to design various HCI applications.

Ambient Intelligence in the Living Room.

The emergence of the Ambient Intelligence (AmI) paradigm and the proliferation of Internet of Things (IoT) devices and services unveiled new potentials for the domain of domestic living, where the line between “the computer” and the (intelligent) environment becomes altogether invisible. Particularly, the residents of a house can use the living room not only as a traditional social and individual space where many activities take place, but also as a smart ecosystem that (a) enhances leisure activities by providing a rich suite of entertainment applications, (b) implements a home control middleware, (c) acts as an intervention host that is able to display appropriate content when the users need help or support, (d) behaves as an intelligent agent that communicates with the users in a natural manner and assists them throughout their daily activities, (e) presents a notification hub that provides personalized alerts according to contextual information, and (f) becomes an intermediary communication center for the family. This paper (i) describes how the “Intelligent Living Room” realizes these newly emerged roles, (ii) presents the process that was followed in order to design the living room environment, (iii) introduces the hardware and software facilities that were developed in order to improve quality of life, and (iv) reports the findings of various evaluation experiments conducted to assess the overall User Experience (UX).

L’idée D’Université à L’heure des Démocraties Modernes : Quel Projet ?

With this proposal, we wish to revisit the Idea of the University in the perspective of the democratic project of the American philosopher and pragmatist John Dewey. Our hypothesis consists in the thesis that the deweyen project confronts the problem of the social distribution of knowledge, in the aim of giving us the means to transform the latter. In this way, if we consider that the university is part of this distribution through its student learning function, then our purpose is to demonstrate that this learning is currently guided by an individual intelligence paradigm harms that the development of collective intelligence. For John Dewey, the latter is one of the central means of his democratic project which would reconcile the Idea of a University with real University institutions. Therefore, according to him, if universities want to contribute to the democratic project, we must adopt a new pedagogical orientation promoting individual and collective intelligence.

Open Knowledge Graph of Historical Heritages for Korean Religions

The Knowledge Graphs (KGs) as the emerging artificial intelligence (AI) paradigm play a crucial role in knowledge representing and sharing to achieve intelligent knowledge services. This paper addresses a new Linked Open Data (LOD) development method that can realize KGs of the diverse domains from the practical perspectives. The proposed approach is based on two types of separation of concerns for the effective development of LOD data sets：conceptual layer separation between schema and instances, and view separation between LOD management system and user interface. In order to mediate separated concerns, a LOD Interface Sheet (LIS) is implemented to create a domain-specific LOD development environment. The paper implements the proposed method that can generate the domain-specific LOD development system providing the necessary functionalities for LOD management as well as KG realization. This paper also presents the development of an open knowledge graph of historical heritages for Korean religions to demonstrate the capability of the implemented i-Manager and the effectiveness of KGs in scholarly knowledge management.

Bases epistemológicas que sustentan la teoría de las inteligencias múltiples de Howard Gardner en la pedagogía

La Teoría de las Inteligencias Múltiples fue propuesta por el estadounidense Howard Gardner como contrapeso al paradigma tradicional de una inteligencia única y ha revolucionado la concepción de la inteligencia en la pedagogía. El objetivo fue determinar las bases epistemológicas de la teoría de las inteligencias múltiples de Howard Gardner en la pedagogía. El estudio corresponde al enfoque cualitativo de tipo teórico de análisis retrospectivo para el cual se ha usado la hermenéutica y como técnica el análisis del contenido. Los resultados y discusión de la investigación subsumen una interpretación teórica sistemática de autores respecto a las bases epistemológicas que sustentan la teoría de las inteligencias múltiples en la pedagogía. Se concluye que las bases epistemológicas de la Teoría de las Inteligencias Múltiples de Howard Gardner se encuentran en la psicología humanista de la educación, la neuropsicología y el modelo holístico de la educación.

Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory.

Analyzing multimodal transportation problem and its application to artificial intelligence

In recent decades, there has been increased interest among both transportation researchers and practitioners in exploring the application of artificial intelligence (AI) paradigms to address the real-life problems in order to improve the efficiency, safety and environmental compatibility of transportation systems. In this paper, our main interest is to solve transportation problem by considering the multimodal transport systems and then utilize it to solve neural network (NN) problem in AI. The multimodal transportation problem (MMTP) is nothing but a linear programming problem, and so it is easy to solve by any simplex algorithm. To analyze the proposed method, a numerical example is included and solving it we reveal a better impact for analyzing the real-life decision-making problems. Thereafter, we revoke our approach for solving NN problems, which enhances a connection between MMTP and NN problems. Finally, conclusion and future research directions are presented regarding our study.

Smart Management Consumption in Renewable Energy Fed Ecosystems.

