Journal of Intelligent & Fuzzy Systems - Volume 39, issue 6

Authors: Arun, Ranganathan | Balamurugan, Rangaswamy

Article Type: Research Article

Abstract: In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH …(CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs. Show more

Keywords: Wireless Sensor Network (WSN), cluster head (CH), Distributed Entropy Energy-Efficient Clustering (DEEEC), distributed, heterogeneous, Chaotic Firefly Algorithm (CFA), clustering, and energy consumption

DOI: 10.3233/JIFS-189135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8139-8147, 2020

Authors: Li, Ping | Ni, Zhiwei | Zhu, Xuhui | Song, Juan

Article Type: Research Article

Abstract: Domain adaptation (DA) aims to train a robust predictor by transferring rich knowledge from a well-labeled source domain to annotate a newly coming target domain; however, the two domains are usually drawn from very different distributions. Most current methods either learn the common features by matching inter-domain feature distributions and training the classifier separately or align inter-domain label distributions to directly obtain an adaptive classifier based on the original features despite feature distortion. Moreover, intra-domain information may be greatly degraded during the DA process; i.e. , the source data samples from different classes might grow closer. To this end, this …paper proposes a novel DA approach, referred to as inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding (IDAME). Specifically, IDAME commits to adapting the classifier on the Grassmann manifold by using structural risk minimization, where inter-domain feature distributions are aligned to mitigate feature distortion, and the target pseudo labels are exploited using the distances on the Grassmann manifold. During the classifier adaptation process, we simultaneously consider the inter-class distribution alienation, the inter-domain distribution alignment, and the manifold consistency. Extensive experiments validate that IDAME can outperform several comparative state-of-the-art methods on real-world cross-domain image datasets. Show more

Keywords: Domain adaptation, structural risk minimization, maximum mean discrepancy, manifold embedding

DOI: 10.3233/JIFS-189136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8149-8159, 2020

Authors: Khan, Anwar | Ur Rahman, Atiq | Zareei, Mahdi | Us Sama, Najm | Vargas-Rosales, Cesar | Qadir, Junaid | Mohamed, Ehab Mahmoud

Article Type: Research Article

Abstract: In underwater acoustic networks (UANs), an acoustic modem is one of the key elements that governs underwater communications. The major demerit of the existing surveys conducted on acoustic modems is that such surveys do not comprise of the novel and state-of-the-art modems. In addition, the described characteristics do not specify the full potential and capabilities of the designed modems. This, in consequence, makes it difficult to identify the parameters for improvement in future investigation. Also, it does not provide the optimal choice of a modem for various underwater applications. To overcome these challenges, this paper considers the latest acoustic modems …designed in the last five years. Further, the modems are classified in terms of their suitability for the various applications, which helps the selection of the appropriate modem for a particular application scenario. Furthermore, the specification of every modem is described in a manner so that it is completely analyzed in highlighting its promising performance parameters as well exposing its region of compromised performance. Besides, acoustic modems developed by commercial manufacturers are also described. These strategies help researchers, scientists and engineers to compare to opt for the right modem for the right application. They also provide knowledge of the margin for improvement in the characteristics of these modems. Finally, perspectives are given for future investigation to make these modems more efficient, robust, smart and effective. Show more

Keywords: Acoustic modem, underwater acoustic networks, underwater communications, acoustic modem design

DOI: 10.3233/JIFS-189137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8161-8171, 2020

Authors: Jeon, Gwanggil

Article Type: Research Article

Abstract: Deinterlacing is the procedure of reworking interlaced video signal into a non-interlaced format. The interlaced video signal is generally used in both of analog TV and HDTV with 1080i. The deinterlacing method was designed to solve a legacy issue, therefore the interlaced signal which was requested by analog TV must be trans-formed to be displayed on HDTV. This paper introduces fuzzy concept-based deinterlacing method and shows how it can be executed on various test set. The proposed method includes weight calculation stage and deinterlacing implementation stage. In weight calculation stage, I compute correct weight values on each edge direction, which …are calculated using fuzzy concepts. In deinterlacing implementation stage, the computed weight values are used to obtain final deinterlacing pixels by multiplying them with candidate existent pixels. The presented method provides outstanding subjective and objective results in various data set. Show more

Keywords: Fuzzy concept, intelligent system, cognitive concept color image

DOI: 10.3233/JIFS-189138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8173-8179, 2020

Authors: Saravanan, P. | Sri Ram, E. | Jangiti, Saikishor | Ponmani, E. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: Dysarthria is a speech disorder caused by stroke, Parkinson’s disease, neurological injury, or tumors that damage the nervous system and weaken the speech quality. Developing a unique voice command system for Dysarthric speech helps to recognize impaired speech and convert them into text or input commands. Hidden Markov Model (HMM) is one of the widely used generative model-based classifiers for Dysarthric speech recognition. But due to insufficient training data, HMM doesn’t provide optimal results on overlapping classes. We propose an ensemble Gaussian mixture model to recognize impaired speech more accurately. Our model converts the sequence of feature vectors into a …fixed dimensional representation of patterns with varying lengths. The performance efficiency of the proposed model is evaluated on the Dysarthric UA-speech benchmark dataset. The discriminatory information provided by the proposed approach yields better classification accuracy even for shallow intelligibility words compared to conventional HMM. Show more

Keywords: Dysarthric speech recognition, ensemble, hidden Markov model, classification

DOI: 10.3233/JIFS-189139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8181-8189, 2020

Authors: Akshya, J. | Priyadarsini, P.L.K.

Article Type: Research Article

Abstract: In recent times, Unmanned Air Vehicles (UAVs) are being deployed for several tasks of terrain coverage such as surveillance, photogrammetry, smart irrigation, civil security, and disaster management. In these applications, one of the most vital issues to be addressed is, covering the area under observation with minimum traversal for the UAV. So, the problem addressed in this paper is as follows: For a given geographic area and the given parameters describing the UAV’s coverage capacity, the problem is to find an optimal route that covers the given geographic area. In this paper, an optimal path planning algorithm for the area …under observation, given as a closed curve, is proposed. The algorithm partitions the given area of interest into multiple non-uniform rectangles while considering other parameters such as the flying altitude of the UAV and obstacles that could be encountered during its flight. The problem is transformed into Traveling Salesman Problem by constructing a graph from the rectangular partitioning. Effective approximate solutions are provided to this problem, using the Minimum Spanning Tree (MST) approximation algorithm and Ant Colony Optimization (ACO). The experimental results show that ACO outperforms the MST based algorithm as it does not get stuck in local minima. Show more

Keywords: Travelling salesman problem, minimum spanning tree, area coverage, path planning, ant colony optimization, UAV

DOI: 10.3233/JIFS-189140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8191-8203, 2020

