Journal of Intelligent & Fuzzy Systems - Volume 36, issue 5

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3933-3933, 2019

Authors: Vijayakumar, V. | Subramaniyaswamy, V. | Abawajy, Jemal | Yang, Longzhi

Article Type: Editorial

DOI: 10.3233/JIFS-179108

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3935-3943, 2019

Authors: Li, Kairu | Fang, Yinfeng | Zhou, Yu | Ju, Zhaojie | Liu, Honghai

Article Type: Research Article

Abstract: Functionality and cosmetics are two concerns for future hand prosthesis development and they both can be improved by a combination with artificial soft materials which can mimic human skin. To bridge the gap between the human and artificial side, it is essential to have a comprehensive understanding of the human skin’s biomechanics, especially the fingertip’s haptics-related mechanism. Available studies characterise the mechanical behaviour of human fingertip only by deterministic models based on either statistical data analysis or fingertip structure/viscoelasticity analysis. To take the force uncertainty into consideration, this paper proposes a novel probability-based haptics model, which includes two parts: a …force prediction model to obtain the most possible contact force according to the indentation depth, and a probabilistic model based on Gaussian distribution to describe the force uncertainty. Experiments were conducted by pressing subjects’ index fingertips against a cone-shape probe with the measurement of the contact force and the indentation depth under a wide range of 0∼5 mm. Four types of non-linear regression models and the Gaussian distribution model are applied for model training and validation. Experiment results reveal that the contact force varying with the indentation depth presents the characteristics of non-linearity, dispersion, and individual difference. Model testing results confirm the effectiveness of the haptics model on force prediction and force uncertainty description. An example of its application on a virtual hand of a rehabilitation system is demonstrated. Show more

Keywords: Haptics model, fingertip biomechanics, skin deformation, force, Gaussian distribution

DOI: 10.3233/JIFS-169956

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3945-3955, 2019

Authors: Majumdar, A. | Laskar, N.M. | Biswas, A. | Sood, S.K. | Baishnab, K.L.

Article Type: Research Article

Abstract: With the advent of IoT, cloud/fog based healthcare systems have become a growing trend in modern healthcare systems. These systems comprise of smart sensors, which on integration with medical devices, generate heterogeneous medical big data that can be used in diagnosis of various diseases. However, there is a continuous flow of large quantity of data in such a systems, due to which it may face many difficulties. Among various pre-requisites for proper functioning of these systems, lifetime is a vital factor. Keeping in view these aspects, the use of new hybrid whale-PSO algorithm (HWPSO) in clustering has been proposed for …prolonging the network lifetime by preserving the power of network edge devices. In addition to this, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been taken into account in the selection of cluster leader. The cluster leader is responsible for managing intra-cluster and inter-cluster data communication. Show more

Keywords: e-Healthcare, fog computing, particle swarm optimization, whale optimization

DOI: 10.3233/JIFS-169957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3957-3969, 2019

Authors: Zeng, Daojian | Dai, Yuan | Li, Feng | Wang, Jin | Sangaiah, Arun Kumar

Article Type: Research Article

Abstract: Recently, sentiment analysis has become a focus domain in artificial intelligence owing to the massive text reviews of modern networks. The fast increase of the domain has led to the spring up of assorted sub-areas, researchers are also focusing on subareas at various levels. This paper focuses on the key subtask in sentiment analysis: aspect-based sentiment analysis. Unlike feature-based traditional approaches and long short-term memory network based models, our work combines the strengths of linguistic resources and gating mechanism to propose an effective convolutional neural network based model for aspect-based sentiment analysis. First, the proposed regularizers from the real world …linguistic resources can be of benefit to identify the aspect sentiment polarity. Second, under the guidance of the given aspect, the gating mechanism can better control the sentiment features. Last, the basic structure of model is convolutional neural network, which can perform parallel operations well in the training process. Experimental results on SemEval 2014 Restaurant Datasets demonstrate our approach can achieve excellent results on aspect-based sentiment analysis. Show more

Keywords: Aspect-based sentiment analysis, linguistic resources, convolutional neural networks, gating mechanism

DOI: 10.3233/JIFS-169958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3971-3980, 2019

Authors: Bhuvaneswari, A. | Valliyammai, C.

Article Type: Research Article

Abstract:  The demand for Cyber Social Networks has increasingly become the main source of information propagation due to the rapid growth of micro-blogging activity between socially connected people. The process of detecting disaster events, in huge volumes, on fast-streaming platform is quite challenging. In this paper, an information entropy based event detection framework is proposed to identify the event and its location by clustering relatively high-density ratio of tweets using Twitter data. The Shannon entropy of target users, location, time intervals and hashtags are estimated to quantify the dissemination of events as “how-far about” in real- world using entropy maximization inference …model. The geo-tagged (spatial) tweets are extracted for a specified time period (temporal) to identify the location of an event as “where-when about”; and visualizes the event in geo-maps. The evaluation parameters of Entropy, Cluster Score, Event Detection Hit and False Panic Rate during four major disaster events are identified to illustrate the effectiveness of the proposed framework. The retweeting activity of the Twitter user is classified as human signatures and bots. The experimental outcome determines the scope and significant dissemination direction of finding events from a new perspective which demonstrates 96% of improved event detection accuracy. Show more

Keywords: Cyber-social networks, event detection, geo-tag, spatiotemporal, Shannon entropy

DOI: 10.3233/JIFS-169959

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3981-3992, 2019

Authors: Priyanga, S. | Gauthama Raman, M.R. | Jagtap, Sujeet S. | Aswin, N. | Kirthivasan, Kannan | Shankar Sriram, V.S.

