Journal of Intelligent & Fuzzy Systems - Volume 38, issue 5

Authors: Shakir, Hina | Rasheed, Haroon | Rasool Khan, Tariq Mairaj

Article Type: Research Article

Abstract:  Machine learning methods with quantitative imaging features integration have recently gained a lot of attention for lung nodule classification. However, there is a dearth of studies in the literature on effective features ranking methods for classification purpose. Moreover, optimal number of features required for the classification task also needs to be evaluated. In this study, we investigate the impact of supervised and unsupervised feature selection techniques on machine learning methods for nodule classification in Computed Tomography (CT) images. The research work explores the classification performance of Naive Bayes and Support Vector Machine(SVM) when trained with 2, 4, 8, 12, 16 …and 20 highly ranked features from supervised and unsupervised ranking approaches. The best classification results were achieved using SVM trained with 8 radiomic features selected from supervised feature ranking methods and the accuracy was 100%. The study further revealed that very good nodule classification can be achieved by training any of the SVM or Naive Bayes with a fewer radiomic features. A periodic increment in the number of radiomic features from 2 to 20 did not improve the classification results whether the selection was made using supervised or unsupervised ranking approaches. Show more

Keywords: Quantitative imaging features, radiomic features, nodule classification, machine learning, feature selection algorithms

DOI: 10.3233/JIFS-179672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5847-5855, 2020

Authors: Ashraf, Muhammad Waseem | Manzoor, Saher | Shahzad Sarfraz, Muhammad | Wasim, Muhammad Faisal | Ali, Basit | Akhlaq, Maham | Rujita, Ciurea | Popa, Alexandru

Article Type: Research Article

Abstract: Nano porous anodized aluminum oxide is fabricated in acidic electrolyte using two step anodization process with varied potential and etching time. Pore diameter of the fabricated membrane increases with increasing the voltage and time of etching. The rate of pore opening of the membrane is established and optimized. Morphology of the membrane is studied by SEM micrographs and quantitative analysis is done by EDX. The pore size was in the range of 85–140 nm. Also the simulation and analysis for varied parameters is done using Fuzzy Logic Controller and it was observed that the simulated and value of pore diameter calculated …using Mamdani’s model are approximately equal with minute percentage error. The AAO membrane have potential applications in biotechnology. Show more

Keywords: AAO, fuzzy analysis, pore diameter, etching time, voltage

DOI: 10.3233/JIFS-179673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5857-5864, 2020

Authors: Sarwar, Ghulam | Ashraf, Muhammad Waseem

Article Type: Research Article

Abstract: Fuzzy logic has been considered as a viable method for every field of study because of its large number of applications and advantages. In nano-materials, structural knowledge of material and parametric estimation can be studied using fuzzy logic. The main objective of this work is to perform fuzzy logic analyis for parametric estimation of Zinc oxide (ZnO) nano-rods based structures. The zinc oxide nano-rods when prepared with doped metal that results in change in properties of Zinc oxide nano-rods. These properties include structural, optical, mechanical and electrical properties of ZnO nano-rods. The enhancement in properties make the doped ZnO based …material suitable for energy harvesting, bio-medical, energy and electronics application. Literature depicts the effect of 2nd group elements on ZnO nano-particles which directly shows the effect of change of parameters due to change in doping concentration. In this work, the analysis of effect on bandgap and rod diameter due to change in doping concentration and synthesis time is performed on ZnO nano-rods with doping 2nd group elements. The authors concluded that the synthesis time increase the rod diameter which directly decreases the bandgap. However, the doping concentration of 2nd group elements results in increase in band gap and decrease in rod diameter. However this effect is negligible for Mg and Be due to there small atomic size. The comparison between fuzzy logic simulation and mamdani model were also analysed which shows an error of less than 1% between the value. The 2nd group doped ZnO nano-rods can be used for various application due to adjustable band-gap and rod diameter with change in doping concentration. Show more

Keywords: Zinc oxide, fuzzy analysis, magnesium, calcium, beryllium, strontium

DOI: 10.3233/JIFS-179674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5865-5875, 2020

Authors: Sodhar, Irum Naz | Jalbani, Akhtar Hussain | Buller, Abdul Hafeez | Channa, Muhammad Ibrahim | Hakro, Dil Nawaz

Article Type: Research Article

Abstract: Sentiment Analysis have also an important role in natural language processing to evaluate and analyzing the public opinion, sentiments and views about social activities such as product, services, Academic institutes, organizations etc. Lot of work has been done on English language in natural language processing. However, it is found out from the literature that still huge research gap is available for the Romanized Sindhi and there sentiment analysis in the field of natural language processing and also no any trained data is available for the testing. Classification of sentiment of Romanized Sindhi text is very difficult task. For the evaluation …of sentiment of Romanized Sindhi text easily available online Python tool were used. In this research work thousand words of Romanized Sindhi text/data were used for the sentiment classification. Also discussed issues in sentiment classification in Python tool on Romanized Sindhi text. Show more

Keywords: Sentiment analysis, natural language processing (NLP), dataset, Romanized Sindhi, Python

DOI: 10.3233/JIFS-179675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5877-5883, 2020

Authors: Tayyaba, Shahzadi | Ashraf, Muhammad Waseem | Tariq, Muhamamd Imran | Nazir, Mohsin | Afzulpurkar, Nitin | Balas, Marius M. | Mihalache, Sanda Florentina

Article Type: Research Article

Abstract: Computers have been used in different areas of medical technology and applications. Innovation in the field of tehnology has been considered as a fundamental consitutent of medical discipline. Advancement in the field of medical and health care results in an ease for disease diagnostic, reduction in risk of diseases and lessen pain which is eventually beneficial to human life. In this work, the designing and effect of skin puncturing of micro-needle was studied. The simulations for skin puncturing was performed in ANSYS and MATLAB fuzzy logic tool. The skin puncturing using needle based on human skin coatings including dermis, stratum …corneum and viable epidermis was studied. Fuzzy logic analysis was use to study the effect of effect of applied stress and tip diameter of the needle on the three layers of skin. A 3D model of human skin layer and needle was created in ANSYS and studied for an applied force of 0.4 to 0.9 N. Thinner the tip diameter of the needle, more penetration and puncturing of skin will occur. Similarly, for applied skin, more stress is required for proper puncturing of stratum corneum layer of human skin. The microfluidic analysis performed in the CFX environment of ANSYS shows that at the driving pressure of 140 kPa, 415μ L/min flow rate has been achieved. Show more

Keywords: Fuzzy logic, ANSYS, microneedle, skin insertion

DOI: 10.3233/JIFS-179676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5885-5895, 2020

Authors: Duddu, Vasisht | Rajesh Pillai, N. | Rao, D. Vijay | Balas, Valentina E.