Authors: Ragala, Ramesh | Bharadwaja Kumar, G

Article Type: Research Article

Abstract: Due to the massive memory and computational resources required to build complex machine learning models on large datasets, many researchers are employing distributed environments for training the models on large datasets. The parallel implementations of Extreme Learning Machine (ELM) with many variants have been developed using MapReduce and Spark frameworks in the recent years. However, these approaches have severe limitations in terms of Input-Output (I/O) cost, memory, etc. From the literature, it is known that the complexity of ELM is directly propositional to the computation of Moore-Penrose pseudo inverse of hidden layer matrix in ELM. Most of the ELM variants …developed on Spark framework have employed Singular Value Decomposition (SVD) to compute the Moore-Penrose pseudo inverse. But, SVD has severe memory limitations when experimenting with large datasets. In this paper, a method that uses Recursive Block LU Decomposition to compute the Moore-Penrose generalized inverse over the Spark cluster has been proposed to reduce the computational complexity. This method enhances the ELM algorithm to be efficient in handling the scalability and also having faster execution of the model. The experimental results have shown that the proposed method is efficient than the existing algorithms available in the literature. Show more

Keywords: Recursive Block LU Decomposition, Extreme Learning Machine, Apache Spark, Moore-Penrose pseudoinverse, Big Data

DOI: 10.3233/JIFS-189141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8205-8215, 2020

Authors: Giełczyk, Agata | Choraś, Michał

Article Type: Research Article

Abstract: Biometrics, as an intelligent and secure authentication method, has recently become increasingly popular. Modern society uses fingerprints, iris and face recognition on a daily basis, even on a large scale; for example, in biometric passports. However, there are still other biometric traits that may provide sufficiently high accuracy but have not been widely implemented so far, e.g. palmprints. In this article, we propose a novel human-centred method of palmprint-based user verification. The proposed method is dedicated to the mobile devices and provides the accuracy reaching 94.5%. Moreover, the method is time-computing efficient and gives the response in less than 0.2 s. …All the experiments described in the article were performed using the benchmark PolyU database and three widely available mobile phones. Show more

Keywords: Mobile biometrics, palmprint, pattern recognition, intelligent user authentication, security, image processing

DOI: 10.3233/JIFS-189142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8217-8224, 2020

Authors: Babu, Bandla Pavan | Indragandhi, V

Article Type: Research Article

Abstract: In an Electrical system, Power Quality (PQ) is becoming significant to all types of consumers. With the increase of power demand from end users, maintaining the quality of power within the limitations is a major problem. In this paper, harmonic analysis in a grid connected three phase induction motor is tested according to PQ international standards which are found in the International Electro technical Commission (IEC). These mentioned standards are maintained in the transmission line and fed to the induction motor through a regenerative grid simulator. With the results obtained, execution of this fuzzy control system can be investigated through …the digital simulation, which is based on MATLAB-SIMULINK package. It provides human operands to constitute a knowledge base which is used for diagnosing power quality and capable of predicting abnormal operation in Industries. Show more

Keywords: Power quality, power quality issues and fuzzy logic controller

DOI: 10.3233/JIFS-189143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8225-8235, 2020

Authors: Devarajan, Malathi | Sasikaladevi, N.

Article Type: Research Article

Abstract: With the growing trend of Communication Technologies (ICT), buying goods through online has been increased drastically. Besides, e-payment makes online purchase easier and made our daily life more convenient. However, there exists a user privacy and data security issue in conventional e-payment systems. Thus, a hyper elliptic curve based signcryption scheme is proposed to achieve reduced computational cost. Because, most of the payment is processed on resource constraint devices like smart phone, hence an energy efficient e-payment system is in requisite. In order to ensure user authenticity, an aadhaar number (unique identity) is used to generate signcryption key. The proposed …signcryption scheme can be implemented in real-time applications like e-payment system to ensure confidentiality, privacy, authenticity and integrity. The security of the system is validated through a simulation tool – AVISPA (Automated Validation of Internet Security Protocols and Applications). Further, the resistivity against various cryptographic attacks was analyzed informally and also the computational cost is estimated and compared with other related schemes. Show more

Keywords: E-payment system, signcryption, hyper elliptic curve, security analysis, cybersecurity

DOI: 10.3233/JIFS-189144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8237-8247, 2020

Authors: Vasudev, R. Aravind | Anitha, B. | Manikandan, G. | Karthikeyan, B. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: Heart diseases are one of the crucial diseases that may cause fatality in both men and women. About 12 million deaths occur across the world due to heart diseases. With the advancement in information technology, it is possible for the Healthcare industry to store enormous volume of data containing millions of patient’s medical information along with their treatment details. If utilized in an efficient manner, this information helps the doctors to diagnose the diseases in a precise manner. Data mining algorithms are employed to analyse huge data sets and to discover unseen patterns. Data mining plays an essential role in …medical diagnosis. Doctors bank on different computer models which uses data mining algorithms to prefigure different kinds of diseases in patients. So, the need is to design a methodical data mining algorithm that helps for better forecast of diseases. The main goal of this work is to create an ensemble of algorithms which results in better accuracy. The ensemble is constructed by making use of stacking ensemble technique, which comprises of two categorization algorithms namely Naïve Bayes and Artificial Neural Network. The Cleveland heart disease data set acquired from UCI machine learning repository containing 14 attributes and 303 instances is given as input to these algorithms. From our experimental analysis it is evident that the proposed ensemble scheme results in a better accuracy. Show more

Keywords: Cardio vascular disease, naïve bayes, neural network, stacking, resilient back propagation

DOI: 10.3233/JIFS-189145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8249-8257, 2020

Authors: Sengan, Sudhakar | Arokia Jesu Prabhu, L. | Ramachandran, V. | Priya, V. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: In the last decade, numerous researches have been focused on Image Super-Resolution (SR); this recreation or improvement model is vital in different research areas. Recently, deep learning algorithm finds useful to advance in the resolution of the medical output. Here, we devise a novel Deep Convolutional Network model along with the optimal learning rate of the Rectified Linear Unit (ReLU) intended for Medical Image Super-Resolution (MISR). For getting the optimal values of Deep Learning AlexNet structure, Modified Crow Search (MCS) is utilized, which is mainly depends on the behavior of crow sets. The chosen Alexnet lacks in a sort of …suitable supervision for upgrading execution of the proposed model that effectively aims to overfit. The proposed design, i.e., MISR, named Deep Optimal Convolutional AlexNet (DOCALN), derives the optimal values of learning rates of the ReLU activation function. Based on this optimal deep learning structure, the Low Resolution (LR) medical images can be applied. Experimentation results of our proposed model are compared with variants of Convolution Neural Networks (CNN) concerning different measures such as image quality assessment, SR efficiency analysis, and execution time. Show more

Keywords: Deep learning, super resolution, optimization, convolutional neural network, alexnet, deep learning

DOI: 10.3233/JIFS-189146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8259-8272, 2020

Authors: Kirn Kumar, N. | Indra Gandhi, V.

