Search results for: maturity classification

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Michelle Sánchez de León Brajkovich, Nuria Martí Audi

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The envelope is one of the most important elements when one analyzes the operation of the building in terms of sustainability. Taking this into consideration, this research focuses on setting a classification system of the envelopes opaque systems, crossing the knowledge and parameters of construction systems with requirements in terms of sustainability that they may have, to have a better understanding of how these systems work with respect to their sustainable contribution to the building. Therefore, this paper evaluates the importance of the envelope design on the building sustainability. It analyses the parameters that make the construction systems behave differently in terms of sustainability. At the same time it explains the classification process generated from this analysis that results in a classification where all opaque vertical envelope construction systems enter.

Keywords: sustainable, exterior walls, envelope, facades, construction systems, energy efficiency

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Eva Šmelová, Alena Berčíková

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The issue of school maturity and readiness of a child for a successful start of compulsory education is one of the long-term monitored areas, especially in the context of education and psychology. In the context of the curricular reform in the Czech Republic, the issue has recently gained importance. Analyses of research in this area suggest a lack of a broader overview of indicators informing about the current level of children’s school maturity and school readiness. Instead, various studies address partial issues. Between 2009 and 2013 a research study was performed at the Faculty of Education, Palacký University Olomouc (Czech Republic) focusing on children’s maturity and readiness for compulsory education. In this study, social skills were of marginal interest; the main focus was on the mental area. This previous research is smoothly linked with the present study, the objective of which is to identify the level of school maturity and school readiness in selected characteristics of social skills as part of the adaptation process after enrolment in compulsory education. In this context, the following research question has been formulated: During the process of adaptation to the school environment, which social skills are weakened? The method applied was observation, for the purposes of which the authors developed a research tool – record sheet with 11 items – social skills that a child should have by the end of preschool education. The items were assessed by first-grade teachers at the beginning of the school year. The degree of achievement and intensity of the skills were assessed for each child using an assessment scale. In the research, the authors monitored a total of three independent variables (gender, postponement of school attendance, participation in inclusive education). The effect of these independent variables was monitored using 11 dependent variables. These variables are represented by the results achieved in selected social skills. Statistical data processing was assisted by the Computer Centre of Palacký University Olomouc. Statistical calculations were performed using SPSS v. 12.0 for Windows and STATISTICA: StatSoft STATISTICA CR, Cz (software system for data analysis). The research sample comprised 115 children. In their paper, the authors present the results of the research and at the same time point to possible areas of further investigation. They also highlight possible risks associated with weakened social skills.

Keywords: compulsory education, curricular reform, educational diagnostics, pupil, school curriculum, school maturity, school readiness, social skills

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Authors: Bharatendra Rai

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The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Beenish Urooj, Ubaid Ullah, Sidra Riasat

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National Testing Service-Pakistan (NTS) is an agency in Pakistan that conducts student success appraisal examinations. In this research paper, we must present a security model for the NTS organization. The security model will depict certain security countermeasures for a better defense against certain types of breaches and system malware. We will provide a security roadmap, which will help the company to execute its further goals to maintain security standards and policies. We also covered multiple aspects in securing the environment of the organization. We introduced the processes, architecture, data classification, auditing approaches, survey responses, data handling, and also training and awareness of risk for the company. The primary contribution is the Risk Survey, based on the maturity model meant to assess and examine employee training and knowledge of risks in the company's activities.

Keywords: NTS, risk assessment, threat factors, security, services

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Abdelkhalek Bakkari

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Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

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Authors: M. Safa, A. Sabet, S. MacGillivray, M. Davidson, K. Kaczmarczyk, C. T. Haas, G. E. Gibson, D. Rayside

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To address construction project requirements and specifications, scholars and practitioners need to establish a taxonomy according to a scheme that best fits their need. While existing characterization methods are continuously being improved, new ones are devised to cover project properties which have not been previously addressed. One such method, the Project Definition Rating Index (PDRI), has received limited consideration strictly as a classification scheme. Developed by the Construction Industry Institute (CII) in 1996, the PDRI has been refined over the last two decades as a method for evaluating a project's scope definition completeness during front-end planning (FEP). The main contribution of this study is a review of practical project classification methods, and a discussion of how PDRI can be used to classify projects based on their readiness in the FEP phase. The proposed model has been applied to 59 construction projects in Ontario, and the results are discussed.

