Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Sermakani, A.M. | Paulraj, D.

Article Type: Research Article

Abstract: The contemporary development of cloud is a next generation federated cloud technology envisioned by virtualization to enable cost-efficient usage of computing resources. The resources are intended on scalability as data grows enormously with on demand services. Federated cloud is an efficient networked computing environment that can adopt infrastructure which aims for virtual unlimited pool during on demand services. The challenging task for federated cloud includes managing workloads of individual cloud, progressing virtual machine volumes, cost utilization, fair load distribution. In order to addresses these challenges, this approach uses “Optimized Bit Matrix based Node Acquisition for Federated cloud (BMNF)”. The framework …process two different approaches: managing bit matrix and fulfilling load distribution in federated cloud based on cost aware workloads. The formation of bit matrix designed by each member in cloud services that validates load availability status. Load distribution factor concentrates on fair allocation with cost aware policy at individual level. BMNF policy segregates the request among various clouds by analyzing bits patterns. In addition to load distribution using bit matrix, it also focuses on improving cost utilization and targets with better quality of load distribution. The proposed working model is highly efficient with computation and communication overhead for federated cloud. Show more

Keywords: Cloud computing, load distribution, virtualization, federated cloud, virtual machine allocation, quality of service

DOI: 10.3233/JIFS-232897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11613-11627, 2023

Authors: Minh, K.D. | Nguyen, X.H. | Nguyen, V.P.

Article Type: Research Article

Abstract: With the rapid expansion of artificial intelligence (AI) and machine learning, the evaluation of AI cloud platforms has become a critical research topic. Given the availability of many platforms, selecting the best AI cloud services that can satisfy the requirements and budget of an organization is crucial. Several solutions, each with its advantages and disadvantages, are available. In this study, a combinative-distance-based assessment approach was proposed in probabilistic linguistic hesitant fuzzy sets (PLHFSs) to accommodate the multiple characteristics of group decision-making. The original data were normalized using a standardized process that integrated numerous methodologies. Furthermore, under PLHFSs, the statistical variance …approach was used to generate the weighted objective of the vector of assessment criteria. Finally, an AI cloud platform evaluation and comparison analysis case study was used to validate the feasibility of this method. Show more

Keywords: Combinative-distance-based assessment (CODAS) method, probabilistic linguistic hesitant fuzzy sets (PLHFSs), AI cloud platform evaluation, multiple attribute decision-making (MADM), fuzzy environments

DOI: 10.3233/JIFS-232546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11629-11646, 2023

Authors: Wang, Xin-Fan | Zhang, Li-Na | Zhou, Huan | Wang, Xue-Bin

Article Type: Research Article

Abstract: The intuitionistic uncertain linguistic information aggregation problems considering different priority levels of criteria are investigated. Firstly, we extended the prioritized averaging (PA) operator to intuitionistic uncertain linguistic environment, defined two new prioritized aggregation operators called the intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator and the intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator, and established various desirable properties of the proposed operators. Secondly, we developed a multi-criteria decision making (MCDM) approach based on the IULPWA operator (or the IULPWG operator) to deal with the MCDM problems in which the criterion values take the form of intuitionistic uncertain linguistic numbers …(IULNs) and the criteria are in different priority levels. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method, and a comparison analysis is conducted to make clear the differences among the IULPWA operator, the IULPWG operator, the intuitionistic uncertain linguistic number weighted averaging (IULNWA) operator and the intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator. Show more

Keywords: Multi-criteria decision making (MCDM), intuitionistic uncertain linguistic number (IULN), intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator, intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator

DOI: 10.3233/JIFS-223829

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11647-11661, 2023

Authors: Hajmirfattahtabrizi, Mahboobehalsadat | Feylizadeh, Mohammad Reza | Song, Huaming

Article Type: Research Article

Abstract: In the past two years, 2020-2022, the developing construction industry has been a huge issue according to the negative effect of Covid-19 with the increasing pandemic situation in cities and areas. In Covid-19 pandemic situation, the cement manufacturing industry has been crucial and needed more scrutiny. As cement is the second significant component after water in concrete and construction industry. Meanwhile, locating a cement plant in a special area of the city is challenging and affecting more by local communities and other involved environmental factors. The location selection decisions need to grow by environmental, economic, technical and social attributes. This …study aims to present the site suitability decisions through a case study of locating a new manufacturing plant for cement production in Tehran surrounding, Iran. In this process, some required technical and tactical criteria are deserved for evaluating and suitability of the plant through decision-makers for cement manufacturing. All the feasible industrial alternative locations were evaluated under various criteria and regarding the Covid-19 pandemic’s negative impact to identify the most appropriate location for the cement industry. The authors proposed two Multi-Criteria Decision Attributes (MCDA) methods of MacBeth and COmplex PRoportional ASsessment (COPRAS) to evaluate and select the most suitable location for site suitability of the cement plant in this problem. Though the MacBeth method does not need to calculate weights of the Geographical Information System (GIS) criteria, the COPRAS method determined and used BWM (Best-Worst Method) as the weighing method. In sum, the comparison of the two methods was obtained according to the given results and ranks of volunteer cement suppliers for site suitability of the cement plant. Show more