Article Type: Research Article

Abstract: As the world is moving towards green energy generation to reduce the pollution by renewable sources such as wind, solar, geothermal and more. These sources are intermittent in nature, to coordinate and control with traditional power generating units a control technique is necessary. This paper mainly focuses on the design of fuzzy based classical controller using a PSO algorithm for optimal controller gains to control the frequency variations in island hybrid power system. The considered mathematical model comprises of a diesel generating model, wind turbine generator and a battery storage system. Fuzzy is an intelligent controller which is designed with …trial and error rules or on the basis of past experience provided by experts or by optimization methods for optimized gains using computational algorithms. To give best solution for these kinds of problems with FLCs traditional controllers are integrated with fuzzy logic. The PSO algorithm is applied to tune the classical controller gains to decrease the frequency deviation of the island power system, during the different load and wind disturbances. The Fuzzy PID classical controller shows the best performance compared with the only fuzzy and Fuzzy-PI controller configurations by illustrating the under shoot, overshoot and settling time and the proposed method is robust for various loading conditions and different wind changes. Show more

Keywords: Battery energy storage, fuzzy logic controller, load frequency control, particle swarm optimization, renewable energy sources

DOI: 10.3233/JIFS-189147

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8273-8283, 2020

Authors: Meena, V. | Gireesha, Obulaporam | Krithivasan, Kannan | Shankar Sriram, V.S.

Article Type: Research Article

Abstract: Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming …and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time. Show more

Keywords: Computational offloading, fuzzy logic, mobile cloud computing, multisite offloading, swarm optimization

DOI: 10.3233/JIFS-189148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8285-8297, 2020

Authors: Ni, Zhiwei | Xia, Pingfan | Zhu, Xuhui | Ding, Yufei | Ni, Liping

Article Type: Research Article

Abstract: Ensemble pruning has been widely used for enhancing classification ability employing a smaller number of classifiers. Ensemble pruning extracts a part of classifiers with good overall performance to form the final ensemble. Diversity and accuracy of classifiers are of vital importance for a successful ensemble. It is hard for the members in one ensemble to achieve both good diversity and high accuracy, simultaneously, because there is a tradeoff between them. Existing works usually search for the tradeoff in terms of diversity measures, or find it utilizing heuristic algorithms, which cannot gain the exact solution without exhaustive search. To address the …above issue, a novel ensemble pruning method based on information exchange glowworm swarm optimization and complementarity measure, abbreviated EPIECM, is proposed using the combination of information exchange glowworm swarm optimization (IEGSO) and complementarity measure (COM). Firstly, multiple generated classifiers are utilized to construct a pool of learners who perform diversely. Secondly, COM is employed to pre-prune the classifiers with poor comprehensive performance, and the pre-pruned ensemble is formed utilizing the retaining classifiers. Finally, the optimal subset of classifiers is combined from the remaining constituents after pre-pruning with IEGSO. Empirical results on 27 UCI datasets indicate that EPIECM significantly outperforms other techniques. Show more

Keywords: Ensemble pruning, glowworm swarm optimization, information exchange, complementarity measure

DOI: 10.3233/JIFS-189149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8299-8313, 2020

Authors: Nath, Keshab | Dhanalakshmi, R | Vijayakumar, V. | Aremu, Bashiru | Hemant Kumar Reddy, K. | Xiao-Zhi, Gao

Article Type: Research Article

Abstract: Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those …derived communities as hidden or hierarchical communities. In this work, we present a method called H idden C ommunity based on N eighborhood Similarity C omputation (HCNC ) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks. Show more

Keywords: Embedded Community, Intrinsic structure, Hidden community, Neighborhood similarity, Community Strength, Social graphs

DOI: 10.3233/JIFS-189150

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8315-8324, 2020

Authors: Viloria, Amelec | Lezama, Omar Bonerge Píneda | Varela, Noel

Article Type: Research Article

Abstract: As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, and government offices have begun to invest a ton of energy together towards the acknowledgment of the idea of huge scope vehicular interchanges. One of the primary methodologies in this kind of system is the advancement of remote advances and their assorted organizations, concentrating on the association with the Internet through WiFi systems, cell systems, or specially appointed vehicular systems. VANETs …are essentially intended to give data trade through Vehicle to Vehicle (V2V) and Vehicle to foundation (V2I) interchanges, permitting ceaseless network and being exceptionally utilized for short-range correspondence, with high transmission speed through which it is proposed that clients keep up an association and distinguish occasions about clog or street conditions. This exploration presents a vehicular situation that tries to acquire a sufficient presentation while executing a heterogeneous network in a few segments of the city of Bogotá, Colombia. Show more

Keywords: Coverage, heterogeneous, throughput, VANET, WIFI, DSRC

DOI: 10.3233/JIFS-189151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8325-8332, 2020

Authors: Karthikeyan, M.P. | Venkatesan, R. | Vijayakumar, V. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum …misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods. Show more

Keywords: White blood cell, Eulers Principal Axis, Jenks Optimization, pooling, Multinomial Logistic Classification

DOI: 10.3233/JIFS-189152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8333-8343, 2020

Authors: Alamelu, M. | Pradeep Kumar, T.S. | Vijayakumar, V.

Article Type: Research Article

Abstract: Service Level Agreement (SLA) is an agreement between the service provider and consumer to provide the verifiable quality of services. Using the valuable metrics in SLA, a service consumer could easily evaluate the service provider. Though there are different types of SLA models are available between the consumer and provider, the proposed approach describes the Fuzzy rule base SLA agreement generation among multiple service providers. A negotiation system is designed in this work to collect the different sets of provider services. With their desired quality metrics, a common Fuzzy based SLA report is generated and compared against the existing consumer …requirements. From the analysis of the common agreement report, consumers can easily evaluate the best service with the desired Impact service, cost and Quality. The main advantage of this approach is that it reduces the time consumption of a consumer. Moreover, the best service provider can be selected among multiple providers with the desired QoS parameters. At the same time, the bilateral negotiation is enhanced with the approach of multilateral negotiation to improve the searching time of consumers. Show more

Keywords: Multiparty negotiation, SLA, expert advice, service level agreement, fuzzy based support system

DOI: 10.3233/JIFS-189153

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8345-8356, 2020

Authors: Stephan, Thompson | Rajappa, Ananthnarayan | Sendhil Kumar, K.S. | Gupta, Shivang | Shankar, Achyut | Vijayakumar, V.

Article Type: Research Article

Abstract: Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably …towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols. Show more

Keywords: Vehicular ad hoc network (VANET), fuzzy logic, routing, mobility, trust

DOI: 10.3233/JIFS-189154

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8357-8364, 2020

Authors: Chandrasekar, Jayakumar | Madhawa, Surendar | Sangeetha, J.

