Journal of Intelligent & Fuzzy Systems - Volume 41, issue 5

Authors: Jithendra, K.B. | Kassim, Shahana T.

Article Type: Research Article

Abstract: Security of a recently proposed bitwise block cipher GIFT is evaluated in this paper. In order to mount full round attacks on the cipher, biclique cryptanalysis method is applied. Both variants of the block cipher are attacked using Independent biclique approach. For recovering the secret keys of GIFT-64, the proposed attack requires 2127.45 full GIFT-64 encryption and 28 chosen plain texts. For recovering the secret keys of GIFT-128, the proposed attack requires 2127.82 full GIFT-128 encryption and 218 chosen plain texts. The attack complexity is compared with that of other attacks proposed previously. The security level …of GIFT is also compared with that of the parent block cipher PRESENT, based on the analysis. Show more

Keywords: Block cipher, cryptanalysis, biclique, complexity

DOI: 10.3233/JIFS-189875

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5551-5560, 2021

Authors: Tripathi, Diwakar | Ramachandra Reddy, B. | Padmanabha Reddy, Y.C.A. | Shukla, Alok Kumar | Kumar, Ravi Kant | Sharma, Neeraj Kumar

Article Type: Research Article

Abstract: Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants’ credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive …performance of credit scoring model. Towards to feature selection, Binary BAT optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with “Radial Basis Function Neural Network (RBFN)”, “Support Vector Machine (SVM)” and “Random Forest (RF)” for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring. Show more

Keywords: BAT algorithm, credit score, feature selection

DOI: 10.3233/JIFS-189876

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5561-5570, 2021

Authors: Biswas, Kusan

Article Type: Research Article

Abstract: In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems  (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy …channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature. Show more

Keywords: Data hiding, JPEG, ECC, SVD, Turbo codes, PSNR, SSIM

DOI: 10.3233/JIFS-189877

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5571-5581, 2021

Authors: Anushiadevi, R. | Amirtharajan, Rengarajan

Article Type: Research Article

Abstract: Reversible Data Hiding (RDH) schemes have recently gained much interest in protecting the secret information and sensitive cover images. For cloud security applications, the third party’s data embedding can be done (e.g., cloud service). In such a scenario, to protect the cover image from unauthorized access, it is essential to encrypt before embedding it. It can be overcome by combining the RDH scheme with encryption. However, the key challenge in integrating RDH with encryption is that the correlation between adjacent pixels begins to disappear after encryption, so reversibility cannot be accomplished. RDH with elliptic curve cryptography is proposed to overcome …this challenge. In this paper (ECC-RDH) by adopting additive homomorphism property; the proposed method, the stego image decryption gives the sum of the original image and confidential data. The significant advantages of this method are, the cover image is transferred with high security, the embedding capacity is 0.5 bpp with a smaller location map size of 0.05 bpp. The recovered image and secrets are the same as in the original, and thus 100% reversibility is proved. Show more

Keywords: Elliptic curve cryptography, reversible data hiding, additive homomorphism, lossless data hiding, reversible steganography

DOI: 10.3233/JIFS-189878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5583-5594, 2021

Authors: Sarraf, Gaurav | Srivatsa, Anirudh Ramesh | Swetha, MS

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-211111 .

DOI: 10.3233/JIFS-189879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5595-5606, 2021

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Venu, Sivakumar

Article Type: Research Article

Abstract: Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling …algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms. Show more

Keywords: Cloud computing, task scheduling, Qos aware task scheduling, cost enabled scheduling, cloud utilization, cloud utility

DOI: 10.3233/JIFS-189881

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5607-5615, 2021

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Ramasubramanian Gobichettipalayam, Kanagachidambaresan

Article Type: Research Article

Abstract: A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. …Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement. Show more

Keywords: Service level agreement, broker-based negotiation framework, game theory decision system, E-commerce application

DOI: 10.3233/JIFS-189882

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5617-5628, 2021

Authors: Sujatha, M. | Geetha, K. | Balakrishnan, P.

Article Type: Research Article

Abstract: The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference …system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model. Show more

Keywords: Cloud computing, IaaS selection, Sugeno Fuzzy inference system, CSP selection, compute service, MCDM

DOI: 10.3233/JIFS-189883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5629-5637, 2021

Authors: Dat, Nguyen Quang | Ngoc Anh, Nguyen Thi | Nhat Anh, Nguyen | Solanki, Vijender Kumar

Article Type: Research Article

Abstract: Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle …seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series. Show more

Keywords: Hybrid online, RNN online, multi time series, multi seasonal decompose, Electricity forecasting

DOI: 10.3233/JIFS-189884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5639-5652, 2021

Authors: Kumar, Manish | Kumar, Bhavnesh | Rani, Asha

Article Type: Research Article

Abstract: The primary objective of this work is to optimize the induction motor rotor flux so that maximum efficiency is attained in the facets of parameter and load variations. The conventional approaches based on loss model are sensitive to modelling accuracy and parameter variations. The problem is further aggravated due to nonlinear motor parameters in different speed regions. Therefore, this work introduces an adaptive neuro-fuzzy inference system-based rotor flux estimator for electric vehicle. The proposed estimator is an amalgamation of fuzzy inference system and artificial neural network, in which fuzzy inference system is designed using artificial neural network. The training data …for neuro-fuzzy estimator is generated offline by acquiring rotor flux for different values of torque. The conventional fuzzy logic and differential calculation methods are also developed for comparative analysis. The efficacy of developed system is established by analyzing it under varying load conditions. It is revealed from the results that suggested methodology provides an improved efficiency i.e. 94.51% in comparison to 82.68% for constant flux operation. Show more

Keywords: Loss minimization, torque estimation, adaptive neuro fuzzy inference system (ANFIS), electrical vehicle (EV)

DOI: 10.3233/JIFS-189885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5653-5663, 2021