Search results for: mineral potential classification

Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Krasimira Georgieva, Yordan Denev

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Many of thermosetting resins have application only in filled state, reinforced with different mineral fillers. The co-filling of polymers with mineral filler and gases creates a possibility for production of polymer composites materials with low density. This processing leads to forming of new materials – gas-filled plastics (polymer foams). The properties of these materials are determined mainly by the shape and size of internal structural elements (pores). The interactions on the phase boundaries have influence on the materials properties too. In the present work, the gas-filled urea-formaldehyde resins were reinforced by waste phosphogypsum. The waste phosphogypsum (CaSO₄.2H₂O) is a solid by-product in wet phosphoric acid production processes. The values of the interactions polymer-filler were increased by using two modifying agents: polyvinyl acetate for polymer matrix and sodium metasilicate for filler. Technological methods for gas-filling and recipes of urea-formaldehyde based materials with apparent density 20-120 kg/m³ were developed. The heat conductivity of the samples is between 0.024 and 0.029 W/m^oK. Tensile analyses were carried out at 10 and 50% deformation and show values 0.01-0.14 MPa and 0.01-0.09 MPa, respectively. The apparent density of obtained materials is between 20 and 92 kg/m³. The changes in the tensile properties and density of these materials according to sodium metasilicate content were studied too. The mechanism of phosphogypsum adsorption modification was studied using methods of FT-IR spectroscopy. The structure of the gas-filled urea-formaldehyde resins was described by results of electron scanning microscopy at three different magnification ratios – x50, x150 and x 500. The aim of present work is to study the possibility of the usage of phosphogypsum as mineral filler for urea-formaldehyde resins and development of a technology for the production of gas-filled reinforced polymer composite materials. The structure and the properties of obtained composite materials are suitable for thermal and sound insulation applications.

Keywords: urea formaldehyde resins, gas-filled thermostes, phosphogypsum, mechanical properties

Procedia PDF Downloads 92

Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Devadrita Dey Sarkar

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Regardless of the many technologic advances in the past decade, increased training and experience, and the obvious benefits of uniform standards, the false-negative rate in screening mammography remains unacceptably high .A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this abstract which employs features extracted by a new technique based on independent component analysis. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral breast images has the potential to improve the overall performance in the detection of breast lumps. Because breast lumps can be detected reliably by computer on lateral breast mammographs, radiologists’ accuracy in the detection of breast lumps would be improved by the use of CAD, and thus early diagnosis of breast cancer would become possible. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for breast CAD may include the computerized detection of breast nodules, as well as the computerized classification of benign and malignant nodules. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with these CAD systems, which would be reliable and useful method for quantifying the similarity of a pair of images for visual comparison by radiologists.

Keywords: CAD(computer-aided design), lesions, neural network, ROS(region of suspicion)

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Authors: Zdenek Silar, Martin Dobrovolny

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This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

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Authors: Sajjad Negahban, Ruihe Wang, Baojiang Sun

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Gas-lift dual gradient drilling is a solution for deepwater drilling challenges. As well, Continuous development of drilling technology leads to increase employment of mineral oil based drilling fluids and synthetic-based drilling fluids, which have adequate characteristics such as: high rate of penetration, lubricity, shale inhibition and low toxicity. The paper discusses utilization of nitrified mineral oil base drilling for deepwater drilling and for more accurate prediction of pressure in DGD at marine riser, solubility and bubble pressure were considered in steady state hydraulic model. The Standing bubble pressure and solubility correlations, and two models which were acquired from experimental determination were applied in hydraulic model. The effect of the black oil correlations, and new solubility and bubble pressure models was evaluated on the PVT parameters such as oil formation volume factor, density, viscosity, volumetric flow rate. Eventually, the consequent simulated pressure profile due to these models was presented.

Keywords: solubility, bubble pressure, gas-lift dual gradient drilling, steady state hydraulic model

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: M. Abdullah, Al-Wadud

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Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian, based classification, activity recognition, sensor data, object-usage model

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Authors: Carlos Arias-Alcaide, Cristina Soguero-Ruiz, Paloma Santos-Álvarez, Adrián García-Romero, Inmaculada Mora-Jiménez

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Analysing clinical data with computers in such a way that have an impact on the practitioners’ workflow is a challenge nowadays. This paper provides a first approach for monitoring the health status of COVID-19 patients through the use of some biomarkers (blood tests) and the simplest Naïve Bayes classifier. Data of two Spanish hospitals were considered, showing the potential of our approach to estimate reasonable posterior probabilities even some days before the event.