Article Type: Research Article

Abstract: A robust disruption prediction system is mandatory in a Tokamak control system as the disruption can cause malfunctioning of the plasma-facing components and impair irrecoverable structural damage to the vessel. To mitigate the disruption, in this article, a data-driven based disruption predictor is developed using an ensemble technique. The ensemble algorithm classifies disruptive and non-disruptive discharges in the GOLEM Tokamak system. Ensemble classifiers combine the predictive capacity of several weak learners to produce a single predictive model and are utilized both in supervised and unsupervised learning. The resulting final model reduces the bias, minimizes variance and is unlikely to over-fit …when compared to the individual model from a single algorithm. In this paper, popular ensemble techniques such as Bagging, Boosting, Voting, and Stacking are employed on the time-series Tokamak dataset, which consists of 117 normal and 70 disruptive shots. Stacking ensemble with REPTree (Reduced Error Pruning Tree) as a base learner and Multi-response Linear Regression as meta learner produced better results in comparison to other ensembles. A comparison with the widely employed stand-alone machine learning algorithms and ensemble algorithms are illustrated. The results show the excellent performance of the Stacking model with an F1 score of 0.973. The developed predictive model would be capable of warning the human operator with feedback about the feature(s) causing the disruption. Show more

Keywords: GOLEM Tokamak, stacking, REPTree algorithm, multi-response linear regression, disruption prediction

DOI: 10.3233/JIFS-189155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8365-8376, 2020

Authors: Bragadeesh, Srinivasan Ananthanarayanan | Umamakeswari, Arumugam

Article Type: Research Article

Abstract: Traceability and food quality are significant challenges in realizing a reliable food supply chain. The reliability of data in supply chains is one of the critical factors. Ensuring transparency, integrity, and availability is the primary requirement for establishing a proper supply chain network. Blockchain is a distributed structure of immutable records that are chained together to form blocks. It provides a guarantee of storing the data correctly and reliably. Smart contracts, which are self-executing contracts containing the terms of the agreement between the entities involved, provide utility for automation of reputation calculation with the transactions. Reputation systems allow participants to …rate each other, thus building trust through reputation. The present reputation systems have bounded scrutiny and lack granularity; hence they are not ideal for supply chains. In this work, we propose a reliable supply chain framework using blockchain and smart contracts. It uses a consortium blockchain network to trace communication between the participants and to calculate reputation scores dynamically. Rewards and penalties are assigned to the participants of the supply chain network based on the food product quality involved in the trade. The network participants have defined roles and the access permissions govern who can access the ledger. An immutable ledger stores all the transactions occurring in the network. Any change in one block will reflect in the consecutive blocks, which ensures the data is reliable and secure. The proposed system is implemented using Hyperledger Composer. The proposed framework is evaluated in terms of throughput and latency for varying asset size and batch size using the benchmarking tool Caliper. Results show that the security and reliability provided by the proposed framework justify the overheads in contrast to a trading model that does not include a blockchain network. Show more

Keywords: Blockchain, hyperledger composer, penalty, reputation, smart contracts

DOI: 10.3233/JIFS-189156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8377-8387, 2020

Authors: Kamili, Asra | Fatima, Izat | Hassan, Muzamil | Parah, Shabir A. | Vijaya Kumar, V. | Ambati, L. S.

Article Type: Research Article

Abstract: Embedding information in medical images is considered as one of the significant methods for safeguarding the integrity and authenticity of medical images besides providing security to electronic patient records (EPR). The conventional embedding methods deteriorate the perceptual quality of medical images making them unsuitable for proper diagnosis. To preserve the perceptual quality of medical images reversible embedding is used. The reversible embedding schemes, however, have less embedding capacity. In this work, a reversible scheme based on histogram bin shifting and RGB plane concatenation has been proposed which offers high embedding capacity as well. We have exploited the fact that medical …images, unlike general images, consist of a large number of peaks and zero points that can be employed for reversibly embedding the data. Reversibility ensures that original image restoration takes place after the extraction of embedded data, which is of great importance in medical images for proper diagnosis and treatment. We have used various subjective and objective image quality metrics for analyzing the scheme. The proposed scheme has been shown to provide a Peak Signal to Noise Ratio (PSNR) value of above 56 dB for an embedding capacity of 0.58 bits per pixel (bpp). The results obtained show that the performance of scheme presented is far better in comparison to the state-of-the-art. Show more

Keywords: Medical images, security, authentication, reversibility, electronic patient record

DOI: 10.3233/JIFS-189157

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8389-8398, 2020

Authors: Chernyi, Sergei G. | Vyngra, Aleksei V. | Novak, Bogdan P.

Article Type: Research Article

Abstract: In order to implement and demonstrate all the processes associated with the real stability trial system on vessels, a ship’s model was made. The developed model consists of electrical and hardware parts. It is concluded that the model is applicable for the study of issues of automatic control of the ship’s list, simulating various loading options. Scalable loading studies of various types and sizes of cargo were carried out. The results of the study showed the correct operation of the model according to a specified algorithm. To work with the microcontroller and to code, the mathematical modeling environment Matlab/Simulink was …used. The results of the study showed that the created control system is able to secure the vessel during various types of loading operations, speed up the loading process, thus reducing the time spent at the port stay and save port costs. Show more

Keywords: Stability, electrical and hardware parts, automatic control, specified algorithm, microcontroller, code, Simulink

DOI: 10.3233/JIFS-189158

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8399-8408, 2020

Authors: Jha, Sudan | Prashar, Deepak | Elngar, Ahmed A.

Article Type: Research Article

Abstract: In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and …improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities. Show more

Keywords: Cloud environment, modified filtering algorithm (MFA), heuristic algorithms, PSO, task scheduling, quantum time

DOI: 10.3233/JIFS-189159

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8409-8417, 2020

Authors: Venkatesh, Veeramuthu | Anishin Raj, M. M. | Mohamed Sajith, K. | Anushiadevi, R. | Suriya Praba, T.

Article Type: Research Article

Abstract: Cancer is a prevalent disease which comes in several forms. The need of the hour in cancer research is to be able to diagnose cancer in its early stages. The furthermost common forms of cancer among women us breast cancer. In recent times, there has been a drastic increase in the number of breast cancer cases among women. As a wide range of medical data is available in electronic form and with easy access to Machine Learning(ML) techniques disease progression risk evaluation has been made easier. These ML tools can aid in giving us complex insights from the massive amounts …of available data. Some of the techniques used for developing predictive models for perfect decision making in cancer research are Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs). Although it is acceptable that ML is used to predict cancer progression, we need some level of validation. In this paper, we have come up with a review of several ML methods in modelling cancer progression. We discuss several predictive models based on supervised ML techniques and the inputs given by users, along with the data available. The results that were obtained from Logistic Regression show us that this method gave a significantly higher accuracy than most other classifiers. The best accuracy is 98.2%, however, the best precision and recall is 100 and 98.60% correspondingly. Show more

Keywords: Machine learning, cancer susceptibility, predictive models, feature selection techniques, breast cancer

DOI: 10.3233/JIFS-189160

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8419-8426, 2020

Authors: Espinosa-Leal, Leonardo | Chapman, Anthony | Westerlund, Magnus

Article Type: Research Article

Abstract: Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper, we indicate the distinctions between automated and autonomous systems as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as …digital twins, how to train reinforcement learning agents to learn specific tasks through generalisation. Once generalisation is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment that focuses on how the agents can adapt to different environments. We introduce an initial prototype of our ideas by solving a multi-armed bandit problem using two ε -greedy algorithms. Further, we discuss future applications in the industrial management realm and propose a modular architecture for improving the decision-making process via autonomous agents. Show more

Keywords: Autonomous systems, reinforcement learning, self-play, digital twin, industry 4.0

DOI: 10.3233/JIFS-189161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8427-8439, 2020

Authors: Sekar, K. R. | Thaventhiran, C. | Sathiamoorthy, G.

