Forthcoming articles

International Journal of Reasoning-based Intelligent Systems

International Journal of Reasoning-based Intelligent Systems (IJRIS)

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International Journal of Reasoning-based Intelligent Systems (15 papers in press)

Regular Issues

  • Generalized Linear Orthomorphisms   Order a copy of this article
    by Haiqing Han, Siru Zhu, Yanqing Dai, Qili Mao, Qin Li, Kang Shi 
    Abstract: In this scientific research paper, the authors have generalized the concept with regard to orthomorphic permutations(called orthomorphisms) over the Galois field. Meanwhile we have gain the enumeration formula of the total generalised linear orthomorphic permutations over the Galois field, which possesses an arbitrary prime number as the characteristic of the prime subfield. So, the local creating algorithm with regard to partial generalised linear orthomorphic permutations over the Galois general fieldis realized. Comparatively speaking, the innovativeness and originality enumeration formula with regard to linear orthomorphisms over a Galois field with characteristic 2 is a special case to contain in our novel fruits over the general field. What is more, the generalised linear orthomorphic permutations have been thoroughly discussed and generated far
    Keywords: P-permutation; Block Cipher; the Branch Number; Generalized Linear Orthomorphism.
    DOI: 10.1504/IJRIS.2021.10034133
  • Multi-Agent-Based Distributed Text Information Filtering Method   Order a copy of this article
    by Wuxue Jiang 
    Abstract: In order to improve the filtration efficiency and precision, and reduce the occupation of network resources in distributed text information filtering system, a kind of Multi-Agent-based text filtering method was designed. Directed by multi-Agent theory and technology, the system structure and working mechanism of distributed text information filtering are presented, which makes detailed design for scheduling responding agent and learning agent. The load balance was implemented by dynamic range adaptive load migration (DRALM). The experiment shows that this filtering method, boasting higher filtering performance, not only has higher filter precision, but processes tasks in many machines effectively balancing computing load.
    Keywords: Multi-Agent System; Text Information Filtering; Distributed System; Open Computing Model; Dynamic Range Adaptive Strategy; Daemon.

  • Modified Jaya algorithm with chaos   Order a copy of this article
    by Mingjing Pei, Shuhao Yu, Maosheng Fu, Xukun Zuo 
    Abstract: Jaya algorithm is a recently developed optimization algorithm, which is a new optimization algorithm designed to solve optimization related problems, it has two random parameters in equations. In the study of this paper, we will introduce chaos into Jaya so as to increase non-repeatability and ergodicity for global optimization. Here, four different chaotic maps are utilized to control random parameters in Jaya. The results show that some chaotic maps can outperform the random parameters in the high dimensional function and the result of the two-dimensional function is almost the same.
    Keywords: Jaya algorithm; Chaos; Global optimization.

  • Hedging Strategy for Commodity Futures Based on SVM-KNN   Order a copy of this article
    by Mei Sun, Rongpu Chen, Yulian Wen, Peiyao Nie 
    Abstract: In view of the problem of excessive exposure in the field of quantitative investment in commodity futures and policy failure in the low volatility market environment, a new quantitative investment strategy using SVM-KNN combined classifier to hedge multi-factor futures is proposed and applied to the management of quantitative fund. The quantitative investment strategy can not only reduce the overall systemic risk of the investment portfolio, but also adapt to the long-term environment of the commodity futures market. The retest data and the results of real trading show that the SVM-KNN based hedging strategy of commodity futures is significantly higher than the traditional CTA trend tracking strategy in the annual rate of return and the SHARP ratio, and the retracting of the cross period is greatly reduced.
    Keywords: Quantitative Investment; Commodity Futures; Multifactor Hedging; Support Vector rn Machine; K-nearest Neighbors.

