Showing papers in "International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems in 2022"

Multi-Objective Particle Swarm Optimization Based Preprocessing of Multi-Class Extremely Imbalanced Datasets

Abstract: Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many researchers have introduced methods to select features for performing classification on multi-class attributes. The process becomes more tedious when the attribute values are imbalanced for which researchers have contributed many methods. But, there is no sufficient research to handle extreme imbalance and feature selection together and hence this paper aims to bridge this gap. Here Particle Swarm Optimization (PSO), an efficient evolutionary algorithm is used to handle imbalanced dataset and feature selection process is also enhanced with the required functionalities. First, Multi-objective Particle Swarm Optimization is used to transform the imbalanced datasets into balanced one and then another version of Multi-objective Particle Swarm Optimization is used to select the significant features. The proposed methodology is applied on eight multi-class extremely imbalanced datasets and the experimental results are found to be better than other existing methods in terms of classification accuracy, G mean, F measure. The results validated by using Friedman test also confirm that the proposed methodology effectively balances the dataset with less number of features than other methods.

Aggregation Operators with Undefined Inputs or Outputs

Abstract: We consider a generalization of aggregation and fusion functions when some of their inputs or outputs are undefined. For simplicity, we represent undefined values by a single NaN (not-a-number) value. We define four main methods of treating undefined values, observe some of their properties, and compare them with several mainstream implementations and norms for handling NaN values. Finally, as a case study demonstrating the apparatus's usefulness, the formalism is applied to the discrete fuzzy transform technique. The formalism allows us to prove a generalized approximation theorem for fuzzy transforms, which removes previously required restrictions on fuzzy partitions for sparse input data.

Personalized E-Learning Based on Ant Colony Optimization

Abstract: With the advancement in technology, the approach to learning has also been modified. “Standardization” and “One-size-fits-all” has become an outdated concept. To adjust to the changing learning approaches, e-learning came into being, but this was not as per the knowledge and intelligence of users. This created a hurdle in the achievement of better learning and acquisition of skills. This calls for the provision of personalization in e-learning. Successful implementation of personalized e-learning in the present education system will lead to better and faster learning by adapting as per the preferences and knowledge of students. The core idea behind this research is to make an application using Android, which provides a personalized and adaptable route of e-learning using Ant Colony Optimization and recommendations from similar peers. This research will cater to the needs of many students, and it will help in decreasing the time taken to complete any subject or course. It will also help in attaining better and efficient learning as the learning route is determined as per the user. Also, the collection of records of every user will help in improving efficiency and accuracy in the determination of the learning path. The developed app aiming for adaptative e-learning can act as a promising solution during the Covid-19 scenario.

Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System

Abstract: Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively.

ℐ풢,풩-Implications Induced from Quasi-Grouping Functions and Negations on Bounded Lattices

Abstract: Fuzzy implications, as a generalization of the classical implication, are not only required in fuzzy logic systems and fuzzy control but also have an important effect on solving fuzzy relational equations, fuzzymathematicalmorphology, image processing and etc. Therefore, it is necessary for us to investigate multiple types of fuzzy implications on different truth values sets. In this paper, we devote to propose theQL -(operators) implications derived from quasioverlap (quasi-grouping) functions and negations on bounded lattices. Firstly, we exactly investigate some desirable properties ofQL -operators. Afterwards, we provide a necessary and sufficient condition along with a sufficient condition for theQL -operator to be aQL implication and study various prime properties ofQL -implications. Moreover, we show the relationship between QL -implications and L-automorphisms on bounded lattices. Finally, we consider the QL -implications to analyze their intersection with IG ,N -implications derived from quasi-grouping functions and negations on bounded lattices.