Article Type: Research Article

Abstract: Vitiligo is a problem due to the destruction of melanocytes which is present in 1% of people all over the world. The origin of this disease is unknown and difficult to cure. Absence of melanin in the body causes lesions which will ooze out all over the body. Phototherapy and surgical therapy are the two types of treatment available in the existing world. The Cytotoxic CD8 + T lymphocytes act as a major factor for the abovesaid disease. The main objective of this work is to treat patients with different methods according to the severity of vitiligo, which can be identified with …the help of out ranking based on hesitant fuzzy relation. Multi-criteria and multi-objective hesitant fuzzy are applied in this work to find out the ranking through which the severity of the disease is detected. This method helps in identifying the vitiligo lesions, which can be treated effectively in a short period of time. During the application of vitiligo treatment, FQA-TOPSIS (Fuzzy Quantified Attribute-TOPSIS) hesitant fuzzy relation methodology is deployed with three decision maker’s support using linguistic and intuitionistic values. The decision maker’s fuzzy values will be normalized and aggregated in this work with improved methodologies. The two objectives are deployed with their own fuzzy values and are implemented in the decision maker’s values. In the article fuzzy weightage has been calculated in two ways. One is every linguistic like low, medium, high and very high has got its significant intuitionistic values that all will be available with the scale of 1 to 10. The same has given as triplets. In our research work the above said has applied with the objective based weightage. So the accuracy has been increased through the work. The outcome of this methodology is to find out the coefficient closeness of the alternatives and to out rank the decision alternatives. The difference between the Final +ve Ideal Solution (FPIS) and Final -ve Ideal Solution (FNIS) is determined and FQA-TOPSIS Hesitant Fuzzy is ranked in the result. Show more

Keywords: FQA-TOPSIS, hesitant fuzzy, cytotoxic, vitiligo, melanocytes, final positive ideal solution and final -ve ideal solution

DOI: 10.3233/JIFS-189162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8441-8451, 2020

Authors: Palanisamy, R. | Mohana Sundram, K. | Selvakumar, K. | Boopathi, C.S. | Selvabharathi, D. | Vijayakumar, V.

Article Type: Research Article

Abstract: An Artificial Neural Network (ANN) based Space Vector Pulse Width Modulation (SVPWM) for five level cascaded H-bridge inverter (CHBI) fed grid connected photovoltaic (PV) system. The multilevel inverter topologies are offers better performance compare conventional two level inverter like reduced total harmonic distortion, less electromagnetic interferences and voltage stresses across switches are low. The ANN based SVPWM generates the switching pulses for cascaded H-bridge inverter; it improves the accuracy in reference vectors tuning and identification, which leads to improve the inverter output voltage, better utilization of dc-link voltage and controlled output current. The ANN control makes the implementation of SVPWM …becomes simple and minimizes the intricacy in tracking reference vector and calculation of switching time; it is suitable for any type of non-linear systems. This proposed system is energized using PV system and it is boosted using dc-dc boost converter, and the output of CHBI is synchronized with grid connected system using coupled inductor. The simulation and experimental results of ANN based SVPWM for CHBI is verified using simulink-matlab and DSP processor. Show more

Keywords: Artificial Neural Network (ANN), space vector pulse width modulation (SVPWM), cascaded H-bridge inverter (CHBI), photovoltaic (PV) system, DSP processor, multilevel inverter (MLI).

DOI: 10.3233/JIFS-189163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8453-8462, 2020

Authors: Srinivasan, Palanivel | Doraipandian, Manivannan

Article Type: Research Article

Abstract: Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The …developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams. Show more

Keywords: Artificial Neural Network, Context-free grammar, Rare event detection, streaming video monitoring

DOI: 10.3233/JIFS-189164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8463-8475, 2020

Authors: Revathy, P. | Mukesh, Rajeswari

Article Type: Research Article

Abstract: Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop’s architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these …critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. Prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured. Show more

Keywords: Big data, Hadoop security, data placement strategy for sensitive data, sensitivity in Hadoop, prescriptive analytics

DOI: 10.3233/JIFS-189165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8477-8486, 2020

Authors: Devarajan, Malathi | Sasikaladevi, N.

Article Type: Research Article

Abstract: With ever growing popularity, wireless communication system also vulnerable to various security attacks. To provide high level security, many cryptographic solutions have been proposed. One such solution is signcryption, where authenticity and confidentiality provided by single logical step. Therefore, signcryption scheme helps to reduce computational cost, but it is not feasible for resource constraint environments. Because, most of the existing approaches were based on El-Gamal, bilinear pairing, Rivest-Shamir-Adleman (RSA), and Elliptic curve Cryptography (ECC). They consume more energy due to their increased key size. Hence, the new signcryption approach is proposed based on Hyper Elliptic Curve Cryptosystem (HECC) whose key …size is much lesser than ECC. It significantly reduces the cost of computation and communication overhead by half the amount of ECC which suits well for resource constraint environments. Further, the proposed scheme attains necessary security features along with forward secrecy and public verifiability. On the other hand, the security of the approach is validated through an automated protocol validation tool – AVISPA. Show more

Keywords: Hyper elliptic curve, signcryption, mutual authentication, security analysis, AVISPA

DOI: 10.3233/JIFS-189166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8487-8498, 2020

Authors: Varela, Noel | Pineda Lezama, Omar Bonerge | Neira, Harold

Article Type: Research Article

Abstract: The principle assaults on a Wireless Sensor Network (WSN) essentially influence the uprightness and accessibility of the data gathered, for example, Deni-al of Service, Blackhole, Wormhole, and assault on the data being transmitted. Privacy is not an important security objective because the data caught by the sensors are typically not delicate or mystery from individuals. A remote sensor organizes applied to shrewd metering frameworks might be adequately powerful as far as robotization and adjustment of the information that is gathered, however, if the system doesn’t have satisfactory security, both the client and the organization offering the support might be influenced …by assaults on the respectability and accessibility of the data transmitted. This research proposes the use of MESH encryption techniques and Star topology to find the best combination that meets the requirements of a Smart Metering System. Show more

Keywords: Security, encryption, topology, WSN, smart metering networks, cryptographic techniques

DOI: 10.3233/JIFS-189167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8499-8506, 2020

Authors: Varghese, Lijo Jacob | Sira Jacob, Suma | Banumathi, S. | Ravi, Logesh | Vairavasundaram, Subramaniyaswamy | Jacob Raglend, I.