Article Type: Research Article

Abstract:  Applications using Artificial Intelligence techniques demand a thorough assessment of different aspects of trust, namely, data and model privacy, reliability, robustness against adversarial attacks, fairness, and interpretability. While each of these aspects has been extensively studied in isolation, an understanding of the trade-offs between different aspects of trust is lacking. In this work, the trade-off between fault tolerance, privacy, and adversarial robustness is evaluated for Deep Neural Networks, by considering two adversarial settings under security and a privacy threat model. Specifically, this work studies the impact of training the model with input noise (Adversarial Robustness) and gradient noise (Differential Privacy) …on Neural Network’s fault tolerance. While adding noise to inputs, gradients or weights enhances fault tolerance, it is observed that adversarial robustness lowers fault tolerance due to increased overfitting. On the other hand, (ε dp , δ dp )-Differentially Private models enhance the fault tolerance, measured using generalisation error, which theoretically has an upper bound of e ε dp  - 1 + δ dp . This novel study of the trade-offs between different aspects of trust is pivotal for training trustworthy Machine Learning models. Show more

Keywords: Trustworthy machine learning, differential privacy, fault tolerance, adversarial robustness, deep learning

DOI: 10.3233/JIFS-179677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5897-5907, 2020

Authors: Roy, Sanjiban Sekhar | Paraschiv, Nicolae | Popa, Mihaela | Lile, Ramona | Naktode, Ishan

Article Type: Research Article

Abstract: Air pollution is one of the major environmental concerns in recent time. The majority of the population in the developed world live in urban area, hence air pollution concern is even more in cities. The worst gaseous pollutants are Caron Monoxide (CO), Nitrogen dioxide (NO2) and OZONE (O3). In this paper, we propose two predictive models for estimation of concentration of gases in the air, namely Carbon Monoxide (CO), Nitrogen dioxide (NO2) and OZONE (O3). The first proposed model is a combination of linear regression and Genetic Algorithm (GA). The second proposed model estimates concentration of gasses using Multivariable Polynomial …Regression. First model uses a linear regression for prediction of concentration of gases, whereby errors like MAPE, R2 obtained by linear regression are optimized using a genetic algorithm (GA). Multivariable Polynomial Regression is adopted as a second proposed method for the prediction of concentration of same gases. A detailed comparative study has been carried out on the performances of GA and Multivariable Polynomial Regression. In addition, predictive equations are formed for CO, O3 and NO2 based on temperature, relative humidity, benzene and Nox (oxides of nitrogen). Show more

Keywords: Concentration of gases, genetic algorithm, polynomial regression, air quality

DOI: 10.3233/JIFS-179678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5909-5919, 2020

Authors: Mundra, Ankit | Mundra, Shikha | Srivastava, Jai Shanker | Gupta, Punit

Article Type: Research Article

Abstract: Cryptography is the study of techniques which used to transforms the original text (plain text) to cipher text (non understandable text). Due to recent progress on digitized data exchange in electronic way, information security has become crucial in data storage and transmission. Some of the cryptographic algorithm has provided a promising solution which not only protects the data but also authenticates the systems and its participants, so the threat of various attacks is minimized. Nonetheless in the advancement of computing resources the cryptanalysis techniques also emerged and performing competitively in the field of information security with good results. In this …paper, we have proposed the optimized deep neural network approach for cryptanalysis of symmetric encryption algorithm 64-bit DES (Data encryption standard). Our approach has used back propagation technique with multiple hidden layers and advanced activation function also we have addressed the problem of vanishing gradient. Further, the implementation results show that we have achieved 90% accuracy which is significantly higher as compared to previous approaches. We have also compared the proposed technique with the existing ones against three parameters i.e. time, loss, accuracy. Show more

Keywords: Cryptography, encryption, decryption, plaintext, cipher text, DES

DOI: 10.3233/JIFS-179679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5921-5931, 2020

Authors: Tariq, Muhammad Imran | Tayyaba, Shahzadi | Ali Mian, Natash | Sarfraz, Muhammad Shahzad | Hussain, Akhtar | Imran, Muhammad | Pricop, Emil | Cangea, Otilia | Paraschiv, Nicolae

Article Type: Research Article

Abstract: Fuzzy logic has wide adoption in every field of study due to its immense advantages and techniques. In cloud computing, there are many challenges that can be resolved with the help of fuzzy logic. The core objective of this paper is to analyze the application of fuzzy logic in most demanding research areas of cloud computing. We also analyzed the fuzzy methods that were used in the solving of problems relates to cloud computing. A systematic literature review was conducted to enlist the all the challenging areas of research relates to cloud computing, categorized the most critical and challenging areas …of cloud research, studied existing problem-solving techniques of each challenging cloud area, and finally studied the application of fuzzy logic in each aforementioned areas to redress different problems. The authors concluded that fuzzy logic can be used in every area of research including cloud computing to solve the problems and optimized the performance, as well as fuzzy logic techniques, were opted by many cloud computing researchers to conduct their study to optimize the performance of the system. Show more

Keywords: Cloud computing, fuzzy logic, scheduling algorithms, mobile cloud computing, fuzzy logic applications

DOI: 10.3233/JIFS-179680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5933-5947, 2020

Authors: Mundra, Ankit | Mundra, Shikha | Verma, Vivek Kumar | Srivastava, Jai Shankar

Article Type: Research Article

Abstract: Stock market analysis or stock price prediction is aimed at predicting firm’s profitability based on current as well as historical data. From recent studies it is observed that machine learning approaches have outperformed traditional statistical methods in predictive analysis task. In our work we have analyzed time series data as prediction of stock price depends on historical variation in prices of stocks. To enhance the prediction accuracy, we have proposed a hybrid approach which is based on the concept of support vector machines (SVM) and Long Short-Term Memory (LSTM) as these algorithms are performing better in time series problem. On …applying proposed approach onto the TATA Global Beverages stock dataset, we have observed prediction accuracy of ninety seven percent which is outperforming, along with this to enhance the performance author have presented some observation like relative importance of the input financial variables and differences of determining factors in market comparative predictive analysis onto the experimentation dataset. Show more

Keywords: SVM, LSTM, back propagation, RNN, machine learning

DOI: 10.3233/JIFS-179681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5949-5956, 2020

Authors: Touqeer, Muhammad | Jabeen, Salma | Irfan, Rida

Article Type: Research Article

Abstract:  Multi-criteria decision making (MCDM) problems have been solved involving various types of fuzzy sets. We know that interval type-2 fuzzy sets (IT2FSs) are the most representative known fuzzy sets since they have the ability to capture both type of linguistic uncertainties associated with a word namely, the intra-personal and inter-personal uncertainties respectively. Here for MCDM problems, we will use the three trapezoidal fuzzy numbers (TT2FNs) which are more effective in capturing the uncertainty than IT2FSs, just like triangular fuzzy numbers has a better representational power than simple interval numbers. Moreover, Entropy method is employed for evaluating the values of unknown …attribute weights. The ranking method employed here is the grey correlation projection method (GRPM), obtained by joining grey relational method (GRM) and projection method (PM) respectively. Lastly an example will be given to check the productivity of the suggested method. Show more

Keywords: Multi-criteria decision making (MCDM), three trapezoidal fuzzy number (TT2FN), Entropy method (EM), grey correlation projection method (GRPM)

DOI: 10.3233/JIFS-179682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5957-5967, 2020

