Journal of Intelligent & Fuzzy Systems - Volume 43, issue 6

Authors: Huang, Dan | Lin, Hai | Li, Zhaowen

Article Type: Research Article

Abstract: Information system (IS) is a significant model in the field of artificial intelligence. Information structure is not only a research direction in the field of granular computing (GrC), but also an important method to study an IS. A multiset-valued information system (MVIS) refers to an IS where information values are multisets. A MVIS can be seen as a model that is the result of information fusion of multiple categorical ISs. This model helps deal with missing values in the dataset. This paper studies information structures in a MVIS on the view of GrC and consider their application for uncertainty measurement …(UM). First of all, some notions of multisets and probability distribution sets (PDSs) are proposed. Naturally, relationships between multisets and PDSs are researched. Then, the concept of a MVIS based on the notion of multisets is given, and the internal structure of a MVIS is revealed by an incomplete information system (IIS). Furthermore, tolerance relations in a MVIS are defined by using Hellinger distance, and tolerance classes are obtained to construct the information structures of a MVIS. Considering the association of information structures, relationships between information structures are raised from the two aspects of dependence and separation. Moreover, some properties between information structures are provided by using information distance and inclusion degree. Finally, four UMs as the applications of information structures are investigated, and comprehensive experiments on several datasets demonstrate the feasibility and superiority of the proposed measures. These results will be helpful for establishing a framework of GrC in a MVIS and studying UM. Show more

Keywords: GrC, RST, Information fusion, PDS, MVIS, Information structure, UM

DOI: 10.3233/JIFS-220652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7447-7469, 2022

Authors: Neelamegam, G. | Marikkannu, P.

Article Type: Research Article

Abstract: The Cloud-based storage is able to store more information in gigabyte size in all formats such as text, image or video and it can access at any time with their login credentials. In such a system, reducing the duplication of data and increasing security is an important factor for efficient storage. In this work, the file level de-duplication process is applied on the Magnetic Resonance Imaging (MRI) brain image by reducing the shares of the image to retrieve an original image from the cloud. To reduce the storage problem in this an optimization-based RSSS is used. The objective of this …investigation is to decrease the storage blow-up problem in Cloud storage and reduce the duplicate files in the Cloud storage of the health care centre. The proposed model comprises of two subsets: In the first set, the input image is divided into a number of shares using RSSS scheme. In the second set, the minimum share is determined by using the optimization process and it is encrypted and it is stored in the Cloud. Initially, the image is divided into number of shares for reconstructing using the ramp secret sharing scheme.Without these shares, the original image cannot be recovered. But storing all the shares result in high storage capacity. It is overcome with the help of Ant Lion optimization (ALO) to determine the minimum number of shares required for recovering the image. The ALO works to minimizing the Mean Square Error (MSE) of the image reconstruction to find the minimum shares. Then, the minimum shares are encrypted and converted into hash keys. Those hash keys are stored in the Cloud storage. The proposed ALO-RSSS is achieved its objective by reducing the shares to 2 as compared to the traditional method as well as the PSNR is 27% improved. Show more

Keywords: Cloud security, data de-duplication, ramp secret sharing scheme, ant lion optimization, shares, storage blow up

DOI: 10.3233/JIFS-212898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7471-7484, 2022

Authors: Gnanaprakasam, C.N. | Brindha, G. | Gnanasoundharam, J. | Ahila Devi, E.

Article Type: Research Article

Abstract: In this paper proposes an efficient hybrid approach for resolve the issues based on unit commitment model integrated with electric vehicles considering the responsive load. The proposed hybrid approach is the combined performance of both the Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO) and later it is known as MFM-TFWO method. The major objective of proposed approach is reduction of operational costs, reduction of real power losses, and reduction of emissions and improves the voltage stability index. The proposed system is incorporated with wind turbine and photovoltaic, electrical and thermal energy storage systems. The MFM approach is …performed for the optimization of the best combination of thermal unit depend on uncertainty; cost minimization, constraints of the system. For capturing the uncertainty and ensuring the demand satisfaction is performed by the TFWO approach. The proposed approach evaluates the impact of the stochastic behavior of electric vehicles and responsive load of the demand side management. The proposed method considers the uncertainty of PV, wind, thermal, electrical demands, and electric vehicles. At last, the proposed model is actualized in MATLAB/Simulink platform and the performance is compared with other techniques. The simulation results depicted that electric vehicles and responsive loads on energy management is decreasing the operation cost and emissions. Show more

Keywords: Operational costs, active power losses, emissions, voltage stability index, combined cooling heating and power, electric vehicle, responsive loads, energy storage

DOI: 10.3233/JIFS-220810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7485-7510, 2022

Authors: Bai, Yuhang | Wang, Chunbo | Zhang, Lizhong

Article Type: Research Article

Abstract: With the continuous opening up of China’s dairy market to foreign countries, dairy products import volume continues to grow rapidly. The structural vector autoregressive model (SVAR) was used in this article to analyze the impact of dairy product imports on China’s raw milk production from 1996 to 2017. It is found that, dairy product import volume has a positive impact on China’s raw milk production, and negative impact on the liquid dairy product; and mainly negative impacts on the cost control variables in the short term. The price of corn has a stronger impact on the raw milk production compared …with that of the soybean meal prices and crude oil price; the impact of Domestic raw milk demand on raw milk production fluctuates frequently in the short term, and has a positive impact on the diary export. Based on this, this article believes that adjusting the milk industry policy, optimizing the dairy products import structure and the dairy cows’ source structure, and advocating scientific feeding can effectively alleviate the impact caused by dairy products import. Show more

Keywords: Dairy import, raw milk production, shock effect, Structure vector autoregressive model (SVAR)

DOI: 10.3233/JIFS-221220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7511-7524, 2022