Article Type: Research Article

Abstract: This work aims to improve the operation of multi-level inverter with reduced switching losses, thereby propose a new structure for an MLI with a reduced component count. A 27-level asymmetric Multi Level Inverter (MLI) with a minimal number of static switches is considered as a test system. The proposed MLI is developed with three input DC sources and thirteen power electronic switches. The hardware prototype is developed for 40 V and 3.5 A output. The control logic is developed in dsPIC30F410 controller. The main objective of this work is to effectively bring down the Total Harmonic Distortion (THD) of the resulting …output voltage by analyzing the harmonic spectrum of the proposed MLI configuration with various low frequencies switching techniques and optimizing their switching angles and to choose an appropriate switching state using fuzzy logic controller (FLC). The proposed FLC covers wide range of operating conditions i.e. 10 switching states and variables 9*9 rules to predict the suitable switching angel. The performance metrics of the proposed structure of 27-level MLI has been evaluated upon simulation results and experimental results based on hardware prototype. The comparative study also carried out with the recent MLI topologies. Show more

Keywords: Asymmetric multilevel inverter, fuzzy logic, total harmonic distortion, pulse width modulation, reduced number of switches, optimum switching angles.

DOI: 10.3233/JIFS-189168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8507-8519, 2020

Authors: Nagarajan, Manikandan | Sasikumar, A. | Muralidharan, D. | Rajappa, Muthaiah

Article Type: Research Article

Abstract: Approximate computing is a rapidly growing technique to speed up applications with less computational effort while maintaining the accuracy of error-resilient applications such as machine learning and deep learning. Inheritance properties of the machine and deep learning process give freedom for the designer to simplify the circuitry to speed up the computation process at the expense of accuracy of computational results. Fundamental blocks of any computation are adders. In order to optimize it for better performance, 2-bit multi-bit approximate adders (MAPX) are proposed in this work which breaks the lengthy carry chain. In contrast with other approximate larger width adders, …instead of using accurate adders for the most significant part, here proposed 2-bit MAPX-1 and MAPX-2 adders are arranged in various ways to compose most and least significant parts. Designed 8-bit and 16-bit adders are evaluated for their performance and error characteristics. Proposed 2-bit MAPX-2 shows better error characteristics whose MED is 0.250 while occupying less area and MAPX-1 consumes less power and delay at the cost of accuracy. Among the extended adders, MAPX 8-bit adder design1 outperforms the best performing APX based 8-bit adder design1. The error performance of it is improved by 14%, 42.1% and 50.4% compared to the existing well-performing APX 8-bit Design1, Design2 and Design3 respectively. Similarly, proposed MAPX 16-bit Design1 exhibits overwhelming performance compared to best performing APX 16-bit Design1, and its error performance is improved by 24.3%, 34.9% and 50.3% compared to APX 16-bit Design1, Design2 and Design3 respectively. In order to evaluate the proposed adder for a real application, extended MAPX 16-bit Design1 is fit in the convolution layer of Low Weights Digit Detector (LWDD) convolutional neural network-based digit classification system. Our modified system accelerates the computation process by 1.25 factors while exhibiting the accuracy of 91% and it best fits error-tolerant real applications. All the adders are synthesized and implemented in the Intel Cyclone IV EP4CE22F17C6N FPGA. Show more

Keywords: Approximate adders, multi-bit adders, CNN accelerator, digit classification, deep learning

DOI: 10.3233/JIFS-189169

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8521-8528, 2020

Authors: Martinaa, M. | Santhi, B. | Raghunathan, A.

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) is created, stemming from their applications in distinct areas. Huge sensor nodes are deployed in geographically isolated regions in WSN. As a result of uninterrupted transmission, the energy level of the nodes gets rapidly depleted. Sensor node batteries cannot be replaced or recharged often and maintaining the energy level is a crucial issue. Thus energy efficiency is the significant factor to be consider in WSN. This paper focuses to implement an efficient clustering and routing protocols for maximized network lifetime. Clustering has been confirmed as a successful approach in network organization. The fundamental responsibilities of the …clustering mechanism include improved energy efficiency and extended network lifespan. In this work, energy efficiency is improved to maximize lifespan of the WSN by proposing a novel method known as the Populated Cluster aware Routing Protocol (PCRP). The proposed method comprises three different steps: cluster formation, cluster head selection, and multi-hop data transmission. All sensor nodes are joined to a Cluster Head in a single hop in the cluster formation phase. Node distance is calculated and from which cluster head is selected. Then, cluster head aggregates the data from sensor nodes and transfer to the Base Station (BS). The shortest pathway is estimated by the Energy Route Request Adhoc On-demand Distance Vector (ERRAODV) algorithm. The proposed method considers the residual energy involved to attain high energy efficiency and network stability. The experimental analysis is demonstrated to validate the proposed method with existing, which improves the network lifespan. Vital parameters are validated using Network Simulator (NS2). Show more

Keywords: Energy consumption, clustering, routing protocol, network lifetime, wireless sensor networks

DOI: 10.3233/JIFS-189170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8529-8542, 2020

Authors: Nourbakhsh, Azamossadat | Moin, Mohammad-Shahram | Sharifi, Arash

Article Type: Research Article

Abstract: Face is the most important and most popular biometric used in many identification and verification systems. In these systems, for reducing recognition error rate, the quality of input images need to be as high as possible. Face Image Compliancy verification (FICV) is one of the most essential methods for this purpose. In this research, a brain functionality inspired model is presented for FICV using Haxby model, which is a face visual perception consistent model containing three bilateral areas for three different functionalities. As a result, contribution of this work is presenting a new model, based on human brain functionality, improving …the compliancy verification of face images in FICV context. Perceptual understanding of an image is the motivation of most of the quality assessment methods, i.e., the human quality perception is considered as a gold standard and a perfect reference for recognition and quality assessment. The model presented in this work aims to make the operational process of face image quality assessment system closer to the performance of a human expert. Three basic modules have been introduced. Face structural information, for initial information encoding, is simulated by an extended Viola-Jones model. Face image quality assessment is presented by International Civil Aviation Organization (ICAO), in ICAO (ISO / IEC19794 -11) requirements’ compliancy assessment document. Like Haxby model, perception is performed through two distinct functional and neurological pathways, using Hierarchical Maximum pooling (HMAX) and Convolutional Deep Belief Networks (CDBN). Information storing and fetching for training are similar to their corresponding modules in brain. For simulating the brain decision making, the final results of two separate paths are integrated by weighting sum operator. Nine ISO / ICAO requirements were used for testing the model. The simulation results, using AR and PUT databases, shows improvements in six requirements using the proposed method, in comparison with the FICV benchmark. Show more