Authors: Touqeer, Muhammad | Shaheen, Kiran | Irfan, Rida

Article Type: Research Article

Abstract: Here we have employed three trapezoidal fuzzy numbers (TT2FNs) to deal with a multi-criteria decision making (MCDM) problems. The introduced technique takes into consideration the left and right areas of the three types of membership memberships involved in TT2FNs and also considers the risk attitude of decision maker. The presented method is more generalized since we have used TT2FNs, which are more effective in capturing uncertainty than IT2FSs, just like triangular fuzzy numbers has a better representational power than simple interval numbers. We have considered the unknown attribute environment where maximizing deviation method has been employed to evaluate the attribute …weights. Moreover, evaluation model for manufacturing plants with linguistic information has been provided as an illustrative example for the justification of the proposed technique. Show more

Keywords: MCDM, IT2 fuzzy set (IT2FS), maximizing deviation, generalized fuzzy number, three trapezoidal fuzzy numbers (TTFNs)

DOI: 10.3233/JIFS-179683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5969-5978, 2020

Authors: Touqeer, Muhammad | Hafeez, Abid | Arshad, Misbah

Article Type: Research Article

Abstract: Here we present a method to deal with multi-attribute decision making(MADM)problems when the attribute values are modeled in the form of interval type-2 trapezoidal fuzzy numbers (IT2FNs), and the attribute weights are completely unknown. Grey Relation Projection Method (GRPM), which is a combination of “grey relational analysis method" and the “projection method" is employed for ranking the alternatives. The linguistic information is modeled in the form of “interval type-2 trapezoidal fuzzy numbers" (IT2TFN) which are able to capture both the intra personal and inter personal uncertainties associated with a linguistic term. Information Entropy Method (IEM) is used for calculating unknown …attribute weights. Lastly, an illustrative example is provided as a verification of the developed approach. Show more

Keywords: Multi-attribute decision making (MADM), interval type-2 trapezoidal fuzzy number (IT2TFN), Information Entropy method (IEM), grey relational projection method (GRPM)

DOI: 10.3233/JIFS-179684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5979-5986, 2020

Authors: Gupta, Punit | Goyal, Mayank Kumar | Mundra, Ankit | Tripathi, Rajan Prasad

Article Type: Research Article

Abstract: Technology has enabled us to carry the world on our tips. Cloud computing has majorly contributed to this by providing infrastructure services on the go using pay per use model and with high quality of services. Cloud services provide resources through various distributed datacenters and client requests been fulfilled over these datacenters which act as resources. Therefore, resource allocation plays an important role in providing a high quality of service like utilization, network delay and finish time. Biogeography-based optimization (BBO) is an optimization algorithm that is an evolutionary algorithm used to find optimized solution. In this work BBO algorithm is …been used for resource optimization problem in cloud environment at infrastructure as a service level. In past several task scheduling algorithms are being proposed to find a global best schedule to achieve least execution time and high performance like genetic algorithm, ACO and many more but as compared to GA, BBO has high probability to find global best solution. Existing solutions aim toward improving performance in term of power execution time, but they have not considered network performance and utilization of the systems performance parameters. Therefore, to improve the performance of cloud in network-aware environment we have proposed an efficient nature inspired BBO algorithm. Further, the proposed approach takes network overhead and utilization of the system into consideration to provide improved performance as compared to ACO, Genetic algorithm as well as with PSO. Show more

Keywords: BBO, cloud infrastructure, meta-heuristic, resource allocation

DOI: 10.3233/JIFS-179685

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5987-5997, 2020

Authors: Gal, Viviane | Banerjee, Soumya | Rad, Dana V.

Article Type: Research Article

Abstract: Pattern of emotion identification is one of the improvised research application regarding facial expression as major concern, in those cases, conventional facial expressions for patterns identification. The present model is based on signal collected from physiological sensors followed by consecutive deployment of unsupervised machine learning model. The proposed model is unsupervised in following aspects: firstly, it introduces Expectation Maximization problem with respect to unknown emotion labels to be derived from the measures. Correlation of physiological signal and individual emotion labels can be identified. This follows a considerable emotion classification method. However, the output of EM model doesn’t ensure the …correct identification of emotion class, if any. We introduce Support Vector Regression (SVR) as output module of this model. Hence, we try to forecast the probable classes of emotion after investigating the ranges of values and appropriate standard threshold values of physiological signal with respect to respective emotion class e.g. angry, frustration and joy. This should be noted that, the proposed model doesn’t envisage facial expression analysis. However, after successful implementation of Gaussian behaviors of mixed physiological signal, we can enhance the accuracy of identification. Significant emotional context exists in output with more precise results of emotion identification phases. Show more

Keywords: Deep learning, emotion pattern, hybrid model, physiological sensors, unsupervised learning

DOI: 10.3233/JIFS-179686

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5999-6017, 2020

Authors: Nath, Malaya Kumar | Dandapat, Samarendra | Barna, Cornel

Article Type: Research Article

Abstract:  Here, an unique approach is presented for automatic detection of blood vessels and estimation of retinal disorder from color fundus image. This technique can be used to determine the progression of retinal disorders due to diabetic retinopathy, which can help in better evaluation and treatment for clinical purposes. The proposed method combines the Gaussian based matched filter with Kirsch for extraction of blood vessels, and inpainting technique to determine the pathologically affected region. This is tested on various databases (such as: DRIVE, Aria and Glaucoma etc.,). Various performance measures (such as: accuracy, sensitivity, specificity and F-score etc.,) are used to …estimate the quality of blood vessels detection. Here, we have applied the segmentation technique to the subband-2 image in 5-level wavelet decomposition by db4 mother wavelet. This reduces the computational time for inpainting. Comparing the blood vessels and the pathologies, index for blood vessel damage is calculated. This index is proportional to retinal damage in case of diabetic retinopathy. Higher index corresponds to significant amount of blood vessel damage. From the index, progression of the disease and condition of the retina can be assessed. The index for blood vessel damage for Im-24 is 2.98%, whereas for Im-18 is 68.78%. This indicates that in Im-18 more blood vessels are affected by pathologies. It also indicates that maximum portion of the retina is affected by pathology. Show more

Keywords: Retinal vascular disorder, matched filter, inpainting, F-score, wavelet decomposition

DOI: 10.3233/JIFS-179687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6019-6030, 2020

Authors: Naga Srinivasu, P. | Srinivasa Rao, T. | Dicu, Anca M. | Mnerie, Corina Anca | Olariu, Iustin

Article Type: Research Article

Abstract: MRI image segmentation, a challenging task in medical diagnostics aids in extracting the summarized information of the anatomy of the human brain, thereby offering the potentiality for accurate treatment of the disease. The purpose of this work is to elevate the performances of different optimization techniques that are used in automated segmentation procedures. The performance of four algorithms was evaluated quantitatively over the genetic algorithm-based segmentation, which is the prevailing approach in automated segmentation. The upshot exhibits the accuracy and performance of various optimization techniques with a genetic algorithm.

