Journal of Intelligent & Fuzzy Systems - Volume 35, issue 2

Authors: Luo, Peicong | Wang, Xiaoying

Article Type: Research Article

Abstract: With the construction and application of large-scale datacenters, the issue of resource allocation in cloud computing becomes a serious concern. Although the current static allocation method can make applications get corresponding resources, there still exist some shortcomings such as resource surpluses or shortages. This kind of problem is more crucial in real-time requirements of mobile cloud computing service. Therefore, it is necessary to establish a forecasting model to predict the future resource demands, and then perform on-demand distribution, which can effectively reduce the unnecessary daily network management fees and address the issues mentioned above. This paper focuses on CPU resource …forecasting, establishing three forecasting models including Markov chain, weighted Markov chain and stacking weighted Markov chain. By comparing and analyzing the experiment results, the most reasonable forecasting model is found and explained. Show more

Keywords: Mobile cloud computing, resource allocation, forecasting model, markov chain, CPU

DOI: 10.3233/JIFS-169675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1315-1324, 2018

Authors: Yan, Maoling | Liu, Pingzeng | Zhao, Rui | Liu, Lining | Chen, Weijie | Yu, Xueru | Zhang, Jianyong

Article Type: Research Article

Abstract: With the accelerated process of agricultural modernization, the accurate acquisition of agricultural environmental information has become a major trend. A field microclimate monitoring system based on wireless sensor network is constructed based on the latest concept model of the Internet of things and fuzzy control theory; it mainly composed of data acquisition system, data storage system and visualization platform for big data analysis. The data acquisition system is highly integrated with data acquisition and data transmission to obtain real-time data of farmland environment in different terrain areas, including meteorology, hydrology, soil, growth etc. Wireless transmission will transmit real-time data through …the GPRS network to the big data analysis platform. The big data analysis platform presents the site data information and analyzes the rule of historical data through visualization technology, realizes the meteorological disaster early warning and forecast, and provides effective decision-making service information based on the agricultural fuzzy theory. Finally, we analyze all kinds of hardware interference problems and software defects encountered in the debugging process, and propose new solutions through the experimental data obtained from actual production applications. It has been proved that the field microclimate monitoring system runs steadily and meets the demand of agricultural monitoring. Show more

Keywords: Wireless sensor networks, field microclimate, monitoring system

DOI: 10.3233/JIFS-169676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1325-1337, 2018

Authors: Wang, Hua | Wen, Yingyou | Zhao, Dazhe

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) are vulnerable to various localization attacks where attackers intended to provide improper beacons or manipulate the location determination. Attack classification for localization in WSNs is not only the condition, prerequisite and premise of threat analysis, but, more significantly, a vital part of the security anomaly detection. In this paper, a localization attack recognition method using a deep learning architecture was proposed. To enhance the classification performance, a good feature representation was established through combining location features with topological indexes based on the complex network theory. The ability of Stacked Denoising Autoencoder (SDA) to learn the underlying …features from input data was exploited. Back-propagation algorithm was performed to update weights through a stochastic gradient descent method. The proposed approach could efficiently distinguish the Sybil attacks, Replay attacks, Interference attacks, Collusion attacks and normal beacons. Extensive experiments demonstrated that the proposed algorithm can achieve an average classification accuracy of 94.39% and was more robust and efficient even in the existent of huge baneful beacons. Show more

Keywords: Security, wireless sensor networks, attack classification, deep learning

DOI: 10.3233/JIFS-169677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1339-1351, 2018

Authors: Xu, Zhi | Ding, Hongwei | Liu, Qianlin | Yang, Zhijun | Bao, Liyong | Zhan, Gang

Article Type: Research Article

Abstract: In order to solve the contradiction between wireless network application requirements and increasingly scarce spectrum resources, cognitive wireless network technology emerged. Based on the characteristics of wireless network nodes and combining multiple hybrid control strategies, this paper proposes a multi-priority dual-clock probability detection CSMA(MPDCPD-CSMA) protocol with a monitoring mechanism. The field-programmable gate array (FPGA) hardware circuit is used as an experimental research platform for the first time. Cognitive wireless network MAC protocol design and implementation. The design took full advantage of the flexibility of the FPGA, using a hardware description language Verilog HDL and schematic input combined with the QuartusII9.0 …circuit design. By comparing the statistical values of the circuit system with the theoretical values, it is verified that the design has the characteristics of good real-time performance, high reliability, and strong portability. It can effectively reduce system node energy consumption, improve system throughput, and can be applied to wireless networks. Show more