Keywords: Haxby model, ICAO, facial images quality verification, HMAX model, CDBN

DOI: 10.3233/JIFS-189171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8543-8555, 2020

Authors: Jagdish, Mukta | Viloria, Amelec | Vargas, Jesus | Pineda Lezama, Omar Bonerge | Ovallos-Gazabon, David

Article Type: Research Article

Abstract: Cloud-based computation is known as the source architecture of the upcoming generation of IT enterprise. In context to up-coming trade solutions, the Information Technology sections are established under logical, personnel, and physical control, it transfers application software and large database to appropriate data centers, where security and management of database with services are not trustworthy fully. So this process may face many challenges towards society and organizations and that not been well understood over a while duration. This becomes one of the major challenges days today. So in this research, it focuses on security-based data storage using cloud, which plays …one of the important aspects bases on qualities of services. To assure user data correctness in the cloud system, a flexible and effective distributed technique with two different salient features was examined by utilizing the token called homomorphic with erasure-coded data for distributed verification, based on this technique it achieved error data localization and integration of storage correctness. Also, it identifies server misbehaving, efficient, and security-based dynamic operations on data blocking such as data append, delete, and update methods. Performance analysis and security show the proposed method is more effective resilient and efficient against Byzantine failure, even server colluding attacks and malicious data modification attacks. Show more

Keywords: Cloud security, architecture design, data storage, homomorphic token, dynamic operation

DOI: 10.3233/JIFS-189172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8557-8564, 2020

Authors: Quadir, Md Abdul | Christy Jackson, J. | Prassanna, J. | Sathyarajasekaran, K. | Kumar, K. | Sabireen, H. | Ubarhande, Shivam | Vijaya Kumar, V.

Article Type: Research Article

Abstract: Domain name system (DNS) plays a critical part in the functioning of the Internet. But since DNS queries are sent using UDP, it is vulnerable to Distributed Denial of Service (DDoS) attacks. The attacker can take advantage of this and spoof the source IP address and direct the response towards the victim network. And since the network does not keep track of the number of requests going out and responses coming in, the attacker can flood the network with these unwanted DNS responses. Along with DNS, other protocols are also exploited to perform DDoS. Usage of Network Time Protocol (NTP) …is to synchronize clocks on systems. Its monlist command replies with 600 entries of previous traffic records. This response is enormous compared to the request. This functionality is used by the attacker in DDoS. Since these attacks can cause colossal congestion, it is crucial to prevent or mitigate these types of attacks. It is obligatory to discover a way to drop the spoofed packets while entering the network to mitigate this type of attack. Intelligent cybersecurity systems are designed for the detection of these attacks. An Intelligent system has AI and ML algorithms to achieve its function. This paper discusses such intelligent method to detect the attack server from legitimate traffic. This method uses an algorithm that gets activated by excess traffic in the network. The excess traffic is determined by the speed or rate of the requests and responses and their ratio. The algorithm extracts the IP addresses of servers and detects which server is sending more packets than requested or which are not requested. This server can be later blocked using a firewall or Access Control List (ACL). Show more

Keywords: Amplification attacks, DRDoS, domain name system, network time protocol

DOI: 10.3233/JIFS-189173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8565-8572, 2020

Authors: Sengan, Sudhakar | Priya, V. | Syed Musthafa, A. | Ravi, Logesh | Palani, Saravanan | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new …computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation. Show more

Keywords: Deep convolutional neural network, computer-based automated detection, breast cancer screening, deep learning, machine learning, mammography, fuzzy logic.

DOI: 10.3233/JIFS-189174

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8573-8586, 2020

Authors: Agrawal, Deepika | Pandey, Sudhakar | Gupta, Punit | Kumar Goyal, Mayank

Article Type: Research Article

Abstract: Wireless Sensor Networks is a complex network with millions of small-scale sensor nodes, working together to detect certain physical phenomena. Sensor nodes are operated by battery therefore the major concern is energy efficiency. Clustering is an effective technique to decrease the energy depletion in the network. However, choosing the optimum Cluster Heads is an NP-Hard problem. This paper proposes an unequal clustering technique that selects probationary Cluster Heads through fuzzy logic and the optimization of this probationary Cluster Heads is done through Harmony Search Algorithm (HSA). The proposed algorithm exhibits the dynamic capability of fuzzy logic and high search efficiency …of HSA that extends the network lifespan. The findings are simulated against traditional clustering protocols and compared. The findings obtained show that the protocol proposed is performing superior in terms of network lifespan prolongation and other metrics. Show more

Keywords: Optimal cluster heads, optimization, harmony search algorithm, wireless sensor networks, unequal clustering

DOI: 10.3233/JIFS-189175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8587-8597, 2020

Authors: Abdulhay, Enas | Alafeef, Maha | Hadoush, Hikmat | Arunkumar, N.

Article Type: Research Article

Abstract: Autism is a developmental disorder that influences social communication skills. It is currently diagnosed only by behavioral assessment. The assessment is susceptible to the experience of the examiner as well as to the descriptive scaling standard. This paper presents a computer aided approach to discrimination between neuro-typical and autistic children. A new method- based on the computing of the elliptic area of the Continuous Wavelet Transform complex plot of resting state EEG- is presented. First, the complex values of CWT, as a function of both time and frequency, are calculated for every EEG channel. Second, the CWT complex plot is …obtained by plotting the real parts of the resulted CWT values versus the related imaginary components. Third, the 95% confidence value of the elliptic area of the complex plot is computed for every channel for both autistic and healthy subjects; and the obtained values are considered as the first set of features. Fourth, three additional features are computed for every channel: the average CWT, the maximum EEG amplitude, and the maximum real part of CWT. The classification of those features is realized through artificial neural network (ANN). The obtained accuracy, sensitivity and specificity values are: 95.9%, 96.7%, and 95.1% respectively. Show more

Keywords: Autism, EEG, CWT, Elliptic area, classification

DOI: 10.3233/JIFS-189176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8599-8607, 2020

Authors: Patel, Chintan | Joshi, Dhara | Doshi, Nishant | Veeramuthu, A. | Jhaveri, Rutvij

Article Type: Research Article

Abstract: With the agile development of the Internet era, starting from the message transmission to money transactions, everything is online now. Remote user authentication (RUA) is a mechanism in which a remote server verifies the user’s correctness over the shared or public channel. In this paper, we analyze an RUA scheme proposed by Chen for the multi-server environment and prove that their scheme is not secured. We also find numerous vulnerabilities such as password guessing attack, replay attack, Registration Center (RC) spoofing attack, session key verification attack, and perfect forward secrecy attack for Chen’s scheme. After performing the cryptanalysis of Chen’s …scheme, we propose a biometric-based RUA scheme for the same multi-server environment. We prove that the proposed authentication scheme achieves higher security than Chen’s scheme with the use of informal security analysis as well as formal security analysis. The formal security analysis of the proposed scheme is done using a widely adopted random oracle method. Show more