Authors: Anitha, R. | Bapu, B.R. Tapas

Article Type: Research Article

Abstract: In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is …proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods. Show more

Keywords: Wireless sensor network, secured routing protocol, malicious node attacks, Deep Dropout extreme machine learning, Improved Alpha-Guided Grey Wolf, Crypto Hash Signature token, blockchain technology

DOI: 10.3233/JIFS-212455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7525-7543, 2022

Authors: Fang, Min | Liu, Lu | Ye, Yuxin | Zhu, Beibei | Han, Jiayu | Peng, Tao

Article Type: Research Article

Abstract:  Knowledge graphs have been introduced into recommender systems due to the rich connectivity information. Many knowledge-aware recommendation methods use graph neural networks (GNNs) to capture the high-order structural and semantic information of knowledge graphs. However, previous GNN-based methods have the following limitations: (1) they fail to make full use of the neighborhood information of entities and (2) they ignore the importance of user interaction sequences on reflecting user preferences. As such, these models are insufficient for generating accurate representations of users and items. In this study, we propose a K nowledge-aware H ierarchical A ttention N etwork (KHAN) to provide …better recommendation. Specifically, the proposed model mainly consists of an item encoder and a user encoder. The item encoder is equipped with a hierarchical attention network, which is used to generate entity (item) representations by carefully aggregating neighborhood information of entities. The user encoder is also designed to learn more informative user representations from user interaction sequences using multi-head self-attention. The learned user representations are then combined with user representations introduced in the item encoder through a gating mechanism to generate the final user representations. Extensive experiments on two real-world datasets about movie and restaurant recommendation demonstrate the effectiveness of our model. Show more

Keywords: Recommender system, knowledge graph, graph neural network, hierarchical attention network

DOI: 10.3233/JIFS-212918

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7545-7557, 2022

Authors: Cai, Jinya | Zhang, Haiping | Yu, Xinping

Article Type: Research Article

Abstract: The modified bee colony algorithm is one of the excellent methods that has been proposed in recent years for data clustering. This MBCO algorithm randomly values the primary centers of the cluster by selecting a number of data from the data set, which makes the algorithm sensitive to the presence of noise and outgoing data in the data set and reduces its performance. Therefore, to solve this problem, the proposed method used three approaches to quantify the initial centers of the clusters. In the proposed method, first the initial centers of the clusters are generated by chaos methods, KMeans++algorithm and …KHM algorithm to determine the optimal position for the centers. Then the MBCO algorithm starts working with these centers. The performance of the proposed method compared to a number of other clustering methods was evaluated on 7 UCI datasets based on 6 clustering evaluation criteria. For example, in the iris data set, the proposed method with chaos approaches, KHM and KMeans++with accuracy of 0.8725, 0.8737 and 0.8725, respectively, and the MBCO method with accuracy of 0.8678, and in terms of CH criteria, the proposed method with chaotic approaches, KHM and KMeans++reached values of 0.3901, 0.54848, 0.5147 and MBCO method of 0.3620, respectively. Better achieved. In general, the results of the experiments according to the 6 evaluation criteria showed better performance of the proposed method compared to other methods in most data sets according to the 6 evaluation criteria. Show more

Keywords: Modified bee colony optimization, KMeans++algorithm, KHM algorithm, clustering

DOI: 10.3233/JIFS-220739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7559-7575, 2022

Authors: Balasubramanian, C. | Lal Raja Singh, R.

Article Type: Research Article

Abstract: This paper proposes an efficient energy management approach for managing the demand response and energy forecasting in a smart grid using Internet of Things (IoT). The proposed energy management approach is the hybrid technique that is the joint execution of adaptive neuro fuzzy inference system (ANFIS) and balancing composite motion optimization (BCMO), thus it is called ANFIS-BCMO technique. An energy management approach is developed using price-based demand response (DR) program for IoT-enabled residential buildings. Then, we devised a approach depends on ANFIS-BCMO technique to systematically manage the energy use of smart devices in IoT-enabled residential buildings by programming to relieve …peak-to-average ratio (PAR), diminish electricity cost, and increase user comfort (UC). This maximizes effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings on smart cities. The ANFIS-BCMO technique automatically responds to price-based DR programs to combat the main problem of DR programs that is the limitation of the consumer’s knowledge to respond when receiving DR signals. For consumers, the proposed ANFIS-BCMO based strategy programs appliances to exploit benefit based on reduced electricity bill. By then, the proposed method increases the stability of the electrical system by smoothing the demand curve. At last, the proposed model is executed on MATLAB/Simulink platform and the proposed method is compared with existing systems. Show more

Keywords: Energy management, demand response, energy forecast, smart grid, internet of things

DOI: 10.3233/JIFS-221040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7577-7593, 2022

Authors: Yen, Chih-Ping

Article Type: Research Article

Abstract: We examine correlation coefficients for single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) to point out their questionable results for the ideal alternative. Then, we propose three similarity measure methods to solve multi-criteria decision-making (MCDM) problems. Three applications, namely, ranking of alternatives, dysfunctional comments of turbine engine generators, and disease diagnoses for patients, illustrate the stability and effectivity of our new similarity. Our findings will help researchers deal with similarity measures in the future.

Keywords: Multiple criteria decision-making, correlation coefficient, single-valued neutrosophic hesitant fuzzy sets

DOI: 10.3233/JIFS-221142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7595-7604, 2022

Authors: Sindhusaranya, B. | Geetha, M.R. | Rajesh, T. | Kavitha, M.R.

Article Type: Research Article

Abstract: Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina …image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies. Show more

Keywords: Hybrid algorithm, blood vessel segmentation, first-order derivation of Gaussian, matched filter, multi-scale line detector

DOI: 10.3233/JIFS-221137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7605-7615, 2022