Article Type: Research Article

Abstract: Despite the increasing awareness of cyber-attacks against Critical Infrastructure (CI), safeguarding the Supervisory Control and Data Acquisition (SCADA) systems remains inadequate. For this purpose, designing an efficient SCADA Intrusion Detection System (IDS) becomes a significant research topic of the researchers to counter cyber-attacks. Most of the existing works present several statistical and machine learning approaches to prevent the SCADA network from the cyber-attacks. Whereas, these approaches failed to concern the most common challenge, “Curse of dimensionality”. This scenario accentuates the necessity of an efficient feature selection algorithm in SCADA IDS where it identifies the relevant features and eliminates the redundant …features without any loss of information. Hence, this paper proposes a novel filter-based feature selection approach for the identification of informative features based on Rough Set Theory and Hyper-clique based Binary Whale Optimization Algorithm (RST-HCBWoA). Experiments were carried out by Power system attack dataset and the performance of RST-HCBWoA was evaluated in terms of reduct size, precision, recall, classification accuracy, and time complexity. Show more

Keywords: SCADA, intrusion detection system, Rough Set Theory (RST), hyperclique property, feature selection

DOI: 10.3233/JIFS-169960

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3993-4003, 2019

Authors: Umadevi, K.S. | Balakrishnan, P. | Kousalya, G.

Article Type: Research Article

Abstract: Operational Technology (OT) often refers to the industrial control systems which are used to monitor and control the devices and processes of critical infrastructure like, water treatment plant, power grid and sewage systems. Conventionally, these OT systems are completely isolated from Information Technology (IT) infrastructure to protect their processes and devices against cyber-attacks. However, the convergence of IT and OT is inevitable to improvise the remote management of physical devices and to enhance the production by incorporating data-driven decision making by accessing and analyzing their real-time data. To achieve this, the isolated OT systems and devices need to be accessed …using Internet. However, this interconnection leads both the sensor and control data of OT systems vulnerable to cyber-attacks. This research work extends our previous intrusion detection system that identifies anomalies which are deviated from process-invariants in secured water treatment (SWaT) test-bed data obtained from Singapore University of Technology and Design (SUTD). Additionally, it proposes process-invariants based timed automata wherein the attack and its detection model are represented as timed automata. The proposed system is implemented and validated using UPPAAL, a tool for validating real-time systems represented as networks of timed automata. The results conclude that the proposed system effectively identifies the attacks considered thereby recommending the timed automata as an operational tool for detecting the data-integrity attacks in critical infrastructures. The highly reported attacks that include level indicators, motorized valves, pressure indicators and analyzer indicators are detected successfully by the proposed system. Using the results, Stage 1 and Stage 3 are highly vulnerable. Show more

Keywords: Cyber physical system, intrusion detection system, timed automata, UPPAAL

DOI: 10.3233/JIFS-169961

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4005-4015, 2019

Authors: Huang, He | Zeng, Shufang | Sangaiah, Arun Kumar | Wang, Jin

Article Type: Research Article

Abstract:  In contrast to conventional preprocessing aided spatial modulation (PSM), which carries partial information using the indexes of receive antennas, we exploit one receive antenna to implicitly convey information and meanwhile harvest energy at the remaining antennas. Based on this, we propose two novel beamforming schemes. The first scheme is to maximize the sum energy harvested by the receiver. And the second scheme is to maximize the minimum receiving power on each antenna except for the antenna that conveys information. A closed form solution and an iterative algorithm are given, respectively. Simulation results demonstrate that proposed two schemes can harvest a …certain amount of energy with nearly same achievable rate compared to the benchmark schemes. But the second scheme is superior to the first scheme and PSM scheme in terms of bit error rate (BER) performance. Show more

Keywords: Space shift keying (SSK), multiple-input multiple-output (MIMO), simultaneous wireless information and power transfer (SWIPT), transmitter zero forcing (TZF), algorithm design

DOI: 10.3233/JIFS-169962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4017-4023, 2019

Authors: Sundaram, Ramakrishnan | Ravichandran, K.S.

Article Type: Research Article

Abstract:  This paper proposes a prediction system to identify the type of eye diseases like glaucoma and diabetic retinopathy. The proposed system processes the images captured using the fundus camera that is connected to the computer. The acquired fundus images are fed into the proposed prediction system which can be deployed in the cloud, and it identifies the type of disease. This forms a cyber-physical system. Underdeveloped countries which do not have the necessary infrastructure can utilize this service when this system is deployed in the cloud. For identifying these diseases, ophthalmologists extract parameters manually from the fundus image, which is …a difficult task. Hence, this research work attempts to develop a system to automate the feature extraction from fundus images and with the extracted features, eye diseases are predicted. From the literature, it is found that many research works were focused on the binary classification of any one disease. In this paper, a novel classification methodology is proposed that helps the experts and clinicians to classify Diabetic Retinopathy, Glaucoma and healthy eye images with more accuracy. The proposed system with high accuracy is designed with the following phases: i) image acquisition, ii) image enhancement, iii) local features extraction using Speeded Up Robust Feature (SURF), iv) Bag of Features/Visual Words (BoF/BoVW) obtained through k-means clustering of local features, and v) classification using Error-Correcting Output Code (ECOC) linear SVM. It is inferred from the results that proposed method of classification using BoVW provided a maximum accuracy of 92% when compared to other state-of-the-art recent literature. Show more

Keywords: Fundus image, image enhancement, bag of features, support vector machine, classification

DOI: 10.3233/JIFS-169963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4025-4036, 2019