Keywords: Multi-server, smart card, biometrics, three factor authentication

DOI: 10.3233/JIFS-189177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8609-8620, 2020

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8621-8621, 2020

Authors: Lin, Tang

Article Type: Research Article

Abstract: Although much less fatal than the Ebola and previous SARS virus epidemics, the current coronavirus outbreak (COVID-19) has spread to more people in more countries in a much shorter time frame. With the rapid development of the Internet of things, it has played an important role to track/monitor transmission movements throughout the population. The technology infrastructure between mobile devices, wearable devices and sensors, smart home device makes it possible to readily deploy solutions to monitor and collect data and perform analysis to ensure policy make intelligent, rapid decisions. This research combines AOL and Support Vector Machine to form the Internet …of things cycle through smart home. The parameters of Support Vector Machine model are optimized by ALO algorithm, which shortens the learning time and improves the performance of classifier. Then, the algorithm of ALO is used to optimize the Support Vector Machine intrusion detection method and agent technology, and the intrusion detection model is established. Experimental results show that the combination of these two can effectively reduce the false alarm rate of network intrusion. Show more

Keywords: Support vector machine, intrusion detection, internet of things security, smart home

DOI: 10.3233/JIFS-189258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8623-8632, 2020

Authors: Hongwei, Zhu, | Xuesong, Wang,

Article Type: Research Article

Abstract: With the continuous progress of social science and technology, the development of the Internet of things is growing. With the development of Internet of things, security problems emerge in endlessly. During the period of COVID-19, the Internet of Things have been widely used to fight virus outbreak. However, the most serious security problem of the Internet of things is network intrusion. This paper proposes a balanced quadratic support vector machine information security analysis method for Internet of things. Compared with the traditional support vector machine Internet of things security analysis method, this method has a higher accuracy, and can shorten …the detection time, with efficient and powerful characteristics. The method proposed in this paper has certain reference value to the Internet of things network intrusion problem. It provides better security for the Internet of things during the protection period of covid-19. Show more

Keywords: SVM, Balanced binary decision, internet of things security, intrusion detection, COVID-19

DOI: 10.3233/JIFS-189259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8633-8642, 2020

Authors: Weining, Sun | Cheli, Zhao

Article Type: Research Article

Abstract: During COVID-19 pandemic, researchers have used innovative technologies for fast tracking the development to end this pandemic. Virtual Reality (VR) has offered an imperative role for fighting this pandemic, through audiovisual-based virtual communication. Virtual reality modeling language (VRML), as an international standard of virtual reality, has developed rapidly. VRML expanded the function of script node by introducing Java and script programs written in java script language. This paper presents a VRML method. Libraries and platoons are virtualized to meet the normal use of users. In principle, any text editing system can be used for VRML programming, but some editing systems …have few related functions and are not suitable for large-scale VRML Scene Design. The VRML algorithm proposed in this paper can be applied to large buildings. The VRML algorithm proposed in this paper is compared with the traditional algorithm. The VRML algorithm proposed in this paper is superior to the traditional algorithm in the aspects of realism, interactivity, design rationality and execution speed. The practicability of the VRML algorithm is proved. It provides help for people who are inconvenient to go out during the protection period of covid-19. Show more

Keywords: Virtual reality modeling language virtual library, virtual volleyball hall, 3D, Internet, system

DOI: 10.3233/JIFS-189260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8643-8653, 2020

Authors: Zhaoguo, Liu | Tingting, Liang | Wenzhan, Wang

Article Type: Research Article

Abstract: Under the influence of novel corona virus pneumonia epidemic, the protection of traditional villages is put forward higher request. The spread of the epidemic among villages will make the situation of epidemic prevention and control more difficult. As an important part of culture, traditional villages have high historical value. In this paper, the traditional village protection method, a new geographical data algorithm IData storage method. Compared with the traditional ArcGIS method, it improves the efficiency and accuracy of topographic map entry. IData’s data factory can use the symbolic technology of skeleton lines to represent all the figures in the national …standard mode, and any complex figure can only be represented by one element. Idate can quickly load data and render symbols in a drawing. With the powerful data processing engine of IData data factory, we can check out the errors that other software can’t find and process the data automatically. Records of the loss of traditional villages can be recorded quickly. The establishment and protection of traditional villages have had a beneficial impact. Show more

Keywords: Traditional village protection, big data, COVID-19, ArcGIS

DOI: 10.3233/JIFS-189261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8655-8664, 2020

Authors: Tingting, Liang | Zhaoguo, Liu | Wenzhan, Wang

Article Type: Research Article

Abstract: The Covid-19 first occurs in Wuhan, China in December 2019. After that, the virus has spread all over the world and at the time of writing this paper the total number of confirmed cases are above 11 million with over 600,000 deaths. The pattern recognition of complex environment can be used to determine if a COVID-19 breath pattern can be established with accuracy. The traditional decorative pattern detection method has a high degree of recognition in simple scene. However, the efficiency of decorative pattern detection in complex scenes is low and the recognition accuracy is not high. Firstly, the evaluation …index of target detection method is designed. Through this paper, it is found that the success rate of some targets is naturally better than other targets, and easy to distinguish from the background. In order to improve the recognition success rate of the object in the complex environment and determine the position and attitude of the object, the pattern as the artificial identification in the environment is proposed. The interior art decoration pattern is selected as the experimental pattern and the pattern classification evaluation index is designed. The experimental results show that the method proposed in this paper can optimize the pattern subsets which are confused with each other and easy to distinguish from the background. It has a certain reference value for decorative pattern recognition in complex environment for COVID-19 epidemic. Show more

Keywords: Neural network, pattern classification, decorative pattern detection, complex environment, COVID-19

DOI: 10.3233/JIFS-189262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8665-8673, 2020

Authors: Jihong, Yang | Lu, Yun

Article Type: Research Article

Abstract: Under the influence of novel corona virus pneumonia epidemic prevention and control, higher requirements for behavior recognition in complex environment are put forward. The accuracy of traditional methods for sports training is not high, so a method is needed to improve the local action recognition to assist sports training. In the process of behavior recognition, if only the track is regarded as an independent individual, the information of its neighbor will be ignored. Therefore, we use KNN algorithm to get the nearest neighbor trajectory. In order to calculate the rich neighborhood information around the track, this paper calculates the complex …relationship between the center track and the neighborhood track from four different angles, including absolute motion, relative motion, distance relationship and direction relationship. Then, from the four different perspectives of variance, discrete coefficient, skewness and kurtosis, this paper proposes a large interval nearest neighbor coding method. This method makes the four eigenvalues complement each other and improves the ability of describing complex and changeable behaviors. The experimental results show that the coding method proposed in this paper can be used for behavior recognition according to different transformation matrix. Show more

Keywords: Behavior recognition, track space-time feature, feature matching, fuzzy set

DOI: 10.3233/JIFS-189263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8675-8684, 2020