Extending Set Measures to Orthopair Fuzzy Sets

Abstract: Yager have proposed the extending set measures to Pythagorean fuzzy sets, which is able to efficiently solve the problems of uncertain information representation in Pythagorean fuzzy environment. However, the Pythagorean fuzzy sets represent a limited range of fields, while the q-rung orthopair fuzzy sets can represent many fuzzy sets, including the Pythagorean fuzzy sets, Fermatean fuzzy sets, and intuitionistic fuzzy sets. In order to extend the extending set measures to Pythagorean fuzzy sets to a broader range, the paper proposes the extending set measures to orthopair fuzzy sets, which can extend the extending set measures to Pythagorean fuzzy sets to q-rung orthopair fuzzy environment. The extending set measures to orthopair fuzzy sets combined with level sets, measure, principle extension and Choquet Integral, which can greatly extend the ability of representing unknown information and processing unknown information. If the q-rung orthopair fuzzy sets degenerate into to Pythagorean fuzzy sets, then the extending set measures to orthopair fuzzy sets will be generated as the extending set measures to Pythagorean fuzzy sets. Numerical examples are designed to prove the effectiveness of the proposed models and the experimental results demonstrate that the proposed method can extend the extending set measures to Pythagorean fuzzy sets to q-rung orthopair fuzzy environment successfully and solve issues of decision making under q-rung orthopair fuzzy environment effectively.

Visiting Indian Hospitals Before, During and After Covid

Abstract: The prevailing COVID-19 situation has brought in temporary and permanent changes in the attitude and lifestyle of people. Starting from Hand sanitizers and face masks, it extends to online classrooms and work from home culture. In case of visiting hospitals and medications, people with pre-existing medical conditions and minor health issues tend to delay or avoid visiting hospitals due to fear of infection, which is dangerous. Further, people or patients tend to access several alternatives and precautions. The alternatives include home remedies, ayurvedic medication, yoga and meditation. On the other hand, hospitals are trying to adapt online consulting and telemedicine. Besides, Cancellation or delay of nonemergency surgeries became inevitable in the lockdown phase. This survey conducted among the people of Erode district, Tamilnadu to study the perception of people concerning visiting hospitals for health issues. The results show that fear of infection, financial and transportation difficulties are the major factors which affected people from visiting hospital. Also, changing trends like Telemedicine and home remedies are likely to be permanently opted by people. In Brief, the outcomes reveal the changing attitude of people towards medication and hospital visiting habits.

Experiential and Stochastic Learning Algorithms Based on the Probability of a Fuzzy Event and Modified Fuzzy Metric Distance in Intelligent Robotic Part Micro-Assembly

Abstract: Experiential and stochastic learning algorithms for improving the time consumed in robot’s part micro-assembly procedures are presented. A comprehensive comparison of the results with other recent algorithms is described. The experiential learning algorithm unites the neighboring sections in a manner that is very similar to the larger united section based on the specified criterion. If the experiential learning algorithm is consecutively repeated, the micro-assembly dynamics analysis chart will be not only minimized in the number of sections, but also formed such that each united section will have one selected optimal plan with the lowest modified fuzzy metric distance within its own section. Thus, if the new input appertaining to the section is merely identified, the final selected plan within the section will execute the related task quickly and accurately thus resulting to a significant improvement in the time spent in the part insertion. Through the stochastic learning algorithm extended from the crisp set domain to the fuzzy set domain, based on the probability of a fuzzy event and the modified fuzzy metric distance, to deal well with the uncertainties arising during the micro-assembly procedure, if one specific plan among the feasible plans is successively selected to solve its confronting micro-assembly problems, the probability that the selected plan will be continuously chosen will increase as a consequence of compensation, namely, a higher value of membership degree is continuously allocated to the selected plan. As a result, the selected control plan with the highest probability of success and the lowest degree of uncertainty acquired by the stochastic learning facilitates the stabilization of the execution phase of the part insertion task. The degree of uncertainty, which is measured by a modified fuzzy metric distance and related to the task execution of the micro-assembly, is used as a criterion to select the most valid plan. The results demonstrate that the micro-assembly stuck problems can be successfully resolved by the experiential and stochastic learning algorithms.

COVID-19 Classification Using Medical Image Synthesis by Generative Adversarial Networks

Abstract: The outbreak of novel coronavirus disease 2019, also called COVID-19, in Wuhan, China, began in December 2019. Since its outbreak, infectious disease has rapidly spread across the globe. The testing methods adopted by the medical practitioners gave false negatives, which is a big challenge. Medical imaging using deep learning can be adopted to speed up the testing process and avoid false negatives. This work proposes a novel approach, COVID-19 GAN, to perform coronavirus disease classification using medical image synthesis by a generative adversarial network. Detecting coronavirus infections from the chest X-ray images is very crucial for its early diagnosis and effective treatment. To boost the performance of the deep learning model and improve the accuracy of classification, synthetic data augmentation is performed using generative adversarial networks. Here, the available COVID-19 positive chest X-ray images are fed into the styleGAN2 model. The styleGAN model is trained, and the data necessary for training the deep learning model for coronavirus classification is generated. The generated COVID-19 positive chest X-ray images and the normal chest X-ray images are fed into the deep learning model for training. An accuracy of 99.78% is achieved in classifying chest X-ray images using CNN binary classifier model.