Keywords: Cement plant, site suitability, GIS, BWM, COPRAS, entropy, MacBeth

DOI: 10.3233/JIFS-224534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11663-11678, 2023

Authors: Prabakaran, G. | Jayanthi, K.

Article Type: Research Article

Abstract: Coronavirus 2019 (COVID-19) is a severe disease in respiratory syndrome. Early identification and efficient treatment of COVID-19 are not presented which provides ineffective treatment. This research develops an efficient system for early detection and segmentation of COVID-19 severity with the consideration of CT images. To overcome the abovementioned drawbacks, we develop the optimized Mask R-CNN method to train and test the dataset to classify and segment the COVID-19 disease. The proposed technique contains three phases which are, pre-processing, segmentation, and severity analysis. Initially, the patient’s CT images are collected from a different clinic. Then, the noise present in the images …is detached with a Gaussian filter. Then, the pre-processed images are given to the optimized mask region-based convolution neural network (OMRCNN) classifier to detect, classify and segment the image. After segmentation, the severity of the disease is examined. To enhance the performance of the mask RCNN classifier, the parameter is efficiently chosen by using the adaptive red deer algorithm. In the adaptive red deer algorithm, the levy flight is utilized to enhance the updating process. The performance of the proposed technique is analyzed based on various metrics. Show more

Keywords: COVID-19 segmentation, detection, recurrent neural network, gaussian filter adaptive red deer algorithm, and severity analysis

DOI: 10.3233/JIFS-230312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11679-11693, 2023

Authors: Gulistan, Muhammad | Pedrycz, Witold | Yaqoob, Naveed

Article Type: Research Article

Abstract: We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization …of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios. Show more

Keywords: q-fractional fuzzy sets, fuzzy set, interval-valued fuzzy sets, type 2 fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, q-rung orthopair fuzzy sets, linear diophantine fuzzy sets, switching techniques, uncertainty, imprecision

DOI: 10.3233/JIFS-233563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11695-11706, 2023

Authors: Ponniah, Krishna Kumar | Retnaswamy, Bharathi

Article Type: Research Article

Abstract: The Internet of Things (IoT) integrated Cloud (IoT-Cloud) has gotten much attention in the past decade. This technology’s rapid growth makes it even more critical. As a result, it has become critical to protect data from attackers to maintain its integrity, confidentiality, protection, privacy, and the procedures required to handle it. Existing methods for detecting network anomalies are typically based on traditional machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and so on. Although these methods can produce some outstanding results, they have low accuracy and rely heavily on manual traffic feature design, which has …become obsolete in the age of big data. To overcome such drawbacks in intrusion detection (ID), this paper proposes a new deep learning (DL) model namely Morlet Wavelet Kernel Function included Long Short-Term Memory (MWKF-LSTM), to recognize the intrusions in the IoT-Cloud environment. Initially, to maintain a user’s privacy in the network, the SHA-512 hashing mechanism incorporated a blockchain authentication (SHABA) model is developed that checks the authenticity of every device/user in the network for data uploading in the cloud. After successful authentication, the data is transmitted to the cloud through various gateways. Then the intrusion detection system (IDS) using MWKF-LSTM is implemented to identify the type of intrusions present in the received IoT data. The MWKF-LSTM classifier comes up with the Differential Evaluation based Dragonfly Algorithm (DEDFA) optimal feature selection (FS) model for increasing the performance of the classification. After ID, the non-attacked data is encrypted and stored in the cloud securely utilizing Enhanced Elliptical Curve Cryptography (E2 CC) mechanism. Finally, in the data retrieval phase, the user’s authentication is again checked to ensure user privacy and prevent the encrypted data in the cloud from intruders. Simulations and statistical analysis are performed, and the outcomes prove the superior performance of the presented approach over existing models. Show more