Keywords: Genetic algorithm, harmonic mean, magnetic resonance imaging, SVM, PSO, TLBO

DOI: 10.3233/JIFS-179688

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6031-6043, 2020

Authors: Farooq, Umar | Gu, Jason | Asad, Muhammad Usman | Abbas, Ghulam | Hanif, Athar | Balas, Marius

Article Type: Research Article

Abstract: This paper proposes an online algorithm for identifying the nonlinear dynamical systems and is termed as neo-fuzzy based brain emotional learning plant identifier (NFBELPI). As the name suggests, the proposed identifier is a combination of brain emotional learning network and neo-fuzzy neurons. The integration of these two networks is realized in a way that retains the characteristics of both the networks while an enhanced performance is achieved at the same time. Precisely, the orbitofrontal cortex section of the brain emotional learning network is fused with neo-fuzzy neurons with a view to equip it with more knowledge than does the amygdala …section possesses. The proposed identifier accepts n -input and m -output samples to generate an estimate of the plant output and employs a brain emotional learning algorithm to lower the estimation error by adjusting a total of ((n  + m  + 1) × p ) + (n  + m  + 2) weights, with p being the number of neo-fuzzy neurons. The proposal is validated in a MATLAB programming environment using a simulated Narendra dynamical plant as well as against the data recorded from real forced duffing oscillator. Comparison with a brain emotional learning plant identifier (BELPI) and some other state-of-the art identifiers in terms of root mean squared error (RMSE) criterion reveals the improved performance of the proposed identifier. Show more

Keywords: System identification, brain emotional learning, neo-fuzzy neurons, MATLAB

DOI: 10.3233/JIFS-179689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6045-6051, 2020

Authors: Thang, Tran Ngoc | Solanki, Vijender Kumar | Dao, Tuan Anh | Thi Ngoc Anh, Nguyen | Van Hai, Pham

Article Type: Research Article

Abstract: In this article, we use a monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems which include many classes of captivating problems, for example when the criterion functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, an approximation of the weakly efficient solution set is obtained containing the whole weakly efficient solution set of the problem. The algorithm is proved to be convergent and it is suitable to be implemented in parallel using convex programming tools. Some computational experiments are reported to show the accuracy and efficiency …of the algorithm. Show more

Keywords: Multiobjective programming, monotonic optimization, strictly quasiconvex, outcome space, outer approximation

DOI: 10.3233/JIFS-179690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6053-6063, 2020

Authors: Deb, Dipankar

Article Type: Research Article

Abstract: For decentralized biomass plants using farm residue as input, the economic viability study are available in literature. The study calculates the different aspects of the cost of biomass power plants such as the cost of capital investment, running and maintenance of plants, residue processing costs, and the economic value equivalent of embedded nutrients (NPK) that are lost from residues. A comparison is drawn between the costs and revenue from the plant’s power generation. The word modest is used for giving analogy to the power plant which has capacity higher than decentralized power plants and lower than centralized power plants. In …Gujarat there exists total four biomass power plants which should be considered in the modest category. The aim is to establish the cost efficiency of adding soil nutritious chemical fertilizers and using agricultural residues for the purposes of energy-building biomass (rather than incorporating soil). To study economic viability, the discounted rate method is used. In our paper, a modest / centralized biomass power plant has been studied for economic feasibility. For this, we have selected the state of Gujarat and followed district-wise analysis. We have used the penalty method given in optimization research in order to calculate transportation and other costs. Also, we studied a soil nutrition index for measuring soil health and the proportions of N, P and K in the soil. For annual crop production, the data used is district-wise for Gujarat state for the year 2011-12. Show more

Keywords: Agricultural residue, Biomass energy, Soil incorporation, Centralized basis, NPK

DOI: 10.3233/JIFS-179691

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6065-6074, 2020

Authors: Tariq, Muhammad Imran | Tayyaba, Shahzadi | Ali Mian, Natash | Sarfraz, Muhammad Shahzad | De-la-Hoz-Franco, Emiro | Butt, Shariq Aziz | Santarcangelo, Vito | Rad, Dana V.

Article Type: Research Article

Abstract: The organizations utilizing the cloud computing services are required to select suitable Information Security Controls (ISCs) to maintain data security and privacy. Many organizations bought popular products or traditional tools to select ISCs. However, selecting the wrong information security control without keeping in view severity of the risk, budgetary constraints, measures cost, and implementation and mitigation time may lead to leakage of data and resultantly, organizations may lose their user’s information, face financial implications, even reputation of the organization may be damaged. Therefore, the organizations should evaluate each control based on certain criteria like implementation time, mitigation time, exploitation time, …risk, budgetary constraints, and previous effectiveness of the control under review. In this article, the authors utilized the methodologies of the Multi Criteria Decision Making (MCDM), Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help the cloud organizations in the prioritization and selection of the best information security control. Furthermore, a numerical example is also given, depicting the step by step utilization of the method in cloud organizations for the prioritization of the information security controls. Show more

Keywords: Information security, Analytical Hierarchy Process, TOPSIS, fuzzy logic, MCDM, MADM

DOI: 10.3233/JIFS-179692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6075-6088, 2020

Authors: Iqbal, Shafqat | Zhang, Chongqi | Arif, Muhammad | Hassan, Munawar | Ahmad, Shakeel

Article Type: Research Article

Abstract: Time series is a classification of data series of variables, orderly arranged with respect to time. In time series analysis, forecasting is the vital area of study besides other meaningful characteristics of the data. It has vast application in decision-making and prediction in the domain of economics, agriculture, medicine, industries, energy sector and other sciences. Fuzzy time series emerged as a robust tool to cater for historical data in linguistic values. This paper proposes the new method of fuzzy time series forecasting based on the approach of fuzzy clustering and information granules integrated with the weighted average approach to deal …with the uncertainty in data series. To distinguish the power of modeling and prediction, the strategies of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized as a criterion. Findings illustrate that proposed fuzzy based time series approach is vigorous to compute the accurate estimates. Show more

Keywords: Fuzzy set, fuzzy c-means clustering, information granules, fuzzy time series, forecasting

DOI: 10.3233/JIFS-179693

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6089-6098, 2020

Authors: Datta, Rupak | Dey, Rajeeb | Saravanakumar, Ramasamy | Bhattacharya, Baby | Lin, Tsung-Chih

Article Type: Research Article

Abstract:  This paper deals with global delay-range-dependent (DRD) stability analysis for Takagi–Sugeno (T–S) fuzzy Hopfield neural network with time-varying delays. First, we proposes a new Lyapunov-Krasovskii functional (LKF) by incorporating the information of activation function, the lower and upper bound of time delay. Further, to achieve the larger delay bound results and approximating the derivatives of LKFs, Wirtinger based inequality (WBI) together with reciprocal convex lemma (RCL) is being utilized. As a result some DRD global stability conditions for the system under consideration with less conservatism are derived in an linear matrix inequality (LMI) framework. Three numerical examples are presented in …this work to exhibit the efficacy of the proposed stability criterion over the recent existing results. Show more

Keywords: Delay-range-dependent stability, takagi–sugeno fuzzy model, Hopfield neural networks, LKF, LMI

DOI: 10.3233/JIFS-179694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6099-6109, 2020