Online Learning of Parameters for Modeling User Preference Based on Bayesian Network

Abstract: By analyzing users’ behavior data for personalized services, most state-of-the-art methods for user preference modeling are often based on batch-mode machine learning algorithms, where all rating data are assumed to be available throughout the training process. However, data in the real world often arrives sequentially and user preference may change dynamically. The real-time characteristics of rating data make the algorithms for preference modeling challenging to suit real-world online applications. By the user preference model (UPM) based on Bayesian network with a latent variable (BNLV), uncertain relationships among relevant attributes of users, objects and ratings could be represented, in which user preference is represented by the latent variable. In this paper, we propose an online approach for parameter learning of UPM. Specifically, we first extend the classic Voting EM algorithm by using Bayesian estimation in terms of the situation with latent variables. Consequently, we propose the algorithm for learning parameters of UPM from few and sequentially-changing rating data to reflect the gradually changing preferences. Finally, we test the effectiveness of our proposed algorithm by conducting experiments on various datasets. Experimental results demonstrate the superiority of our method in various measurements.

Enhanced CRNN-Based Optimal Web Page Classification and Improved Tunicate Swarm Algorithm-Based Re-Ranking

Abstract: The main intention of this paper is to develop a new intelligent framework for web page classification and re-ranking. The two main phases of the proposed model are (a) classification, and (b) re-ranking-based retrieval. In the classification phase, pre-processing is initially performed, which follows the steps like HTML (Hyper Text Markup Language) tag removal, punctuation marks removal, stop words removal, and stemming. After pre-processing, word to vector formation is done and then, feature extraction is performed by Principle Component Analysis (PCA). From this, optimal feature selection is accomplished, which is the important process for the accurate classification of web pages. Web pages contain several features, which reduces the classification accuracy. Here, the adoption of a new meta-heuristic algorithm termed Opposition based-Tunicate Swarm Algorithm (O-TSA) is employed to perform the optimal feature selection. Finally, the selected features are subjected to the Enhanced Convolutional-Recurrent Neural Network (E-CRNN) for accurate web page classification with enhancement based on O-TSA. The outcome of this phase is the categorization of different web page classes. In the second phase, the re-ranking is involved utilizing the O-TSA, which derives the objective function based on similarity function (correlation) for URL matching, which results in optimal re-ranking of web pages for retrieval. Thus, the proposed method yields better classification and re-ranking performance and reduce space requirements and search time in the web documents compared with the existing methods.

Deep Learning and Neural Network-Based Wind Speed Prediction Model

Abstract: This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural network model that plots the past spread and predicts the future stretch of the weather. The performance of the recurrent neural network model is presented and compared with Adaline neural network, Autoregressive Neural Network (NAR), and Group Method of Data Handling (GMDH). The NAR used three hidden layers. The performance of the model is analyzed by presenting the Wind Speeds of Erbil city. The dataset consists of the Wind Speed of (1992-2020) years, and each year consist of twelve months (from January to December).

Hyper Spectral Fruit Image Classification for Deep Learning Approaches and Neural Network Techniques

Abstract: In the field of agro-business technology, computerization contributes to productivity, monetary turnover of events along local viability. The interest in tariffs in addition to the consistency analysis is influenced by the mix of leafy foods. The most tangible aspect of the food derived from the earth is the implementation that influences the need for, the customer’s desires as well as the judgment of the market. Although people may plan and assess, time-concentrated, complex, subjective, costly, and handily influenced by environmental variables is problematic. Subsequently, a shrewd natural product evaluation system is needed. Deep learning has achieved remarkable milestones in the field of conventional computers. In this article, we use deep learning techniques on the topic of hyperspectral image exploration. Unlike traditional machine vision exercises, the only thing to do with a gander is the spatial setting; our proposed solution would use both the spatial setting and the phantom relationship to enhance the hyperspectral image grouping. In clear words, we endorse four new deep learning models, in particular the 3D Convolutionary Neural Network (3D-CNN) and the Repetitive 3D Convolutionary Neural Network (R-3D-CNN) for hyperspectral image recognition.