Keywords: Bayesian model, blood biomarkers, classification, health tracing, machine learning, posterior probability

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Authors: Soumaya Mrabet, Imen Akkari, Amira Atig, Elhem Ben Jazia

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Introduction: Celiac disease (CD) is a chronic autoimmune inflammatory enteropathy caused by gluten. The clinical presentation is very variable. Malabsorption in the MC is responsible for an alteration of the bone metabolism. Our purpose is to study the osteoarticular manifestations related to this condition. Material and methods: It is a retrospective study of 41 cases of CD diagnosed on clinical, immunological, endoscopic and histological arguments, in the Internal Medicine and Gastroenterology Department of Farhat Hached Hospital between September 2005 and January 2016. Results: Osteoarticular manifestations were found in 9 patients (22%) among 41 patients presenting CD. These were 7 women and 2 men with an average age of 35.7 years (25 to 67 years). These manifestations were revelatory of CD in 3 cases. Abdominal pain and diarrhea were present in 6 cases. Inflammatory polyarthralgia of wrists and knees has been reported in 7 patients. Mechanical mono arthralgia was noted in 2 patients. Biological tests revealed microcytic anemia by iron deficiency in 7 cases, hypocalcemia in 5 cases, Hypophosphatemia in 3 cases and elevated alkaline phosphatases in 3 cases. Upper gastrointestinal endoscopy with duodenal biopsy found villous atrophy in all cases. In immunology, Anti-transglutaminase antibodies were positive in all patients, Anti-endomysium in 7 cases. Measurement of bone mineral density (BMD) by biphotonic X-ray absorptiometer with evaluation of the T-score and the Z-score was performed in Twenty patients (48.8%). It was normal in 7 cases (33%) and showed osteopenia in 5 patients (25%) and osteoporosis in 2 patients (10%). All patients were treated with a Gluten-free diet associated with vitamin D and calcium substitution in 5 cases. The evolution was favorable in all cases with reduction of bone pain and normalization of the phosphocalcic balance. Conclusion: The bone impact of CD is frequent but often asymptomatic. Patients with CD should be evaluated by the measurement of bone mineral density and monitored for calcium and vitamin D deficiencies.

Keywords: bone mineral density, celiac disease, osteoarticular manifestations, vitamin D and calcium

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Authors: M. Sanjarani, A. Danehkar, A. Mashincheyan, A. H. Javid, S. M. R. Fatemi

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The oil spill in marine water has direct impact on coastal resources and community. Environmental Sensitivity Index (ESI) map is the first step to assess the potential impact of an oil spill and minimize the damage of coastal resources. In order to create Environmental Sensitivity Maps for the Chabahar bay (Iran), information has been collected in three different layers (Shoreline Classification, Biological and Human- uses resources) by means of field observations and measurements of beach morphology, personal interviews with professionals of different areas and the collection of bibliographic information. In this paper an attempt made to prepare an ESI map for sensitivity to oil spills of Chabahar bay coast. The Chabahar bay is subjected to high threaten to oil spill because of port, dense mangrove forest,only coral spot in Oman Sea and many industrial activities. Mapping the coastal resources, shoreline and coastal structures was carried out using Satellite images and GIS technology. The coastal features classified into three major categories as: Shoreline Classification, Biological and Human uses resources. The important resources classified into mangrove, Exposed tidal flats, sandy beach, etc. The sensitivity of shore was ranked as low to high (1 = low sensitivity,10 = high sensitivity) based on geomorphology of Chabahar bay coast using NOAA standards (sensitivity to oil, ease of clean up, etc). Eight ESI types were found in the area namely; ESI 1A, 1C, 3A, 6B, 7, 8B,9A and 10D. Therefore, in the study area, 50% were defined as High sensitivity, less than 1% as Medium, and 49% as low sensitivity areas. The ESI maps are useful to the oil spill responders, coastal managers and contingency planners. The overall ESI mapping product can provide a valuable management tool not only for oil spill response but for better integrated coastal zone management.

Keywords: ESI, oil spill, GIS, Chabahar Bay, Iran

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Authors: Serge Provost, Yishan Zang

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Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.

Keywords: arctangent transformation, tail classification, heavy-tailed distributions, distributional moments

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Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 192

Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Nazmul Shahadat, Anthony S. Maida

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In recent years, hypercomplex inspired neural networks improved deep CNN architectures due to their ability to share weights across input channels and thus improve cohesiveness of representations within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention ResNet with their quaternion variants that use cross-channel weight sharing to assess the effect on image classification. We expect the quaternion enhancements to produce improved feature maps with more interlinked representations. We experiment with the stem of the network, the bottleneck layer, and the fully connected backend by replacing them with quaternion versions. These modifications lead to novel architectures which yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.