Authors: Tamasila, Matei | Prostean, Gabriela | Ivascu, Larisa | Cioca, Lucian-Ionel | Draghici, Anca | Diaconescu, Andra

Article Type: Research Article

Abstract: The present study aims at identifying the main drivers underlying the development of municipal solid waste management (MSWM) so as to ensure an effective enhancement of the current waste management system and significantly improved recycling rates. Based on the factors identified in the qualitative evaluation of the deployed statistics and using fuzzy multi-criteria decision-making (MCDM), these factors are hierarchized and the competitive strategic alternative is selected/customized. The judgment of eight experts from the eight major regions of Romania has been used in the applied fuzzy AHP and TOPSIS methodologies. The robustness of the results is analyzed against the sensitivity analysis. …Following the sensitivity analysis, the alternatives retained their rank so that the eight experts’ assessments have been validated. By developing a sustainable MSWM, it is claimed that the recycling rate in Romania will increase. Show more

Keywords: Municipal solid waste management (MSWM), selective collection, recycle, fuzzy analytic hierarchy process

DOI: 10.3233/JIFS-179695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6111-6127, 2020

Authors: Lin, Yu-Chen | Nguyen, Ha Ly Thi | Balas, Valentina Emilia | Lin, Tsung-Chih | Kuo, I-Chun

Article Type: Research Article

Abstract: This paper presents an adaptive prediction-based control scheme for developing an ecological cruise control (ECC) system when simultaneous running on the roads with the continuous curves and up-down slopes, which aims to guarantee the driving safety, riding comfort of a vehicle and enhance its fuel efficiency as well. This study is mainly divided into two parts: vehicle longitudinal speed control and vehicle lateral stability control. Firstly, based on GPS data, topographical information, and a fuel consumption model, the prediction based on approximate dynamic programming (ADP) using adaptive fuzzy neural networks (FNNs) is employed to calculate appropriate control signal required for …ecological driving. Secondly, to maintain the vehicle stable on the curved road, the vehicle lateral stability control is presented utilizing sliding mode control approach. The effectiveness of the proposed ECC system, in view of stability, improved riding comfort and fuel efficiency, has been validated on the CarSim environment. Show more

Keywords: Ecological cruise control (ECC) system, continuous curve, up-down slope, driving safety, fuel efficiency, approximate dynamic programming (ADP)

DOI: 10.3233/JIFS-179696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6129-6144, 2020

Authors: Arif, Muhammad | Wang, Guojun | Peng, Tao | Balas, Valentina Emilia | Geman, Oana | Chen, Jianer

Article Type: Research Article

Abstract: Vehicular Ad-hoc Networks (VANETs) have unique capabilities and are associated with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to reduce traffic dynamics and provide support to the providers and the drivers to rely on security and communication alerts. Our proposed method is only supported by V2V and V2I communications and neighbourhood localization records, which are summarized in an adaptive adjustment of the forwarded information related to the messages(data communication). We optimize these message information, and only the relevant information is transmitted to the relevant vehicles, and all other unnecessary information is terminated. Because, irrelevant information causes many problems, like unnecessary resource …utilization and energy consumption. In this case, the driver will be promptly reminded to take appropriate measures. A swarm intelligence algorithm such as the Artificial Bee Colony (ABC) is utilized to optimize the communication mechanism. In this paper, we present a high-level framework for ABC based on the fuzzy logic for the VANETs. We prove the validity and efficiency of the proposed method. Our proposed method achieves higher optimization accuracy in information broadcasting with minimum delay and with the lowest energy consumption. Show more

Keywords: Communication, vehicles, optimization, fuzzy logic, ad-hoc network, artificial bee colony

DOI: 10.3233/JIFS-179697

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6145-6157, 2020

Authors: Yoseph, Fahed | Ahamed Hassain Malim, Nurul Hashimah | Heikkilä, Markku | Brezulianu, Adrian | Geman, Oana | Paskhal Rostam, Nur Aqilah

Article Type: Research Article

Abstract: Targeted marketing strategy is a prominent topic that has received substantial attention from both industries and academia. Market segmentation is a widely used approach in investigating the heterogeneity of customer buying behavior and profitability. It is important to note that conventional market segmentation models in the retail industry are predominantly descriptive methods, lack sufficient market insights, and often fail to identify sufficiently small segments. This study also takes advantage of the dynamics involved in the Hadoop distributed file system for its ability to process vast dataset. Three different market segmentation experiments using modified best fit regression, i.e., Expectation-Maximization (EM) and …K-Means++ clustering algorithms were conducted and subsequently assessed using cluster quality assessment. The results of this research are twofold: i) The insight on customer purchase behavior revealed for each Customer Lifetime Value (CLTV) segment; ii) performance of the clustering algorithm for producing accurate market segments. The analysis indicated that the average lifetime of the customer was only two years, and the churn rate was 52%. Consequently, a marketing strategy was devised based on these results and implemented on the departmental store sales. It was revealed in the marketing record that the sales growth rate up increased from 5% to 9%. Show more

Keywords: Market segmentation, data mining, customer lifetime value (CLTV), RFM model (recency frequency monetary)

DOI: 10.3233/JIFS-179698

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6159-6173, 2020

Authors: Lin, Tsung-Chih | Li, Cheng-You | Chen, Pin-Fan | Chen, Wei-Kai | Dey, Rajeeb | Balas, Marius M. | Olariu, Teodora | Wong, Wai-Shing

Article Type: Research Article

Abstract: This paper presents an identifier based intelligent adaptive fuzzy control scheme with regulating blood glucose concentration in normoglycemic level of 70 mg/dl for type 1 diabetic patients. The identifier is built with fuzzy neural network (FNN) to predict the blood glucose concentration of the diabetic patient. The fuzzy based controller with generic operating regimes which cluster all the adaptive control rules is designed to robustly reject the multiple meal disturbances resulting from food intake and deal with the parametric uncertainties in model and measurement noise. All the parameters of the FNN and of the fuzzy logic system are tuned by backpropagation …(BP), to achieve the control objectives. The numerical simulations are performed to show that the set point tracking, meal disturbances and measurement noise rejection can be realized within this method. Show more

Keywords: Adaptive control, glucose-insulin, diabetes, FNN, Backpropagation

DOI: 10.3233/JIFS-179699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6175-6184, 2020

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6185-6185, 2020

Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Trajkovic, Ljiljana

Article Type: Editorial

DOI: 10.3233/JIFS-179700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6187-6191, 2020

Authors: Ajeena Beegom, A.S. | Chinmayan, P.