Sum of Certainties with the Product of Reasons: Neural Network with Fuzzy Aggregators

Abstract: The paper attempts to bridge the gap between widely accepted models of biological systems based on the Tsetlin automata acting in random environment and traditional artificial neural networks that consist of the McCalloch and Pitts neurons. Using recently developed algebra with uninorm and absorbing norm aggregators, we consider the neurons as extended Tsetlin automata that implement multi-valued not - xor operator applied to the aggregated inputs and internal states, and then construct the network using these neurons. The inputs of the neurons are specified by the synapses that implement multi-valued joined and and or operations. We demonstrate that for favorable (in the sense of learning) states the suggested neurons act similarly to the traditional neurons, while for unfavorable states they immediately change their activity to the reverse one. Such properties of the neurons both results in the correct activity of the network and demonstrates better correspondence with the logics of natural neural networks.

Enhancing Context-Aware Recommendation Using Hesitant Fuzzy Item Clustering by Stacked Autoencoder Based Smoothing Technique

Abstract: Context-aware recommender systems (CARS) are a key component in businesses, notably in the e-transactions domain, since they assume that reviews, ratings, demographics, and other factors may determine customer preferences. On contrary, while evaluating the sentiment underlying the reviews and the rating score, consumers’ opinion is typically conflicting. As a result, a framework that employs either a review or a rating for top-N recommendation directs to produce unsatisfied recommendations in addition to a meager rating problem and high computation time. To overcome all the problems, a novel sentiment enhanced stacked autoencoder (SSAE) with context-specific hesitant fuzzy item hierarchical clustering (CHFHC) approach is proposed which employs online and offline phases. In the offline-phase, the meager user-item rating matrix is smoothed by learning the users’ concrete preference to a complete matrix by the SSAE approach. They are clustered offline using the CHFHC approach into context-based similar item clusters. In the online-phase, the active user gets context-based recommendations from the most similar cluster that matches the active users’ current context situation. Hence the SSAE_CHFHC approach improves the quality of Top-N recommendation corresponding to the exact contextual situation of the active user with a minimal recommendation computation time. Experiments on the (5-core) Amazon and yelp datasets proved that the intended SSAE_CHFHC framework consistently outperforms state-of-the-art recommendation algorithms on a variety of evaluation measures.

SentiWordNet Ontology and Deep Neural Network Based Collaborative Filtering Technique for Course Recommendation in an E-Learning Platform

Abstract: The expansion of the population that wants to learn online is growing due to several e-learning platforms, which help innovate and suggest courses to learners. Several techniques are devised for determining optimal courses for the learner. In recent days, researchers began to utilize recommendation systems in e-learning. This paper devises a novel technique for course recommendation to students in an e-learning platform, which helps learners select the best course. Here, the Butterfly Weed Optimization (BWO) is newly devised by combining Invasive Weed Optimization (IWO) and Butterfly Optimization Algorithm (BOA). At first, the process is performed by inputting the data to the Course subscription matrix for constructing the matrix based on learner interest and courses. Here, course grouping is performed using Interval type-2 Fuzzy Local Enhancement Based Rough K-means Clustering. Furthermore, the course is matched with input data based on entropy and angular distance. Finally, the sentiment classification is performed using the Ontology-based approach SentiWordNet and Deep Neural Network (DNN). Here, the DNN is trained with the proposed BWO algorithm, and thus the course recommendation is attained by offering a suitable course recommendation to learners.

Analyzing the Impact of Lockdown in Controlling Covid-19 Spread and Future Prediction

Abstract: COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic.