  • Wireless sensor networks for smart agriculture   Order a copy of this article
    by Volodymyr Romanov, Igor Galelyuka, Oleksandr Voronenko 
    Abstract: Digital agriculture requires development and implementation of new information and communication technologies, including wireless ones for increasing efficiency. The authors developed wireless sensor networks, and wireless technology for express-diagnostics of plant state in agriculture, created the appropriate hardware, software and methodological support. The developed networks now are implemented and under field tests both in open agricultural lands and in smart greenhouses. In this paper the design and application of developed wireless sensor network and some results of calculations and modelling are given
    Keywords: wireless sensor network; smart agriculture; Internet of Things.

  • Strategies based on IoT for supporting the decision making in Agriculture: A Systematic Literature Mapping   Order a copy of this article
    by Mario Diván, María Laura Sánchez-Reynoso 
    Abstract: Smart Agriculture has taken a relevant role due to the importance of food production in contrast to the global population growth. In this sense, alternatives for improving the efficiency and efficacy related to the production systems are welcomed. The Internet-of-Thing (IoT) has allowed increasing the coverage in the field through the use of open-hardware technology at affordable prices. In this way, real-time data collecting has become a feasible alternative for smart agriculture, incorporating different kinds of strategies oriented to real-time decision making with actionable recommendations. As the main contribution, this work implements a systematic mapping study (SMS) of the literature to identify different strategies and approaching oriented to support the data-driven decision making in agriculture using IoT devices. SMS is applied to the Scopus database, with a special focus on those data coming from IoT devices, which eventually could be complemented through big data repositories. 74 documents are retained based on the defined filters in relation to the subject. Also, a scoring model is defined and implemented with the aim of establishing an order for documents, which get a balance between the content precision, the number of citations and the publishing year. The Top-Ten documents are contrasted, while the rest of the documents are synthesized, highlighting their contributions and opportunities to improve in an ordered way based on the scoring model. The documented main subjects related to real-time decision making based on IoT devices are represented by precision agriculture, cultivating monitoring, and irrigation management systems.
    Keywords: Strategies; Internet-of-Things; Decision-Making; Agriculture; Systematic Literature Mapping.

  • The Problems of Consistent Intelligent Real-Time Control of Complex Systems   Order a copy of this article
    by Andrey Tyugashev 
    Abstract: There are a lot of complex technical systems with a growing impact on our life. We can mean Railroad Transportation, Automated Manufactories, Automated Agricultural Systems, and Aerospace Missions, etc. Such systems consist of hundreds of sensors, actuators, aggregates, etc. To complete the specific tasks of the complex, we need to implement a required schedule of logically coordinated actions in real-time mode, even in case of faults of the equipment. The control means should be consistent in various senses defined and discussed in the article. The article further develops and generalizes Real-Time Control Algorithms Logic previously proposed by the author, for considering consistent control in case of limited available resources. The special software tools based on the presented approach are also being described.
    Keywords: Intelligent control; Control of Complex Technical Systems; Real-Time Control Algorithms; AI; Program verification; Formal Methods.

  • Enhanced Interconnected Microgrids for Water-Pumps Networks towards Zero Net Energy (ZNE) Farms   Order a copy of this article
    by Ahmed Abdelmaksoud, Hossam A. Gabbar 
    Abstract: This paper presents design of resilient interconnected microgrids to provide clean and cheap energy to water pumps infrastructure at farms during normal and emergency situations in terms of different renewable resources. Water networks are relied on every day millions of customers utilization and bring billions worth of economic goods to market. However, water network infrastructures are dependent on the electric grid, which is vulnerable to extreme weather, changing supply and demand patterns, and cyber-terrorism. The proposed microgrid includes hybrid energy storage including battery systems, which provides flexible and adaptive energy supply in view of water pumps loads. Proposed microgrid will allow sustainable operation capability with resilient performance on both of economical and technical levels. In addition, the proposed solution, based on Artificial Bee Colony Algorithm (ABC) optimization for Zero Net Energy (ZNE), ensures sustained and high performance energy supply in different operational scenarios. Integrated architecture is proposed to ensure local and supervisory operation and management of the interconnected microgrids, with best use of energy storage systems and energy distributed systems. The proposed designs are modelled and simulated in Simulink, for a proposed system of interconnected microgrids for water-pumps infrastructures, which demonstrates a reduced dependence on the electric grid. The results are both extremely impressive and promising towards a more resilient and stable energy future both for water network and its critical infrastructures.
    Keywords: Microgrids; water pumps networks; resilient energy systems; energy storage; renewable resources.