Advances in embedded electronic systems, the development of new communication protocols, and the application of artificial intelligence paradigms have enabled the improvement of current automation systems of energy management. Embedded devices integrate different sensors with connectivity, computing resources, and reduced cost. Communication and cloud services increase their performance; however, there are limitations in the implementation of these technologies. If the cloud is used as the main source of services and resources, overload problems will occur. There are no models that facilitate the complete integration and interoperability in the facilities already created. This article proposes a model for the integration of smart energy management systems in new and already created facilities, using local embedded devices, Internet of Things communication protocols and services based on artificial intelligence paradigms. All services are distributed in the new smart grid network using edge and fog computing techniques. The model proposes an architecture both to be used as support for the development of smart services and for energy management control systems adapted to the installation: a group of buildings and/or houses that shares energy management and energy generation. Machine learning to predict consumption and energy generation, electric load classification, energy distribution control, and predictive maintenance are the main utilities integrated. As an experimental case, a facility that incorporates wind and solar generation is used for development and testing. Smart grid facilities, designed with artificial intelligence algorithms, implemented with Internet of Things protocols, and embedded control devices facilitate the development, cost reduction, and the integration of new services. In this work, a method to design, develop, and install smart services in self-consumption facilities is proposed. New smart services with reduced costs are installed and tested, confirming the advantages of the proposed model.

Distributed, but Global in Reach: Outline of a de-centralized paradigm for biodiversity data intelligence

We provide an overview and update on initiatives and approaches to add taxonomic data intelligence to distributed biodiversity knowledge networks. "Taxonomic intelligence" for biodiversity data is defined here as the ability to identify and renconcile source-contextualized taxonomic name-to-meaning relationships (Remsen 2016). We review the scientific opportunities, as well as information-technological and socio-economic pathways - both existing and envisioned - to embed de-centralized taxonomic data intelligence into the biodiversity data publication and knowledge intedgration processes. We predict that the success of this project will ultimately rest on our ability to up-value the roles and recognition of systematic expertise and experts in large, aggregated data environments. We will argue that these environments will need to adhere to criteria for responsible data science and interests of coherent communities of practice (Wenger 2000, Stoyanovich et al. 2017). This means allowing for fair, accountable, and transparent representation and propagation of evolving systematic knowledge and enduring or newly apparent conflict in systematic perspective (Sterner and Franz 2017, Franz and Sterner 2018, Sterner et al. 2019). We will demonstrate in principle and through concrete use cases, how to de-centralize systematic knowledge while maintaining alignments between congruent or concflicting taxonomic concept labels (Franz et al. 2016a, Franz et al. 2016b, Franz et al. 2019). The suggested approach uses custom-configured logic representation and reasoning methods, based on the Region Connection Calculus (RCC-5) alignment language. The approach offers syntactic consistency and semantic applicability or scalability across a wide range of biodiversity data products, ranging from occurrence records to phylogenomic trees. We will also illustrate how this kind of taxonomic data intelligence can be captured and propagated through existing or envisioned metadata conventions and standards (e.g., Senderov et al. 2018). Having established an intellectual opportunity, as well as a technical solution pathway, we turn to the issue of developing an implementation and adoption strategy. Which biodiversity data environments are currently the most taxonomically intelligent, and why? How is this level of taxonomic data intelligence created, maintained, and propagated outward? How are taxonomic data intelligence services motivated or incentivized, both at the level of individuals and organizations? Which "concerned entities" within the greater biodiversity data publication enterprise are best positioned to promote such services? Are the most valuable lessons for biodiversity data science "hidden" in successful social media applications? What are good, feasible, incremental steps towards improving taxonomic data intelligence for a diversity of data publishers?

Tutorial Gap Identification Towards Student Modeling Using Fuzzy Logic

Tutorial requirements of student modeling are traditionally identified by human tutors through crisp mental calculations, and the accuracy of this identification enhances with the experience of the tutor. These identifications become challenging when the mode of delivery is through e-learning. Automatic identification of tutorial requirements must be included in e-learning through ITS where some intelligent computational paradigms can help in automatic identification of the said requirements. This article proposes some fuzzy logic-based techniques toward the said purpose of automatic tutorial gap identification where analysis of the learner's responses to purposefully designed modular MCQ tests and verbal explanations are taken in to consideration as inputs for the purpose. Variation of the fuzzy logics was taken in to account to justify the usefulness of methods under consideration and to provide a realistic and adaptable approach. The experimental results show that the proposed methods are successful and efficient towards the said purpose.

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes.

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

Artificial Intelligence Paradigm for Customer Experience Management in Next-Generation Networks: Challenges and Perspectives

With the advancements of next-generation programmable networks, traditional rule-based decision- making may not be able to adapt effectively to changing network and customer requirements and provide an optimal customer experience. CEM components and implementation challenges with respect to operator, network, and business requirements must be understood to meet required demands. This article gives an overview of CEM components and their design challenges. We elaborate on data analytics and artificial intelligence driven CEM and their functional differences. This overview provides a path toward an autonomous CEM framework in next-generation networks and sets the groundwork for future enhancements.

