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

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