  • Frost forecast - a practice of machine learning from data   Order a copy of this article
    by Liya Ding, Yosuke Tamura, Kosuke Noborio, Kazuki Shibuya 
    Abstract: Abstract: Among the efforts in frost forecast using machine learning techniques, a well adopted method is to first apply time series forecast for the lowest temperature at future time points, such as the next a few days, and then apply predictive model to predict the event of frost at these time points using corresponding temperature forecasted. According to the domain understanding, there exists some cause-effect between environment factors, including temperature and others, and the occurrence of frost in a few hours period. A new modelling concept has been proposed to capture such cause-effect. Preliminary experiments showed encouraging results with a sample of minute-level sensor data collected in Ikuta campus of Meiji University. In this article, as a continuation of the previous work, we shall further discuss methods of modelling, including causal models and associative models, and propose a framework of hybrid system in supporting frost forecast of short-term (e.g. a few hours) as well as that of relatively longer periods (e.g. a few days). More experiments are provided, and the issues of performance evaluation are discussed.
    Keywords: Frost forecast; Machine learning; Time series forecasting; Prediction; Cause-effect.
    DOI: 10.1504/IJRIS.2021.10038162
  • Low - Cost Energy - Efficient Air Quality Monitoring System Using Sensor Network   Order a copy of this article
    by Mare Srbinovska, Aleksandra Krkoleva Mateska, Vesna Andova, Maja Celeska Krsteska, Tomislav Kartalov 
    Abstract: The air pollution has a significant impact on humans health and global environment. In urban areas the air quality significantly decreases over the past few years. One of the methods for air pollution reduction is by installing a green walls, green roofs or by implementing green buildings in urban areas as plants have capabilities to absorb the particulate matter through their leaves. Urban green spaces especially from trees to green roofs and large natural spaces generally provide significant health benefits for residents and the community. rnThe main goals of this paper is to present system for air quality monitoring using sensor network technology that can be easily deployed in polluted areas; and to examine the influence of the experimental green wall setup to particulate matter concentrations in the air in an urban area in Skopje. Furthermore, the paper presents the preliminary results of the ongoing experiment developed to evaluate the impact of green walls in reduction of air polluting particles. The air quality monitoring system can be easily replicated on other locations in the urban areas of Skopje.rn
    Keywords: air quality monitoring system; green walls; sensor network; particulate matter.

  • A comparative performance analysis of different machine learning techniques for SNR prediction in microcell and picocell wireless environment   Order a copy of this article
    by Nikola Sekulovic, Milos Stojanovic, Aleksandra Panajotovic, Milos Bandjur 
    Abstract: Knowledge of propagation channel conditions enables adaptive data transmission which improves the quality and efficiency of communication system. Wireless channels are characterized by highly dynamic time-varying nature. This means that information regarding propagation channel condition obtained by channel estimation can become outdated because of delay caused by processing and feedback phases. In fast fading environments, prediction of channel based on channel states in previous moments can ensure timely information. In this paper, a comparative performance analysis of an echo state network (ESN), an extreme learning machine (ELM) and least squares support vector machines (LS-SVM) for prediction of wireless channel conditions for single-input single-output (SISO) systems in microcellular and picocellular environments is carried out. Normalized mean squared error (NMSE) and time consumption are used as performance indicators. The experimental results on measured values for signal-to-noise ratio (SNR) show that all models have better and comparable prediction accuracy in microcell environment, while prediction framework based on the ESN outperforms the others in picocell environment.
    Keywords: Channel prediction; Echo state network; Extreme learning machines; Least squares support vector machines; Microcellular environment; Picocellular environment.