Keywords: project classification, project definition rating index (PDRI), risk, project goals alignment

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Authors: Nadeem Munawar, Iftikhar Hussain, Tariq Mahmood

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The lesser bandicoot rat (Bandicota bengalensis) is widely distributed and a serious agricultural pest in Pakistan. It has wide adaptation with rice-wheat-sugarcane cropping systems of Punjab, Sindh and Khyber Pakhtunkhwa and wheat-groundnut cropping system of Pothwar area, thus inflicting heavy losses to these crops. Comparative efficacies of four food baits (onion, guava, potato and peanut butter smeared bread/Chapatti) were tested in multiple feeding tests for kill trapping of this rat species in the Pothwar Plateau between October 2013 to July 2014 at the sowing, tilling, flowering and maturity stages of wheat, groundnut and millet crops. The results revealed that guava was the most preferred bait as compared to the rest of three, presumably due to particular taste and smell of the guava. The relative efficacies of all four tested baits guava also scoring the highest trapping success of 16.94 ± 1.42 percent, followed by peanut butter, potato, and onion with trapping successes of 10.52 ± 1.30, 7.82 ± 1.21 and 4.5 ± 1.10 percent, respectively. In various crop stages and season-wise the highest trapping success was achieved at maturity stages of the crops, presumably due to higher surface activity of the rat because of favorable climatic conditions, good shelter, and food abundance. Moreover, the maturity stage of wheat crop coincided with spring breeding season and maturity stages of millet and groundnut match with monsoon/autumn breeding peak of the lesser bandicoot rat in Pothwar area. The preferred order among four baits tested was guava > peanut butter > potato > onion. The study recommends that the farmers should periodically carry out rodent trapping at the beginning of each crop season and during non-breeding seasons of this rodent pest when the populations are low in numbers and restricted under crop boundary vegetation, particularly during very hot and cold months.

Keywords: Bandicota bengalensis, efficacy, food baits, Pothwar

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Authors: Ouafae Baida, Najma Hamzaoui, Maha Akbib, Abdelfettah Sedqui, Abdelouahid Lyhyaoui

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Numerous scientific works present various methods to analyze the data for several domains, specially the comparison of classifications. In our recent work, we presented a new approach to help the user choose the best classification method from the results obtained by every method, by basing itself on the distances between the trees of classification. The result of our approach was in the form of a dendrogram contains methods as a succession of connections. This approach is much needed in phylogeny analysis. This discipline is intended to analyze the sequences of biological macro molecules for information on the evolutionary history of living beings, including their relationship. The product of phylogeny analysis is a phylogenetic tree. In this paper, we recommend the use of a new method of construction the phylogenetic tree based on comparison of different classifications obtained by different molecular genes.

Keywords: hierarchical classification, classification methods, structure of tree, genes, phylogenetic analysis

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for the balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.

Keywords: power spectral density, 3D EEG model, brain balancing, kNN

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Authors: Temitope L. Baiyegunhi, Kuiwu Liu, Oswald Gwavava, Christopher Baiyegunhi

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Shale gas has recently been the exploration focus for future energy resource in South Africa. Specifically, the black shales of the lower Ecca Group in the study area are considered to be one of the most prospective targets for shale gas exploration. Evaluation of this potential resource has been restricted due to the lack of exploration and scarcity of existing drill core data. Thus, only limited previous geochemical data exist for these formations. In this study, outcrop and core samples of the Ecca Group were analysed to assess their total organic carbon (TOC), organic matter type, thermal maturity and hydrocarbon generation potential (SP). The results show that these rocks have TOC ranging from 0.11 to 7.35 wt.%. The SP values vary from 0.09 to 0.53 mg HC/g, suggesting poor hydrocarbon generative potential. The plot of S1 versus TOC shows that the source rocks were characterized by autochthonous hydrocarbons. S2/S3 values range between 0.40 and 7.5, indicating Type- II/III, III, and IV kerogen. With the exception of one sample from the collingham formation which has HI value of 53 mg HC/g TOC, all other samples have HI values of less than 50 mg HC/g TOC, thus suggesting Type-IV kerogen, which is mostly derived from reworked organic matter (mainly dead carbon) with little or no potential for hydrocarbon generation. Tmax values range from 318 to 601℃, indicating immature to over-maturity of hydrocarbon. The vitrinite reflectance values range from 2.22 to 3.93%, indicating over-maturity of the kerogen. Binary plots of HI against OI and HI versus Tmax show that the shales are of Type II and mixed Type II-III kerogen, which are capable of generating both natural gas and minor oil at suitable burial depth. Based on the geochemical data, it can be inferred that the source rocks are immature to over-matured variable from localities and have potential of producing wet to dry gas at present-stage. Generally, the Whitehill formation of the Ecca Group is comparable to the Marcellus and Barnett Shales. This further supports the assumption that the Whitehill Formation has a high probability of being a profitable shale gas play, but only when explored in dolerite-free area and away from the Cape Fold Belt.

Keywords: source rock, organic matter type, thermal maturity, hydrocarbon generation potential, Ecca Group

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: data envelopment analysis, interval DEA, efficiency classification, efficiency prediction

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Nasar Aldian Ambark Shashoa

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Classification with a dimension reduction based on Bayesian approach is proposed in this paper . The first step is to generate a sample (parameter) of fault-free mode class and faulty mode class. The second, in order to obtain good classification performance, a selection of important features is done with the discrete karhunen-loeve expansion. Next, the Bayes test for minimum error is used to classify the classes. Finally, the results for simulated data demonstrate the capabilities of the proposed procedure.

