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

Authors: Anu Shalini, T. | Sri Revathi, B.

Article Type: Research Article

Abstract: This paper presents the design of a grid connected hybrid system using modified Z source converter, bidirectional converter and battery storage system. The input sources for the proposed system are fed from solar and wind power systems. A modified high gain switched Z source converter is designed for supplying constant DC power to the DC-link of the inverter. A hybrid deep learning (HDL) algorithm (CNN-BiLSTM) is proposed for predicting the output power from the hybrid systems. The HDL method and the PI controller generates pulses to the proposed system. The superiority of the proposed hybrid DL method is compared with …the conventional DL methods like CNN, LSTM, BiLSTM methods and the performance of the hybrid system is validated. A closed loop control framework is implemented for the proposed grid integrated hybrid system and its performance is observed by implementing the PI, Fuzzy and ANN controllers. A 1.5Kw hybrid system is designed in MATLAB/SIMULINK software and the results are validated. A prototype of the proposed system is developed in the laboratory and experimental results are obtained from it. From the simulation and experimental results, it is observed that the ANN controller with SVPWM (Space vector Pulse width Modulation) gives a THD (Total harmonic distortion) of 2.2% which is within the IEEE 519 standard. Therefore, from the results it is identified that the ANN-SVPWM method injects less harmonic currents into the grid than the other two controllers. Show more

Keywords: Power forecasting, timeseries forecasting, bidirectional long short-term memory, convolution neural network, renewable power generation

DOI: 10.3233/JIFS-220307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8247-8262, 2022

Authors: Radhakrishnan, C. | Asokan, R.

Article Type: Research Article

Abstract: To safeguard private information, image steganography is extensively used. Research is focused on ways to enhance steganographic technologies so that they may increase compression ratio while maintaining steganography image integrity. Because of its essential qualities such as security, scalability, and robustness, Steganography is a preferred way of communicating protected secret information to prevent hacking and misuse. This proposed research offers a steganography approach based on Enhanced Chaotic Particle Swarm Optimization (ECPSO), which uses chaos theory to determine the optimal pixel positions in the cover picture to hide confidential information when keeping the steganography quality in the images. Both the cover …and secret pictures are separated into blocks to increase hiding capacity, with each component storing a sufficient quantity of secret data by mapping the pixels. The suggested ECPSO-Stegano system has better results with the criteria of Mean Square Error (MSE) of 0.00018%, Peak-Signal-to-Noise-Ratio (PSNR) of 79.66%, Bit Error Rate (BER) of 0.45% in average, and Structural Similarity Index (SSI) of 0.98 in average for various input size. It’s also robust to statistical threats. Show more

Keywords: Chaos map, BET, Stego-image, blocks, optimal pixel, confidentiality

DOI: 10.3233/JIFS-221093

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8263-8273, 2022

Authors: Sammeta, Naresh | Parthiban, Latha

Article Type: Research Article

Abstract: In recent times, a number of Internet of Things (IoT) related healthcare applications have been deployed for automating healthcare services and offering easy accessibility to patients. Several issues like security, fault-tolerant, and reliability have restricted the utilization of IoT services in real-time healthcare environments. To achieve security, blockchain technology can be utilized which offers effective interoperability of healthcare databases, ease of medical data access, device tracking, prescription database, hospital assets, etc. Therefore, this paper presents an optimal Elliptic curve cryptography-based encryption algorithm for a blockchain-enabled medical image transmission model, named OECC-BMIT. The presented OECC-BMIT model involves different stages of operations …such as encryption, optimal key generation, blockchain-enabled data transmission, and decryption. Firstly, the OECC-BMIT model performs Elliptic curve cryptography (ECC) based encryption technique to securely transmit the medical images. In order to generate the optimal set of keys for the ECC technique, modified bat optimization (MBO) algorithm is applied. Then, the encrypted images undergo secure transmission via blockchain technology. The encrypted images are decrypted on the recipient side and the original medical image is reconstructed effectively. Extensive sets of experimentations were performed to highlight the goodness of the OECC-BMIT algorithm and the obtained results pointed out the improved outcome over the state of art methods in terms of different measures. Show more

Keywords: Blockchain, encryption, healthcare, medical images, optimal key generation

DOI: 10.3233/JIFS-211216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8275-8287, 2022

Authors: Ren, Yaxue | Wen, Yintang | Liu, Fucai | Zhang, Yuyan

Article Type: Research Article

Abstract: Chaotic systems are dynamic systems with aperiodic and pseudo-random properties, and systems in many fields exhibit chaotic time-series properties. Aiming at the fuzzy modeling problem of chaotic time series, this paper proposes a new fuzzy identification method considering the selection of important input variables. The purpose is to achieve higher model modeling and prediction accuracy by constructing a model with a simple structure. The relevant input variable was swiftly chosen in accordance with the input variable index after the Two Stage Fuzzy Curves method was used to determine the weight of the correlation between each input variable and the output …from a large number of selectable input variables. The center and width of the irregular Gaussian membership function were then optimized using the fuzzy C-means clustering algorithm and the particle swarm optimization technique, which led to the determination of the fuzzy model’s underlying premise parameters. Finally, the fuzzy model’s conclusion parameters were determined using the recursive least squares method. This model is used to simulate three chaotic time series, and the outcomes of the simulation are contrasted and examined. The outcomes demonstrate that the fuzzy identification system has higher prediction accuracy based on a simpler structure, demonstrating its validity. Show more