Article Type: Research Article

Abstract: In natural language processing, the problem of finding the intended meaning or “sense” of a word which is activated by the use of that word in a particular context is generally known as word sense disambiguation (WSD) problem. The solution to this problem impacts many other fields of natural language processing including sentiment analysis and machine translation. Here, WSD problem is modelled as a combinatorial optimization problem where the goal is to find a sequence of meanings or senses that maximizes the semantic meaning among the targeted words. In this work, an algorithm is proposed that uses a combinatorial version …of particle swarm optimization algorithm for solving WSD problem. The test results show that the algorithm performs better than existing methods. Show more

Keywords: Word sense disambiguation, particle swarm optimization, knowledge-based approach, combinatorial PSO

DOI: 10.3233/JIFS-179701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6193-6200, 2020

Authors: Akhtar, Nadeem | Sufyan Beg, M.M. | Hussain, Md. Muzakkir

Article Type: Research Article

Abstract:  Most extractive multi-document summarization (MDS) methods relies on extraction of content relevant sentences ignoring sentence relationships. In this work, we propose a unified framework for extractive MDS that also considers sentence relationships. We argue that adding a sentence to the summary increases summary score by relevance score of the new sentence plus some additional score which depends on the relationships of new sentence with other summary sentences. The quantification of additional score depends on how coherent the new sentence is with respect to the existing sentences in the summary. Simultaneously, some score is decreased from the summary score due to …the redundancy which depends on overlap between new and existing summary sentences. To find the exact solution, sentence extraction problem is modeled as integer linear problem. The sentence relevance score is found using content and surface features of the sentence using topic model and regression framework. To find the relative coherence score, transition probabilities in the entity grid model are used. Redundancy between sentences is found using support vector regression that uses sentence overlapping features. The proposed method is evaluated on DUC datasets over query based multi-document summarization task. DUC 2006 dataset is used as training and development set for tuning parameters. Experimental results produce ROUGE score comparable to the state-of-the-art methods demonstrating the effectiveness of the proposed method. Show more

Keywords: Multi-document summarization, topic model, support vector regression, entity grid, rouge

DOI: 10.3233/JIFS-179702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6201-6210, 2020

Authors: El-Alfy, El-Sayed M. | Al-Azani, Sadam

Article Type: Research Article

Abstract:  With the proliferation of social media and mobile technology, huge amount of unstructured data is posted daily online. Consequently, sentiment analysis has gained increasing importance as a tool to understand the opinions of certain groups of people on contemporary political, cultural, social or commercial issues. Unlike western languages, the research on sentiment analysis for dialectical Arabic language is still in its early stages with several challenges to be addressed. The main goal of this study is twofold. First, it compares the performance of core machine learning algorithms for detecting the polarity in imbalanced Arabic tweet datasets using neural word embedding …as a feature extractor rather than hand-crafted or traditional features. Second, it examines the impact of using various oversampling techniques to handle the highly-imbalanced nature of the sentiment data. Intensive empirical analysis of nine machine learning methods and six oversampling methods has been conducted and the results have been discussed in terms of a wide range of performance measures. Show more

Keywords: Social network, sentiment analysis, polarity detection, word embedding, machine learning, imbalanced dataset, Arabic tweets

DOI: 10.3233/JIFS-179703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6211-6222, 2020

Authors: Sanagar, Swati | Gupta, Deepa

Article Type: Research Article

Abstract: Sentiment analysis research has evolved over the years to extract relevant information from opinionated raw text. Sentiment lexicon is a compiled list of sentiment words and a core component of sentiment analysis tasks. These words play a key role in domain adaptation. Domain adaptation is challenging due to variation in sentiments across the domains. We propose a solution to this research problem by presenting a genre-level sentiment lexicon adaptation approach. The model uses a language domain sense to represent the genre pertaining to the distinct characteristics of the communicated text. The approach addresses the generalization of knowledge at the genre …level by learning the multi-source domain lexicon for the selected source domains. The novelty of our approach lies in the genre level relevancy of the source lexicon to the target domains. The model uses unlabeled training data for the source and target domain sentiment lexicon learning. The lexicon adaptation is demonstrated on a long list of target domains that address the three domain adaptation challenges. Experimental results have proved that the model learns the relevant scores and polarities of sentiment words, in addition, it identifies new domain-based sentiment words. The model is evaluated in comparison with standard baselines. Show more

Keywords: Lexicon adaptation, sentiment lexicon, domain adaptation, multiple source, transfer learning, sentiment analysis

DOI: 10.3233/JIFS-179704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6223-6234, 2020

Authors: Richa, | Bedi, Punam

Article Type: Research Article

Abstract: Group recommender system provides suggestions for a group of users by exploring the choices of individual users of the group. Popularity of group recommender systems is increasing because many activities such as listening to music, watching movies, traveling, etc. are normally performed in groups rather individually. Group recommender systems like personal recommender systems also suffer from cold start and sparsity issues. The cold start and sparsity issues result into inaccurate recommendation computation which degrades the recommendation quality. To handle the cold start and sparsity issues in a Group Recommender System (GRS), this paper proposes to use cross domain approach and …introduces Cross Domain Group Recommender System (CDGRS). The recommendations provided by trustworthy and reputed users in the group enhance the acceptance towards the presented recommendations as compared to the other individuals in the group. We have combined the social factors e.g. trust and reputation to get influential user in the group recommendation. A prototype of the system is developed for tourism domain that incorporates four sub-domains i.e. restaurants, hotels, tourist places and shopping places. The performance of CDGRS is compared with GRS. Spearman’s Correlation Coefficient, MAE, RMSE, Precision, Recall and F-measure are used to find the accuracy of the generated recommendations. Show more

Keywords: Recommender system, cross domain, group recommender system, multi-agent system, user influence

DOI: 10.3233/JIFS-179705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6235-6246, 2020

Authors: Sujitha, P. | Simon, Philomina

Article Type: Research Article

Abstract: For the last three decades human activity recognition has shown a huge technological advancement due to less expensive RGB-D cameras and the increase in the large volume of video data. As a result of the increase in number of surveillance cameras, manual annotation becomes difficult and need for automatic recognition and annotation of video arises. In this paper, we introduce a computationally and storage efficient method for recognizing human activities from depth videos and a new frame selection method based on the mean value of motion energy. We extract normal vectors from the points in the boundary curve. Then polynormals …are obtained by sequentially attaching the normals from a neighborhood of each of the points in the boundary curve. These polynormals from a spatio-temporal cuboid constructed from the input video and it is pooled to form the Super Normal vectors. These Super Normal vectors are the final feature vectors, which are given as input to the classifier. The classifier used is lib-linear SVM. The results on MSRAction3D dataset show that the algorithm we put forward is fast and the accuracy obtained is comparable with the existing methods. The method which we proposed here gives an accuracy of 88% while taking whole frames and 89.82% when frame selection method is applied. The proposed method is also tested on UTD-MHAD dataset. Show more

Keywords: Motion energy, depth videos, frame selection, boundary curves, polynormal, dictionary learning

DOI: 10.3233/JIFS-179706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6247-6255, 2020

Authors: Alpana, | Chand, Satish

Article Type: Research Article

Abstract: Coal is a primary natural resource of fuel that is efficiently used for electricity generation, steel or iron production, and household usage. Characterization is needed for industries to understand the quality of coal before shipping. Currently, industries follow chemical, microscopical, and machine-based analysis as the gold standard for coal characterization. These conventional analyses of coal are an indispensable method over the years and have tested by high skilled petrologists. Though, these types of optical or machine-dependent recognition of coal category samples are quite slow, expensive, and restricted by subjective analyses with less accuracy. The main aim of this research is …to propose an accurate, time, and cost-effective machine learning-based automated characterization system by analyzing coal color and textural features. This paper comes up with a quantitative approach toward the characterization of dissimilar types of coal for better utilization in industries. The proposed ensemble learning coal characterization method provides an accuracy of around 97% and takes less computational time than conventional methods. Hence, the proposed automated coal characterization system provides support to industries in the development of computer-aided assessment of coal category samples. Show more