Keywords: analytical redundancy, fault detection, feature extraction, Bayesian approach

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Authors: Yaser Miaji, Mohammed Aloryani

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The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: Angie Al Habib, Khalid Al Siyabi

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To measure or asses any government’s efficiency we need to measure the performance of this government in regards to the quality of the service it provides. Using a technological platform in service provision became a trend and a public demand. It is also a public need to make sure these services are aligned to values and to the whole government’s strategy, vision and goals as well. Providing services using technology tools and channels can enhance the internal business process and also help establish many essential values to government services like transparency and excellence, since in order to establish e-services many standards and policies must be put in place to enable the handing over of decision making to a mature system oriented mechanism. There was no doubt that the Sultanate of Oman wanted to enhance its services and move it towards automation and establishes a smart government as well as links its services to life events. Measuring government efficiency is very essential in achieving social security and economic growth, since it can provide a clear dashboard of all projects and improvements. Based on this data we can improve the strategies and align the country goals to them.

Keywords: government, maturity, Oman, performance, service

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Authors: Dechen Tshering Vogel, Johannes T. Heverhagen, Bernard Kiss, Spyridon Arampatzis

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In addition to computed tomography (CT), contrast enhanced ultrasound (CEUS), and magnetic resonance imaging (MRI) are being increasingly used for imaging of renal lesions. The aim of this prospective study was to compare the classification of complex cystic renal lesions using the Bosniak classification with CEUS and MRI to CT. Forty-eight patients with 65 cystic renal lesions were included in this study. All participants signed written informed consent. The agreement between the Bosniak classifications of complex renal lesions ( ≥ BII-F) on CEUS and MRI were compared to that of CT and were tested using Cohen’s Kappa. Sensitivity, specificity, positive and negative predictive values (PPV/NPV) and the accuracy of CEUS and MRI compared to CT in the detection of complex renal lesions were calculated. Twenty-nine (45%) out of 65 cystic renal lesions were classified as complex using CT. The agreement between CEUS and CT in the classification of complex cysts was fair (agreement 50.8%, Kappa 0.31), and was excellent between MRI and CT (agreement 93.9%, Kappa 0.88). Compared to CT, MRI had a sensitivity of 96.6%, specificity of 91.7%, a PPV of 54.7%, and an NPV of 54.7% with an accuracy of 63.1%. The corresponding values for CEUS were sensitivity 100.0%, specificity 33.3%, PPV 90.3%, and NPV 97.1% with an accuracy 93.8%. The classification of complex renal cysts based on MRI and CT scans correlated well, and MRI can be used instead of CT for this purpose. CEUS can exclude complex lesions, but due to higher sensitivity, cystic lesions tend to be upgraded. However, it is useful for initial imaging, for follow up of lesions and in those patients with contraindications to CT and MRI.

Keywords: Bosniak classification, computed tomography, contrast enhanced ultrasound, cystic renal lesions, magnetic resonance imaging

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Souhir Chelly, Chahida Harizi, Aicha Hechaichi, Sihem Aissaoui, Leila Ben Ayed, Maha Bergaoui, Mohamed Kouni Chahed

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Introduction: The International Classification of Primary Care (ICPC) is part of the morbidity classification system. It had 17 chapters, and each is coded by an alphanumeric code: the letter corresponds to the chapter, the number to a paragraph in the chapter. The objective of this study is to show the utility of this classification in the coding of the reasons for demand for care in Primary health care (PHC), its advantages and limits. Methods: This is a cross-sectional descriptive study conducted in 4 PHC in Ariana district. Data on the demand for care during 2 days in the same week were collected. The coding of the information was done according to the CISP. The data was entered and analyzed by the EPI Info 7 software. Results: A total of 523 demands for care were investigated. The patients who came for the consultation are predominantly female (62.72%). Most of the consultants are young with an average age of 35 ± 26 years. In the ICPC, there are 7 rubrics: 'infections' is the most common reason with 49.9%, 'other diagnoses' with 40.2%, 'symptoms and complaints' with 5.5%, 'trauma' with 2.1%, 'procedures' with 2.1% and 'neoplasm' with 0.3%. The main advantage of the ICPC is the fact of being a standardized tool. It is very suitable for classification of the reasons for demand for care in PHC according to their specificity, capacity to be used in a computerized medical file of the PHC. Its current limitations are related to the difficulty of classification of some reasons for demand for care. Conclusion: The ICPC has been developed to provide healthcare with a coding reference that takes into account their specificity. The CIM is in its 10th revision; it would gain from revision to revision to be more efficient to be generalized and used by the teams of PHC.

Keywords: international classification of primary care, medical file, primary health care, Tunisia

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Authors: B. S. Harish, M. B. Revanasiddappa

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Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

Abstract:

Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

Abstract:

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

Procedia PDF Downloads 379