Intelligence, anti-diplomacy and liquid security: control systems for a turbulent world

In turbulent global dynamics, surveillance systems have state-of-the-art technologies capable of easily breaching the protocols for information protection produced by each State in international relations, disrupting confidence levels achieved under different regimes in the definition of common threats and risks to security. Due to the asymmetries of international power in different spheres, the practices of intelligence surveillance and instrumentation have been approached from the hegemonic paradigms of International Relations and Intelligence, marginalizing the concurrence of other actors and reflections on their ethical positions. Therefore, from this article we reveal, through critical (poststructuralist) analysis, the explanatory limitations of realistic epistemic concepts: intelligence, anti-diplomacy and surveillance that simplify the understanding of complex current security scenarios. Finally, we validate the relevance of new analytics for the critical interpretation of these scenarios and their challenges.

A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data

One of the recipes for the big data and artificial intelligence paradigms is multi-dimensional data integration for improved decision making in petroleum reservoir characterization. Various machine learning (ML) techniques have been applied. However, there is still ample room for improvement. This paper carries out a rigorous parametric study to investigate the comparative performance of common and sophisticated ML techniques in the estimation of the permeability of a carbonate reservoir in the Middle East. The study integrates seismic attributes and wireline data for improved permeability prediction. The effects of tuning hyperparameters on the performance of the techniques are also studied. The techniques are evaluated on two versions of the seismic-log integrated data: globally-averaged and depth-matched. The results show that using the depth-matched dataset gives marginal improvement on the permeability prediction as reflected in the higher correlation coefficient and lower errors than the globally-averaged version. The outcome of this study will assist users of ML techniques to make informed choices on the appropriate ML techniques to use in petroleum reservoir characterization for more accurate predictions and better decision making especially when faced with limited and sparse data.

Spectrum Enhanced Fault Localization with Artificial Intelligence Paradigm for Software Diagnosis

Spectrum Enhanced Fault Localization with Artificial Intelligence Paradigm for Software Diagnosis

Chaotic Quantum Double Delta Swarm Algorithm Using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents’ solutions. We follow this up with a characterization of solution quality on a suite of 23 single–objective functions and carry out a comparative analysis with eight other related nature–inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen’s d and Hedge’s g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all iterations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.

Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

A novel modularity-based discrete state transition algorithm for community detection in networks

Complex network analysis is a hot topic in the data mining area which aims to reveal the hidden information behind a network. As an important tool in complex network analysis, community detection tries to perform a network clustering operation to find the community structure, which can be formulated as an optimization problem. In the past few decades, various of community detection algorithms have been designed to address this challenging problem. Although many algorithms are feasible to detect the network partitions, most of them only get suboptimal solutions or have poor stability. The state transition algorithm (STA) is a novel intelligent paradigm for global optimization, and it exhibits powerful global search ability in various complex optimization problems. Thus, in this paper, a novel modularity-based discrete state transition algorithm (MDSTA) is proposed to obtain more optimal and stable solutions. Moreover, based on the heuristic information of the network, vertex substitute transformation operator and community substitute transformation operator are proposed for global search. Then, each initialized individual evolves through these two substitute operations. Next, an elite population that contains individuals with high fitness values is selected from these evolved individuals. Finally, a two-way crossover operation among the elite population is conducted for local search. The framework of MDSTA is pretty simple and easy to implement. Several state-of-art community detection algorithms are used to compare with MDSTA both on artificial networks and real-world networks. The experimental results demonstrate that MDSTA is effective and stable for community detection in networks.

A holistic self-regulated learning model: A proposal and application in ubiquitous-learning

Technology enhanced learning (TEL) represents an expert and intelligent paradigm in which technological affordances are used to facilitate learners' acquisition of domain knowledge (DK), where Ubiquitous–Learning (u–learning) is a TEL instance that recreates situated and immersive settings. However in such settings, learners are stressed by diverse, heterogeneous, and simultaneous stimuli that challenge their cognitive skills, increase the cognitive load, trigger emotional reactions, and bias conduct. Thus this research proposes a smart Sequencing approach that enables TEL systems to lead students to regulate their learning process. The essence of the proposal is a holistic self–regulated learning (SRL) model that encourage students to develop higher–order thinking through the practice of metacognitive skills, motivational factors, and behavioral affairs to become aware of their own learning endeavors. The approach was applied as part of the sequencing module of a u–Learning system, where students follow its suggested cognitive strategies to assist them during the programming of control equipment. Results show that, although experimental subjects had to deal with higher cognitive load, they are expected to reach higher learning achievements than their control peers. The experience reveals how TEL systems are enabled to foster learners to handle their own apprenticeship processes. As a consequence, the smart sequencing functionality of TEL is cognitively enhanced to facilitate students the simultaneal acquisition DK and development of higher–order thinking.