International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

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.

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.

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.

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).

Recently, it has been proven that the emotional aspect directly influences the learning process, so that, based on data mining techniques, this behavior has been sought to be characterized. This has made clustering techniques become one of the most used techniques for this purpose. However, studies where emotional data obtained from a person’s brain activity are used, are rare. For this reason, the present study aims to implement and compare advanced clustering techniques based on emotional metrics obtained through Brain-Computer Interfaces, captured in an AR-Sandbox, which fulfills the role of a learning environment. The evaluation of these techniques is carried out using internal criteria such as silhouette coefficient, Composed Density Between and within, Calinski-Harabasz and other statistical measures. When carrying out this study, it was obtained as a result that, the Density-Based Spatial Clustering of Application with Noise and Density-Based Hierarchical Spatial Clustering of Noisy Applications algorithms as the Density-based clustering methods, presented a better level of well-separation, cohesion and compaction, in comparison to the rest of the techniques implemented.

Leadership Education Development (LED) teaching results in criticism and debate in the learning atmosphere for students. The significant challenges in LED are analyzing how research can successfully be applied to benefit the students’ teaching, the political essence of education, identifying education research as a science, and the dislocation between educational research and education practice. Teaching and research are two critical ties to tertiary education programs. Two psychological tools have been built from archived research data and show how this form of reuse allows pedagogue to directly link research and teaching. This manuscript proposed an Optimized Learning Strategy (OLS) to improve curriculum development. OLS provides strategies for learning that increase learning ability, learning experience, boost understanding, and connect with previous knowledge of new information. This paper presents the real practice of the development of LED teaching programming. Finally, the results show the students highly recommended OLS based on case study analysis to measure teaching and learning methods.

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.

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.

In the present times, artificial-intelligence based techniques are considered as one of the prominent ways to classify images which can be conveniently leveraged in the real-world scenarios. This technology can be extremely beneficial to the lepidopterists, to assist them in classification of the diverse species of Rhopalocera, commonly called as butterflies. In this article, image classification is performed on a dataset of various butterfly species, facilitated via the feature extraction process of the Convolutional Neural Network (CNN) along with leveraging the additional features calculated independently to train the model. The classification models deployed for this purpose predominantly include K-Nearest Neighbors (KNN), Random Forest and Support Vector Machine (SVM). However, each of these methods tend to focus on one specific class of features. Therefore, an ensemble of multiple classes of features used for classification of images is implemented. This research paper discusses the results achieved from the classification performed on basis of two different classes of features i.e., structure and texture. The amalgamation of the two specified classes of features forms a combined data set, which has further been used to train the Growing Convolutional Neural Network (GCNN), resulting in higher accuracy of the classification model. The experiment performed resulted in promising outcomes with TP rate, FP rate, Precision, recall and F-measure values as 0.9690, 0.0034, 0.9889, 0.9692 and 0.9686 respectively. Furthermore, an accuracy of 96.98% was observed by the proposed methodology.

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.

In recent times, bacterial Antimicrobial Resistance (AMR) analyses becomes a hot study topic. The AMR comprises information related to the antibiotic product name, class name, subclass name, type, subtype, gene type, etc., which can fight against the illness. However, the tagging language used to determine the data is of free context. These contexts often contain ambiguous data, which leads to a hugely challenging issue in retrieving, organizing, merging, and finding the relevant data. Manually reading this text and labelling is not time-consuming. Hence, topic modeling overcomes these challenges and provides efficient results in categorizing the topic and in determining the data. In this view, this research work designs an ensemble of artificial intelligence for categorizing the AMR gene data and determine the relationship between the antibiotics. The proposed model includes a weighted voting based ensemble model by the incorporation of Latent Dirichlet Allocation (LDA) and Hierarchical Recurrent Neural Networks (HRNN), shows the novelty of the work. It is used for determining the amount of “topics” that cluster utilizing a multidimensional scaling approach. In addition, the proposed model involves the data pre-processing stage to get rid of stop words, punctuations, lower casing, etc. Moreover, an explanatory data analysis uses word cloud which assures the proper functionality and to proceed with the model training process. Besides, three approaches namely perplexity, Harmonic mean, and Random initialization of K are employed to determine the number of topics. For experimental validation, an openly accessible Bacterial AMR reference gene database is employed. The experimental results reported that the perplexity provided the optimal number of topics from the AMR gene data of more than 6500 samples. Therefore, the proposed model helps to find the appropriate antibiotic for bacterial and viral spread and discover how to increase the proper antibiotic in human bodies