Keywords: Internet of Things (IoT), deep learning, cloud computing, data security, IoT authentication, intrusion detection system, Elliptical Curve Cryptography

DOI: 10.3233/JIFS-221873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11707-11724, 2023

Authors: Nunsanga, Morrel V.L. | Pakray, Partha | Devi, Toijam Sonalika | Singh, L. Lolit Kr

Article Type: Research Article

Abstract: The process of associating words with their relevant parts of speech is known as part-of-speech (POS) tagging. It takes a substantial amount of well-organized data or corpora and significant target language research to obtain good performance for a tagger. Mizo is a language that needs more research attention in computational linguistics due to its under-resourced nature. The limited availability of corpora and relevant literature adds complexity to the task of assigning POS labels to Mizo text. This paper explores two methods to potentially improve the Hidden Markov Model (HMM)-based POS tagger for the Mizo language. The proposed taggers are compared …with the baseline HMM tagger and the N-gram taggers on the designed Mizo corpus, which consists of 72,077 manually tagged tokens. The experimental results proved that the two proposed taggers enhanced the HMM-based Mizo POS tagger, achieving 81.52% and 84.29% accuracy, respectively. Moreover, a comprehensive analysis of the performance of the suggested hybrid tagger was conducted, yielding a weighted average precision, recall, and F1-score of 83.09%, 77.88%, and 79.64% respectively. Show more

Keywords: Hybrid POS tagger, rule-based POS tagger, N-gram tagger, Mizo POS tagger, Hidden Markov Model

DOI: 10.3233/JIFS-224220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11725-11736, 2023

Authors: Srihari, Pasala | Harikiran, Jonnadula | Sai Chandana, B. | Surendra Reddy, Vinta

Article Type: Research Article

Abstract: Recognizing human activity is the process of using sensors and algorithms to identify and classify human actions based on the data collected. Human activity recognition in visible images can be challenging due to several factors of the lighting conditions can affect the quality of images and, consequently, the accuracy of activity recognition. Low lighting, for example, can make it difficult to distinguish between different activities. Thermal cameras have been utilized in earlier investigations to identify this issue. To solve this issue, we propose a novel deep learning (DL) technique for predicting and classifying human actions. In this paper, initially, to …remove the noise from the given input thermal images using the mean filter method and then normalize the images using with min-max normalization method. After that, utilizing Deep Recurrent Convolutional Neural Network (DRCNN) technique to segment the human from thermal images and then retrieve the features from the segmented image So, here we choose a fully connected layer of DRCNN as the segmentation layer is utilized for segmentation, and then the multi-scale convolutional neural network layer of DRCNN is used to extract the features from segmented images to detect human actions. To recognize human actions in thermal pictures, the DenseNet-169 approach is utilized. Finally, the CapsNet technique is used to classify the human action types with Elephant Herding Optimization (EHO) algorithm for better classification. In this experiment, we select two thermal datasets the LTIR dataset and IITR-IAR dataset for good performance with accuracy, precision, recall, and f1-score parameters. The proposed approach outperforms “state-of-the-art” methods for action detection on thermal images and categorizes the items. Show more

Keywords: Human action recognition, deep recurrent convolutional neural network, thermal images, classification, CapsNet, feature extraction, DenseNet-169

DOI: 10.3233/JIFS-230505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11737-11755, 2023

Authors: Xiao, Jian | Meng, Linglong | Wu, Kaiyin

Article Type: Research Article

Abstract: A supplier portrait generation method based on Big data analysis and deep learning was proposed to help users make reasonable decisions in core links such as procurement and contract signing. This method establishes a label element analysis model for each level in the vertical label system of power supply enterprises, and divides it into target layer, standard layer, and solution layer based on the logic and attributes of the elements, and establishes a hierarchical structure. Compare the index labels of each level with the labels of the upper and lower levels by considering the logical relationship and correlation between each …level. Utilize deep learning algorithms to sort hierarchically, and use a multidimensional structural model to represent and fuse portrait labels of power supply enterprises. Based on the imaging results of supplier vertical rating, combined with objective factors such as material production cycle, supply cycle, market supply and demand, price fluctuations, etc., it helps power enterprises effectively predict the supplier’s performance ability. The simulation results show that the reliability of the power supply enterprise portrait generated by this method is high, and the credibility of the portrait identification system for all levels of power supply enterprises is high. This supplier portrait method can effectively improve the supplier management capabilities of power enterprises. Show more

Keywords: Deep learning BCCM, multi-aspect, electricity supplier, portrait generation, information management

DOI: 10.3233/JIFS-230722

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11757-11767, 2023