  • Use of infrared radiometry in temperature measurement of plant leaf   Order a copy of this article
    by Hristo Hristov, Kalin Dimitrov, Stanyo Kolev 
    Abstract: Through our present work we will show the importance of infrared radiometry in conducting various plant studies. We will look at the factors that affect temperature measurements and their significance. We will monitor how changing the distance between radiometer and object of study affects the heat flow entering the radiometer aperture from an object, and then how changing the distance between them affects the total remaining heat flux entering the radiometer aperture. We will monitor these processes in different temperatures and with different surface areas of the object of study. We will monitor the change of the flux entering from the object of study and the change in the total remaining heat flux entering the radiometer aperture, when the scene is made of insulation material. We will draw conclusions about the significance of the distance between the thermal camera and the object of study.
    Keywords: infrared radiometry; infrared thermography; agriculture; solid angle; thermal radiation; emission coefficient; surface temperature.

  • Development of IoT based Smart Agriculture Monitoring System for Red Radish Plants Production   Order a copy of this article
    by Ari Aharari, Chunsheng Yang 
    Abstract: The world population is increasing at a fast rate, and as results need for food is also growing briskly. The traditional method of agriculture is not sufficient enough to cover the needs of the market. On the other hand, the aging of agricultural workers has progressed rapidly, and the successor problem is becoming more serious. Under such circumstances are coming out also new farmers that will help the beginner to agriculture. However, the establishment of farming technology has become a significant management challenge for new farmers. In this paper, we focused on automation in agriculture by applying IoT technologies. The proposed system is utilizing?to monitor the environmental information during the experiment of producing the red radish. The sensor data is analyzed to find the relation between ecological parameters and the growth results. The result of the proposed system was satisfactory as the first step in much deep measure development.
    Keywords: Smart agriculture; Internet of Things; Red radish.

  • Many-Valued Tableau Calculi for Decision Logic based on Approximation Regions in VPRS   Order a copy of this article
    by Yotaro Nakayama, Seiki Akama, Tetsuya Murai 
    Abstract: Rough sets theory is studied to manage uncertain and inconsistent information. While the Pawlak's decision logic of rough sets is based on classical two-valued logic, this causes inconvenience for the various reasoning. In this paper, we propose many-valued logics, especially a three-valued logic, as the deduction system for the decision logic of rough sets. To enhance the decision logic from classical bivalent logic to three-valued logic, we adopt Variable Precision Rough Set (VPRS). As a deductive basis for three-valued decision logic, we define a consequence relation based on three-valued semantics to constructing a deduction system withrnthe semantic tableau. We show to deal with two types of the third value of three-valued semantics one is unknown, and the other is inconsistent using Belnap's four-valued interpretation.
    Keywords: many-valued logic; tableau calculi; decision logic; variable precision rough set; knowledge representation.

  • A Bayesian Network Approach to Handle Uncertainty in Web Ontology Language (OWL)   Order a copy of this article
    by Sonika Malik, Sarika Jain 
    Abstract: In information Management Systems ontology play a vital role. An ontological application should include a mechanism for handling uncertainty. Ontological innovations are to become the web's future in the coming period, but there are still some features such as exceptions, vulnerability, default values missing. Ontological languages such as OWL and RDF are by necessity distinct in nature, so ambiguous details cannot be treated. In this research, article uncertainty is handled in the ontology by the Bayesian network. A probabilistic model of uncertainty available in the knowledge base is the Bayesian network. The Bayesian Network is a conceptual model that is ideal for the representation and analysis of ambiguity and information found in data. The probability of uncertainty can be used for many real-life scenarios in the knowledge base. We also introduced defaults and exceptions along with uncertainties to enhance performance and improve the OWL features. The source code is then translated to a jar-file with maven and it can be used in Prot
    Keywords: Ontology; Uncertainty; Bayesian Network; Unit of Knowledge.