A Deep Learning Based Method for Network Application Classification in Software-Defined IoT

Abstract: Network Application Classification (NAC) is a vital technology for intrusion detection, Quality-of-Service (QoS)-aware traffic engineering, traffic analysis, and network anomalies. Researchers have focused on designing algorithms using deep learning models based on statistical information to address the challenges of traditional payload and port-based traffic classification techniques. Internet of Things (IoT) and Software Defined Network (SDN) are two popular technologies nowadays and aims to connect devices over the internet and intelligently control networks from a centralized space. IoT aims to connect billions of devices; therefore, classification is essential for efficient processing. SDN is a new networking paradigm, which separates data plane measurement from the control plane. The emergence of deep learning algorithms with SDN provides a scalable traffic classification architecture. Due to the inadequate results of payload and port-based approaches, a statistical technique to classify network traffic into different classes using a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) is presented in this paper. This paper provides a classification method for software defined IoT networks. The results show that, contrary to other traffic classification methods, the proposed approach offered a better accuracy rate of over 99 %, which is promising.

Interactive Mining of Probabilistic Frequent Patterns in Uncertain Databases

Abstract: Many modern applications such as sensor networks produce probabilistic data. These data are collected into an uncertain database. To interpret uncertainty and to mine frequent patterns in an uncertain database, all possible certain databases are considered, which generates an exponential number of combinations and makes the mining problem highly complicated. In practice, mining is interactive, which makes the discovery of frequent itemsets in an uncertain database even more challenging. The objective of interactive mining is to shorten the time that is required to obtain the desired patterns in the iterated lengthy mining process. The time-consuming mining process in an uncertain database is exacerbated by repeated processing if the mining is performed from scratch. Therefore, we propose an interactive mining algorithm called iDIP to solve this problem. The iDIP algorithm adopts an approximation mechanism to mine the patterns and prunes candidates by using the existing patterns. Comprehensive experiments using both real and synthetic datasets show that iDIP outperforms the well-known re-mining-based MB algorithm for 4.3 times faster on average. In addition, iDIP has good linear scalability.

Fuzzy Solutions of the SIR Models using VIM

Abstract: The aim of this paper first presents a new solution to the SIR model with fuzzy initial value, elementary properties of this new solution are given. We study the application of variational iteration method in finding the approximate solution of SIR model with fuzzy initial value. The presented method have been applied in a direct way without linearization, disretization or perturbation. Result obtained by this method and fuzzy initial value shows that both are in excellent agreement which indicates their affectiveness and reliability.

Analysis on Prediction of Covid-19 with Machine Learning Algorithms

Abstract: During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination.

Fuzzy C-Means Clustering Algorithms with Weighted Membership and Distance

Abstract: Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature does not have studies about how they can improve the clustering quality of partitions with overlapping classes. The better the clustering quality of a partition, the better is the interpretation of the data, which is essential to understand real problems. This work proposes two robust FCM algorithms to prevent ambiguous membership into clusters. For this, we compute two types of weights: an weight to avoid the problem of overlapping clusters; and other weight to enable the algorithm to identify clusters of different shapes. We perform a study with synthetic datasets, where each one contains classes of different shapes and different degrees of overlapping. Moreover, the study considered real application datasets. Our results indicate such weights are effective to reduce the ambiguity of membership assignments thus generating a better data interpretation.

LR Mixed Fuzzy Random Portfolio Choice Based on the Risk Curve

Abstract: In this paper, finding the optimal LR mixed fuzzy random portfolio that is a portfolio that contains some candidate securities with random return rates and some securities with LR fuzzy return rates is investigated. Besides, it is done by a new criterion that as a risk measure provides information about all the likely losses, and at any level of loss, the investor will be able to choose the maximum of her or his tolerable loss. And in the end, a case study of the Tehran stock exchange is presented for the sake of illustration.

A Novel Defuzzification Approach of Ranking Parametric Fuzzy Numbers Based on the Value and Ambiguity Calculated at Decision Levels

Abstract: Generally, in every decision-making process under the fuzzy domain, ranking of fuzzy numbers is indispensable. Although such approaches are abundant, yet a universally accepted approach is not apparent. Hence, newer methodologies have been developed since its inception. In many instances, defuzzification techniques are being criticized as these methodologies are based on intuition and the geometry of the fuzzy numbers. Furthermore, in many instances the reasonable properties that a ranking method should follow are not being verified. However, in the current study, a novel defuzzification technique is being developed with the notion of value and multiple of a ambiguity inclusion-exclusion function, [Formula: see text], with ambiguity at various decision levels. The current method perfectly obeys the intuition and the geometry of the fuzzy numbers. Adding to this, it should be emphasized that the current methodology follows all the reasonable properties of a ranking method. Furthermore, new properties are stated and proved which illustrate the novelty of the present method. Furthermore, the shortcomings and drawbacks of the existing methods are overcome by the current methodology. Noteworthy, the current method ranks the fuzzy numbers and their corresponding images consistently, which was not evident in most of the existing methods.