Keywords: Coal, HSV, GLCM, image processing, machine learning

DOI: 10.3233/JIFS-179707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6257-6267, 2020

Authors: Ramachandran, Sivakumar | Kochitty, Shymol | Vinekar, Anand | John, Renu

Article Type: Research Article

Abstract: The identification of landmark features such as optic disc is of high prognostic significance in diagnosing various ophthalmic diseases. A retinal fundus photograph provides a non-invasive observation of the optic disc. The wide variability present in fundus images poses difficulties in its detection and further analysis. The reported work is a part of the fundus image screening for the diagnosis of Retinopathy of Prematurity (ROP), a sight threatening disorder seen in preterm infants. The diagnostic procedure for this disease estimates blood vessel tortuosity in a pre-defined area around the optic disc. Hence accurate optic disc localization is very important for …the disease diagnosis. In this paper, we present an optic disc localization technique using a deep neural network based framework. The proposed system relies on the underlying architecture of YOLOv3, a fully convolutional neural network pipeline for object detection and localization. The new approach is tested in 10 different data sets and has achieved an overall accuracy of 99.25%, outperforming other deep learning-based OD detection methods. The test results guarantees the robustness of the proposed technique, and hence may be deployed to assist medical experts for disease diagnosis. Show more

Keywords: Optic disc, deep learning, convolutional neural network, retinal images, ROP diagnosis

DOI: 10.3233/JIFS-179708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6269-6278, 2020

Authors: Shiji, T. P. | Remya, S. | Lakshmanan, Rekha | Pratab, Thara | Thomas, Vinu

Article Type: Research Article

Abstract: Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer aided detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented …using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising. Show more

Keywords: Breast ultrasound, Shearlet transform, tumour detection, wavelet modulus maxima, SVM classifier

DOI: 10.3233/JIFS-179709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6279-6290, 2020

Authors: Ravikumar, Sourav | Vinod, Dayanand | Ramesh, Gowtham | Pulari, Sini Raj | Mathi, Senthilkumar

Article Type: Research Article

Abstract:  Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and …elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered approach used in the proposed work improves resource management which is a major bottleneck in real-time deployment scenarios. Show more

Keywords: Human elephant conflict, machine learning, convolutional neural network, support vector machine

DOI: 10.3233/JIFS-179710

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6291-6298, 2020

Authors: Jyotsna, C. | Amudha, J. | Rao, Raghavendra | Nayar, Ravi

Article Type: Research Article

Abstract: Trail making test is a cognitive impairment test used for understanding the visual attention during the visual search task. The classical paper pencil method measures the completion time of the participant and there was no mechanism for comparison across the participant with similar feature. The psychologist has to observe the reactions of the participants during the trial process and there is no mechanism to capture it. This study made an attempt to resolve the above problem and tried to infer additional parameters which can support psychologist to understand the participant performance in trail making test. The insight provided by the …approach is to extract various features which helps a psychologist by providing individual profiling and group profiling of a person and can understand the group of people who show similar cognitive impairment while performing trail Making Test. The proposed Intelligent Gaze Tracking approach could classify the participant into three different groups like low, high and medium cognitive impairment based on the extracted gaze features. The proposed approach has been compared across existing literature survey to significantly show the advantage of the system in terms of identifying the people with similar characteristics in terms of cognitive impairment. Show more

Keywords: Eye tracking, cognitive impairment, trail making test, area of interest, scanpath, fixation, adaptive neuro fuzzy inference system, k-means clustering

DOI: 10.3233/JIFS-179711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6299-6310, 2020

Authors: Amudha, J. | Divya, K.V. | Aarthi, R.

Article Type: Research Article

Abstract: Top–down influences play a major role in the primate’s visual attention mechanism. Design of top-down influences for target search problems is the recommended approach to develop better computational models. Existing top down computational visual attention models mainly exploit three factors namely the context information, target information and task demands. Here in this paper we propose a Fuzzy based System for Target Search (FSTS) which makes use of target information as the top-down factor. The system uses Fuzzy logic to predict the salient locations in an image based on the prior information about a target object to be detected in a …scene or frame. The performance of the system was analysed using multiple evaluation parameters and is found to have a better average hit number, number of first hits and elapsed CPU time than the existing system. The saliency map comparison is performed with human eye fixation map and is found to predict the human fixations with better accuracy than existing systems. Show more

Keywords: Visual attention, saliency, regions of interest, fuzzy system, computer vision

DOI: 10.3233/JIFS-179712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6311-6323, 2020

Authors: Navdeep, | Singh, Vijander | Rani, Asha | Goyal, Sonal

Article Type: Research Article

Abstract: This paper presents an improved hyper smoothing function based methodology for efficient edge detection. The main aim of this work is to obtain localized edges of noisy and blurred images without duplicate ones and integrating them into meaningful object boundaries. Therefore, logarithmic hyper-smoothing function is introduced in local binary pattern leading to improved hyperfunction based local binary pattern (IHLBP) algorithm. The proposed technique uses an improved counting scheme to correctly evaluate the number of image points having pixel value greater than or equal to the central pixel. The IHLBP algorithm is tested on synthetic images, radiography images, real-life pictures from …USC-SIPL and BSDS database. Improved local binary pattern (ILBP), hyper local binary pattern (HLBP), Canny and Sobel methods are also used for comparative analysis. The results reveal that the proposed algorithm performs well on all synthetic and real images in the presence of blur and salt & pepper noise. Thus IHLBP proves to be an effective approach for edge detection in comparison to conventional methods. Show more

Keywords: Edge detection, digital radiography images, real images, noise images

DOI: 10.3233/JIFS-179713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6325-6335, 2020

Authors: Jahnavi, B. Sai | Supraja, B. Sai | Lalitha, S.