Keywords: cognitive wireless network, hybrid control strategy, field programmable gate array, schematic input, throughput

DOI: 10.3233/JIFS-169678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1353-1361, 2018

Authors: Wu, Guangsheng | Liu, Juan | Min, Wenwen

Article Type: Research Article

Abstract: Uncovering the potential treatment associations of the drug-disease pairs is a research focus of drug repositioning. However, it is time-consuming and costly to verify the potential treatment relation between a drug and a disease by “wet” experiment methods. Fortunately, along with the accumulation of large amount of data and the development of machine learning methods, lots of computational methods to predict the drug-disease treatment associations have been proposed. In order to build the prediction model based on machine learning techniques, both plenty of positive and negative training samples are required. In the case of biological experiments, however, we can only …verify whether a drug cures a disease, yet we are unable to answer whether a drug definitely cannot treat a disease. Correspondently, there are only positive and unlabeled samples in the data. Being lack of validated negative samples, most computational methods assume the unlabeled samples to be negative ones and randomly select some unlabeled samples and positive samples to train the prediction models. Obviously, the unlabeled samples are not necessarily negative, and some of them may be positive just remaining uncovered via experiments. In this paper, we propose a method called PUDrDi which directly make use of the positive and unlabeled samples to train a Biased-SVM classifier. Moreover, we combine the drug and disease features together to represent a drug-disease pair, in which we use chemical substructures and symptoms as the features to represent drugs and diseases respectively. The experiment results demonstrate that PUDrDi outperforms some other methods. The case study further shows the practicality of PUDrDi. Show more

Keywords: Drug repositioning, drug-disease treatment associations, unlabeled samples, machine learning, positive-unlabeled learning

DOI: 10.3233/JIFS-169679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1363-1373, 2018

Authors: Vinod Kumar, K. | Ranvijay,

Article Type: Research Article

Abstract: As the performance of modern multi-core processors is significantly increases, the total energy consumption in the systems also increases drastically. Dynamic Voltage and Frequency Scaling (DVFS) is considered as one of the efficient schemes for achieving the aim of energy saving. In this paper, we consider scheduling a task set, whose release times, deadlines and execution requirements are given, on DVFS-enabled multi-core processor system. Our main aim is to meet the execution requirements of all the tasks, and to minimizethe overall energy consumption on the processor with effective utilization of resources. Instead of seeking optimal solutions with high complexity, we …aim to design algorithms suitable for real-time systems, with good performances. We come up with a simple algorithm for task scheduling and energy awareness by considering deadline constraint. We further consider the distribution of deadline and task scheduling, which guarantee that all tasks meet their execution requirements, and tries to minimize the overall energy consumption. Case based simulations for various applications and task characteristics and evaluations using a practical processor’s power configuration indicate that our proposed algorithm has a less energy consumption performance and good resource utilization in terms of saving processor energy, though it has low complexity. Besides, the proposed algorithm is easy to be implemented in practical systems. Show more

Keywords: Realtime, DVFS, energy efficiency, DAG, multicore, resoure utilization

DOI: 10.3233/JIFS-169680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1375-1385, 2018

Authors: Bhuvanesh, A. | Jaya Christa, S.T. | Kannan, S. | Karuppasamy Pandiyan, M. | Gangatharan, K.

Article Type: Research Article

Abstract: Generation Expansion Planning (GEP) aims to define the least cost capacity expansion plan to meet forecasted demand inward a pre-defined reliability criterion and emission constraint over a planning horizon. This paper presents the application of Differential Evolution (DE), Opposition-based Differential Evolution (ODE) and Self-adaptive Differential Evolution (SaDE) algorithms to GEP problem, where the power generating system of an Indian state Tamil Nadu is taken as study region. GEP problem has been solved for short-term (6-years) and long-term (12-years) planning horizon by considering least-cost, reliable supply and lowest emission to the environment using DE, ODE and SaDE also validated by Dynamic …Programming (DP). GEP problem is solved for seven diverse cases such as, Case 1: Base case, Case 2: GEP with Energy Conservation (EC), Case 3: GEP with high penetration of Renewable Energy Sources (RES), Case 4: GEP with penalty costs on emissions from high emission plants (HEP), Case 5: GEP with energy storage technologies (EST), Case 6: Combination of Cases 2, 3&4 and Case 7: Combination of Cases 2, 3, 4&5. The results simultaneously provide the type and capacity of each power plant need to be expanded in each year of the planning horizon at least cost. Show more