Keywords: Fuzzy identification, input variable selection, chaotic time series, fuzzy c-means algorithm, irregular gaussian function

DOI: 10.3233/JIFS-212527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8289-8301, 2022

Authors: Alshareef, Esam Alsadiq | Ebrahim, Fawzi Omar | Lamami, Yosra | Milad, Mohamed Burid | Eswani, Mohamed S.A. | Bashir, Sedigh Abdalla | Bshina, Salah A.M. | Jakdoum, Anas | Abourqeeqah, Asharaf | Elbasir, Mohamed O | Elbahrit, Ellafi.A.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8303-8313, 2022

Authors: Harikumar, Sandhya | Sathyajit, Rohit | Karumudi, Gnana Venkata Naga Sai Kalyan

Article Type: Research Article

Abstract: News feeds generate colossal amount of data consisting of important information hidden in the intricacies. State of the art methods are still at infancy in providing a very generic and publicly available solution to skim through the important information in the news from various sources and an ability to search using specific keywords in different languages. This paper focuses on designing a tool to extract semantic details from news articles published through various internet sources in various languages. The semantic information is stored within DBMS for ease of organizing and retrieving the data. Further, a querying facility to search through …entire articles based on the keyword or date-based search is also proposed to view the crisp content. The news articles in English, and two Indian languages - Hindi and Malayalam are considered for experimentation. The proposed strategy consists of two main components namely, Generative model creation and Query engine. Generative model aims to extract important entities and keywords along with their relevance to the article and other similar articles using Latent Dirichlet Allocation(LDA) and Named Entity Recognition(NER). Query engine is to facilitate on the fly retrieval of semantic content from the database, based on user keyword. The search engine, along with database indexing, reduces the access time to the database thereby retrieving the information in less time. Experimental results show that the proposed method is effective in terms of quality of information and time consumed for information retrieval. Show more

Keywords: News analytics, multilingual, natural language processing(NLP), Latent dirichlet allocation(LDA), semantic information retrieval

DOI: 10.3233/JIFS-221184

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8315-8327, 2022

Authors: Senthamizh Selvi, S. | Anitha, R.

Article Type: Research Article

Abstract: In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part-Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large …volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English-Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving one-time occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil. Show more

Keywords: Natural language processing, part-of-speech tagger, sandhi, bilingual parallel corpus, cross-lingual transformation learning

DOI: 10.3233/JIFS-221278

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8329-8348, 2022

Authors: Rajesh, D. | Kiruba, D. Giji

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8349-8357, 2022

Authors: Gopinath, N. | Prayla Shyry, S.

Article Type: Research Article

Abstract: As technology advances, it becomes easier to share large amounts of data over the internet. Cloud computing is one of the technologies that allows for easy data sharing over the internet. It is critical to provide security for this data when they are being shared across the internet. The security of data saved in cloud storage, as well as data transport and transmitting a key required to encrypt data between two parties, has been a source of concern for the industry, as a result of the growing use of cloud services in recent years. Collective attacks are significantly more powerful …than individual strikes, according to our research. Despite the fact that additional research works were studied in the previous literature review, there are some study concerns for not correcting third-party data hacking. Therefore, this paper focuses on the design of Secured Quantum Key Distribution (SQKD) with Fuzzy logic to improve the security of the shared key. Quantum Key Distribution, Post Quantum Key Distribution, and the EPR Proto-col are technologies that increase the security of data sharing. We have incorporated the Secured Quantum Key Distribution (SQKD) with Fuzzy logic in our proposed work to improve the security of the shared key. The proposed systems include some additional characteristics in addition to the existing approaches. The proposed model uses shifting algorithms and the fuzzification procedure to assure the security of the secret key in the Fuzzification of Quantum Key approach. The experimental results states that the mean value of security losses in SFQ is 1.8306051, and the mean value of QKD is 14.6448416, with standard deviations of 1.7329 and 13.863 for SFQ and QKD, respectively. Show more

Keywords: Quantum key distribution, fuzzy logic, SQKD, Q-bits and quantum cryptography

DOI: 10.3233/JIFS-220398

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8359-8369, 2022

Authors: Wei-Jie, Lucas Chong | Chong, Siew-Chin | Ong, Thian-Song

Article Type: Research Article

Abstract: Masked face recognition embarks the interest among the researchers to find a better algorithm to improve the performance of face recognition applications, especially in the Covid-19 pandemic lately. This paper introduces a proposed masked face recognition method known as Principal Random Forest Convolutional Neural Network (PRFCNN). This method utilizes the strengths of Principal Component Analysis (PCA) with the combination of Random Forest algorithm in Convolution Neural Network to pre-train the masked face features. PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and …LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods. Show more

Keywords: Covid-19, PRFCNN, random forest, principal component analysis, convolutional neural network

DOI: 10.3233/JIFS-220667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8371-8383, 2022