A Modified Deep Convolution Siamese Network for Writer-Independent Signature Verification

Abstract: In this paper problem of offline signature verification has been discussed with a novel high-performance convolution Siamese network. The paper proposes modifications in the already existing convolution Siamese network. The proposed method makes use of the Batch Normalization technique instead of Local Response Normalization to achieve better accuracy. The regularization factor has been added in the fully connected layers of the convolution neural network to deal with the problem of overfitting. Apart from this, a wide range of learning rates are provided during the training of the model and optimal one having the least validation loss is used. To evaluate the proposed changes and compare the results with the existing solution, our model is validated on three benchmarks datasets viz. CEDAR, BHSig260, and GPDS Synthetic Signature Corpus. The evaluation is done via two methods firstly by Test-Train validation and then by K-fold cross-validation (K = 5), to test the skill of our model. We show that the proposed modified Siamese network outperforms all the prior results for offline signature verification. One of the major advantages of our system is its capability of handling an unlimited number of new users which is the drawback of many research works done in the past.

Performance Metrics on Hyperspectral Images in Fuzzy Contextual Convolutional Neural Network for Food Quality Analyzer

Abstract: The quality of food and the safety of consumer is one of the major essential things in our day-to-day life. To ensure the quality of foods through their various attributes, different types of methods have been introduced. In this proposed method, three underlying blocks namely Hyperspectral Food Image Context Extractor (HFICE), Hyperspectral Context Fuzzy Classifier (HCFC) and Convolutional Neural Network (CNN) for Food Quality Analyzer (CFQA). Hyperspectral Food Image Context Extractor module is used as the preprocess to get food attributes such as texture, color, size, shape and molecular particulars. Hyperspectral Context Fuzzy Classifier module identifies a particular part of the food (zone entity) is whether carbohydrate, fat, protein, water or unusable core. CNN for Food Quality Analyzer module uses a Tuned Convolutional Layer, Heuristic Activation Operation, Parallel Element Merge Layer and a regular fully connected layer. Indian Pines, Salinas and Pavia are the benchmark dataset to evaluate hyperspectral image-based machine learning procedures. These datasets are used along with a dedicated chicken meat Hyper Spectral Imaging dataset is used in the training and testing process. Results are obtained that about 7.86% of average values in various essential evaluation metrics such as performance metrics such as accuracy, precision, sensitivity and specificity have improved when compared to existing state of the art results.

Graph-Based Text Summarization and Its Application on COVID-19 Twitter Data

Abstract: Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.

Detection of COVID-19 Cases from Chest X-Rays using Deep Learning Feature Extractor and Multilevel Voting Classifier

Abstract: Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.

3-Tuple Linguistic Distance-Based Model for a New Product go/no-go Evaluation

Abstract: There is a need for a probabilistic linguistic term set model for go/no-go product screening problem for new product development to meet a firm’s expectation. This paper develops a novel 3-tuple linguistic distance-based model to evaluate whether an overall respondents perception meets a firm’s expectation (“go”) for new product development. The respondent’s perception is collected by a Kansei-based survey as an interval-linguistic term. Then, an expected distance between the firm’s expectation and the respondent’s perception is computed by a target-based Manhattan distance measure. The expected distance is compared with a threshold to shows that what product attribute meets the firm’s expectation based on customers’ perceptions. A real case study of Thai-tea soy milk packaging design is provided. The proposed model is compared to the existing model to show its effectiveness and applicability. Experimental results show that the proposed model can effectively point out the inferior product attributes, which leads to redesign the product until all product concepts meet the target attributes before launching the product to the market. Thus, it can significantly reduce the risk of failure of the product in a real market. This paper has significant contributions in that it allows respondents to provide their opinions with uncertainty by providing an interval linguistic assessment, handles a bias of the heterogeneity of respondents by determining the weight of respondents, and overcomes limitations of existing models by applying target-oriented linguistic terms.