Keywords: DE, emission cost, GEP, ODE, RES, SaDE and Tamil Nadu electricity sector

DOI: 10.3233/JIFS-169681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1387-1398, 2018

Authors: Jha, Sunil Kr. | Ahmad, Zulfiqar | Crowley, David E.

Article Type: Research Article

Abstract: Microbial activities are the indicators of soil strength. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, phosphate solubilization (PS), bacterial population (BP), and 1-aminocyclopropane-1-carboxylate ACC-deaminase activity. More specifically, fuzzy c-means clustering (FCM)-FIS, Wang and Mendel’s (WM) fuzzy inference systems (FIS), adaptive neuro-fuzzy inference system (ANFIS), and subtractive clustering (SC) and have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media. Three experimental parameters, including temperature, pH, and incubation period have been …used as inputs of FCM-FIS, SC-FIS, ANFIS, and WM-FIS methods. The SC-FIS method has the best estimation accuracy for the PS (R2 of 0.99) and BP (R2 of 0.94) than the rest three FIS methods. Show more

Keywords: FCM-FIS, WM-FIS, ANFIS, SC-FIS, phosphate solubilizing bacteria, bacterial population, ACC-deaminase activity

DOI: 10.3233/JIFS-169682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1399-1406, 2018

Authors: Zhu, Lei | Liang, Xuefei | Wang, Lei | Wu, Xingrong

Article Type: Research Article

Abstract: Pythagorean fuzzy sets, which is based on intuitionistic fuzzy sets (IFSs), is an important tool to solve problems and has attracted a large number of researchers in different fields. As we know, studies have focused on interval-valued Pythagorean fuzzy set and aggregated operators. However, few studies focus on point operators. This paper introduces and discusses what is the pythagorean fuzzy point operators, study their properties and relationships, which is seen as the extensions of intuitionistic fuzzy sets. The uncertainty regarding to Pythagorean fuzzy set could be decreased if we use the pythagorean fuzzy point operators. In the end, pythagorean fuzzy …multi-attributes decision making based on analytic hierarchy procedure is put forward to cope with the complicated MADM (multi-attributes decision making) issues which can be very useful when we face the multi-level analysis. Show more

Keywords: Point operators, multi-criteria decision making, pythagorean fuzzy set, analytic hierarchy process

DOI: 10.3233/JIFS-169683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1407-1418, 2018

Authors: Seiti, H. | Hafezalkotob, A. | Najafi, S.E. | Khalaj, M.

Article Type: Research Article

Abstract: Failure Modes and Effects Analysis (FMEA) is a common technique used in several manufacturing and service industries for eliminating failures and potential problems using the evaluation of failure modes of a new or an existing product, process, or system. The risk analysis is carried out by calculating the Risk Priority Number (RPN), which is a product of three factors, Occurrence (O), Severity (S), and Detection (D). In the literature, modeling uncertainties are used to improve the FMEA process and overcome the inefficiencies of traditional RPN. One of the common uncertainties in FMEA is the epistemic uncertainty that is essentially modeled …using the Dempster-Shafer theory (DST). In this study, a novel risk-based fuzzy evidential approach is proposed by using interval-valued DST and fuzzy axiomatic design (FAD) to assess the risk of failure modes with fuzzy belief structures. The efficiency of the proposed model was investigated with the help of an example and the results are compared with riskless evaluations. Reviewing the results shows the information content of failure modes decrease relatively when risk is taken into account, in fact failure modes become relatively more critical than those in the case where no risk is considered. Show more

Keywords: Fuzzy belief structure, FMEA, risk of evaluations, fuzzy information axiom

DOI: 10.3233/JIFS-169684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1419-1430, 2018