Article Type: Research Article

Abstract: The main motive of this work is to discriminate a vital neurodegenerative condition of Parkinson Disease (PD) affected patients from individuals with no history of such a disorder. Excitation source features, voice quality features and prosodic features are the speech constituents considered. Voice samples of PD patients are extracted from the University of California-Irvine (UCI) Machine Learning Parkinson’s database. Random Forest (RF) decision trees and Support Vector Machine (SVM) are considered for classification. Feature reduction is applied with the Correlation based Feature Selection (CFS) attribute selector classifier that utilizes Best First Selector (BFS) as a search algorithm. The work involves …recognizing a PD patient from a healthy individual using only two speech sounds of /a/ and /o/. The speech sounds are extracted without the association of a certified clinician, that adds novelty. The proposed algorithm is non-invasive and accomplished 94.77% accuracy with feature selection process and applying RF classifier. Show more

Keywords: Best first selector, correlation based feature selection, feature reduction, parkinson, random forest, support vector machine

DOI: 10.3233/JIFS-179714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6337-6345, 2020

Authors: Singh, Utkarsh | Gupta, Akshay | Bisharad, Dipjyoti | Arif, Wasim

Article Type: Research Article

Abstract: Speech analysis for extracting attributes such as the speaker, gender, accent and like has been a field of great interest and has been widely studied. The paper presents a novel architecture for accent identification by using a cascade of two deep-learning architecture. We design and test our proposed architecture on common voice dataset. The architecture consists of a cascade of Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (CRNN). It is trained on Mel-spectrogram of the audios. We consider five of the most popular English accents groups namely India, Australia, US, England, Canada in this study. The proposed model …has an accuracy of 78.48% using CNN and 83.21% using CRNN. Show more

Keywords: Mel-spectrogram, deep neural networks, foreign accent classification, recurrent neural network

DOI: 10.3233/JIFS-179715

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6347-6352, 2020

Authors: Vyshnav, M.T. | Sachin Kumar, S. | Mohan, Neethu | Soman, K.P.

Article Type: Research Article

Abstract: The present paper proposes Random Kitchen Sink based music/speech classification. The temporal and spectral features such as spectral centroid, Spectral roll-off, spectral flux, Mel-frequency cepstral coefficients, entropy, and Zero-crossing rate are extracted from the signals. In order to show the competence of the proposed approach, experimental evaluations and comparisons are performed. Even though both speech and music signals differ in their production mechanisms, those share many common characteristics such as a common spectrum of frequency and are comparatively non-stationary which makes the classification difficult. The proposed approach explicitly maps the data to a feature space where it is linearly separable. …The evaluation results shows that the proposed approach provides competing scores with the methods in the available literature. Show more

Keywords: Music/speech, random kitchen sink, feature vector, GTZAN database, S&S database, spectral features

DOI: 10.3233/JIFS-179716

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6353-6363, 2020

Authors: Shinde, Hemendra Vijay | Patil, Devashri Manohar | Edla, Damodar Reddy | Bablani, Annushree | Mahananda, Malkauthekar

Article Type: Research Article

Abstract: Background: Students have to manage the strain of rising education level and their future career, accompanying the hormonal changes during their pubescence. This creates a great impact on their education as well as personal life. In this paper, an analysis has been made to study the impact of yoga on engineering students. To understand the impact. Brain-Computer Interface (BCI) approaches have been utilized. An EEG based BCI is used which will give a direct view of whats going on in the students’ brains. Methodology: In this work, an experiment has been performed on engineering students and their brain …activity is recorded before and after practicing yoga. In the experimental procedure, EEG signals are acquired from 8 electrodes which are associated with the cognitive and memory-related tasks of the brain. During each trial, participants solve the set of mathematical questionnaire. EEG signals are acquired during test trials before and after the yoga session. A bandpass filter is applied to preprocess the EEG signals. A discrete wavelet transform is implemented for feature extraction of the preprocessed signals. Results: Different classification algorithms are applied to classify the EEG signals before and after the yoga session. To measure the classification performance, measures such as accuracy, sensitivity, and specificity are presented in the paper. The highest accuracy of 95 % is achieved with Probabilistic Neural Network. Classification concluded the variations in signals before and after yoga. Further, in this work analysis of frequency bands, accuracy and score of the subjects before and after the yoga session are also done. Show more

Keywords: Brain Computer Interface, EEG signals, yoga, wavelets, Probabilistic Neural Network

DOI: 10.3233/JIFS-179717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6365-6376, 2020

Authors: Manu, D.K. | Karthik, P.

Article Type: Research Article

Abstract: Increasingly challenging problems have been addressed in the field of underwater acoustics. The critical topics of research have been increasing the sensitivity of Sensors, measurement of sound intensity, locating target from the source, measurement of radiating power, etc. In this proposed research work, the fiber optic hydrophone sensitivity is increased by varying different parameters like dimensions of the materials, Poisson’s Ratio and Young’s Modulus of the mandrel. The fiber optic hydrophone is composed of different materials- Nylon, Aluminum, Polystyrene, Fiber, and Polyurethane. The design of the hydrophone is carried out using finite element analysis tools. The parameters of the hydrophone …(mandrel) have been varied, and the analytical result shows that there is a considerable increase in sensitivity. These results demonstrate that there is an improvement in the hydrophone sensitivity by around 20 db in contrast with the existing hydrophone. From this result, we are now focusing on customizing the design and further validating the design, in the future. Show more

Keywords: Fiber optic-hydrophone, sensitivity, poissons ratio, Young’s modulus, mandrel, finite element analysis

DOI: 10.3233/JIFS-179718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6377-6382, 2020

Authors: Chaithanya Krishna, D.C. | Tripathi, Shikha

Article Type: Research Article

Abstract: A hybrid architecture for transforms such as N-point Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Wavelet Transform (DWT) has been proposed and implemented using triple matrix product method. There is limited work reported on efficient single architecture that can perform multiple transforms simultaneously or serially depending on the application requirement. The Hybrid architecture implemented, can compute various transforms efficiently. A controller is designed which can perform different transforms using the hybrid architecture based on the input provided. The implemented systolic array can be used for computing the diagonal elements of triple-matrix product. The …designed architecture produces the output of transform sequence in order, which avoids reordering at output. The implemented architecture can be used to handle large sized transforms by repeatedly using fixed size architecture for a large number of points without increasing the number of Processing Elements (PEs). The proposed architecture has been validated with a watermarking algorithm that uses DCT and DWT transforms and its performance analyzed. The proposed hybrid architecture is implemented on Spartan-7 xc7s100fgga676-1. The simulation results are given and analyzed against standalone architecture. Show more

Keywords: Hybrid architecture, transforms implementation, triple matrix product method

DOI: 10.3233/JIFS-179719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6383-6390, 2020

Authors: Remya Revi, K. | Wilscy, M.

Article Type: Research Article

Abstract: Nowadays the manipulations of digital images are common due to easy access of many online photo editing applications and image editing softwares. Forged images are widely used in social media for creating deceitful propaganda of an individual or a particular event and for cooking up fake evidences even in court proceedings. Hence ensuring the integrity of digital images is of prime significance and it has become a hot research area. In this paper, a novel technique for image forgery detection is proposed. The method utilizes the layer activation of inception-ResNet-v2, a pretrained Convolutional Neural Network(CNN)to extract the deep textural features …from Rotation Invariant – Local Binary Pattern (RI-LBP) map of the chrominance image. Non-negative Matrix Factorization (NMF) technique is used to reduce the dimensionality of the extracted features. The dimensionality reduced features are used to train a quadratic Support Vector Machine(SVM) classifier to classify images into forged or authentic. The method is assessed on four benchmark datasets (CASIA ITDE v1.0, CASIA ITDE v2.0, CUISDE and IFS-TC). Extensive experimental analysis is done and the results show an improved detection accuracy compared to the state-of-the-art methods. Show more

Keywords: Deep learning, rotation invariant-local binary pattern, pretrained convolutional neural etworks, deep textual features, image forgery detection

DOI: 10.3233/JIFS-179720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6391-6401, 2020