Keywords: axial attention, representational networks, weight sharing, cross-channel correlations, quaternion-enhanced axial attention, deep networks

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Authors: Amritpal Singh Nafria, Rohit Sharma, Md. Shami Ansari

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Up to now only five different equations of motion can be derived from velocity time graph without needing to know the normal and frictional forces acting at the point of contact. In this paper we obtained all possible requisite conditions to be considering an equation as an equation of motion. After that we classified equations of motion by considering two equations as fundamental kinematical equations of motion and other three as additional kinematical equations of motion. After deriving these five equations of motion, we examine the easiest way of solving a wide variety of useful numerical problems. At the end of the paper, we discussed the importance and educational benefits of classification of equations of motion.

Keywords: velocity-time graph, fundamental equations, additional equations, requisite conditions, importance and educational benefits

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Authors: Jerzy Banski

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Small towns represent a key element of settlement structure and serve a number of important functions associated with the servicing of rural areas that surround them. It is in light of this that scientific studies have paid considerable attention to the functional structure of centers of this kind, as well as the relationships with both surrounding rural areas and other urban centers. But a preliminary to such research has typically involved attempts at classifying the urban centers themselves, with this also assisting with the planning and shaping of development policy on different spatial scales. The purpose of the work is to test out the methods underpinning three different classifications of small urban centers, as well as to offer a preliminary interpretation of the outcomes obtained. Research took in 722 settlement units in Poland, granted town rights and populated by fewer than 20,000 inhabitants. A morphologically-based classification making reference to the database of topographic objects as regards land cover within the administrative boundaries of towns and cities was carried out, and it proved possible to distinguish the categories of “housing-estate”, industrial and R&R towns, as well as towns characterized by dichotomy. Equally, a functional/morphological approach taken with the same database allowed for the identification – via an alternative method – of three main categories of small towns (i.e., the monofunctional, multifunctional or oligo functional), which could then be described in far greater detail. A third, multi-criterion classification made simultaneous reference to the conditioning of a structural, a location-related, and an administrative hierarchy-related nature, allowing for distinctions to be drawn between small towns in 9 different categories. The results obtained allow for multifaceted analysis and interpretation of the geographical differentiation characterizing the distribution of Poland’s urban centers across space in the country.

Keywords: small towns, classification, local planning, Poland

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Authors: S. M. Ishtiaque, V. K. Yadav, S. D. Joshi, J. K. Chatterjee

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The DIAMETRIC FAULTS system has been developed that captures a bi-directional image of yarn continuously in sequentially manner and provides the detailed classification of faults. A novel mathematical framework developed on the acquired bi-directional images forms the basis of fault classification in four broad categories, namely, Thick1, Thick2, Thin and Normal Yarn. A discretised version of Radon transformation has been used to convert the bi-directional images into one-dimensional signals. Images were divided into training and test sample sets. Karhunen–Loève Transformation (KLT) basis is computed for the signals from the images in training set for each fault class taking top six highest energy eigen vectors. The fault class of the test image is identified by taking the Euclidean distance of its signal from its projection on the KLT basis for each sample realization and fault class in the training set. Euclidean distance applied using various techniques is used for classifying an unknown fault class. An accuracy of about 90% is achieved in detecting the correct fault class using the various techniques. The four broad fault classes were further sub classified in four sub groups based on the user set boundary limits for fault length and fault volume. The fault cross-sectional area and the fault length defines the total volume of fault. A distinct distribution of faults is found in terms of their volume and physical dimensions which can be used for monitoring the yarn faults. It has been shown from the configurational based characterization and classification that the spun yarn faults arising out of mass variation, exhibit distinct characteristics in terms of their contours, sizes and shapes apart from their frequency of occurrences.

Keywords: Euclidean distance, fault classification, KLT, Radon Transform

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Authors: Amal Abdelhadi

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Consumer misbehavior evaluation can be markedly different based on a number of variables and different from one environment to another. Using three aberrant consumer behavior (ACB) scenarios (shoplifting, stealing from hotel rooms and software piracy) this study aimed to explore Libyan seller and consumers of ACB. Materials were collected by using a multi-method approach was employed (qualitative and quantitative approaches) in two fieldwork phases. In the phase stage, a qualitative data were collected from 26 Libyan sellers’ by face-to-face interviews. In the second stage, a consumer survey was used to collect quantitative data from 679 Libyan consumers. This study found that the consumer’s and seller’s evaluation of ACB are not always consistent. Further, ACB evaluations differed based on the form of ACB. Furthermore, the study found that not all consumer behaviors that were considered as bad behavior in other countries have the same evaluation in Libya; for example, software piracy. Therefore this study suggested a newly developed classification of ACB based on marketers’ and consumers’ views. This classification provides 9 ACB types within two dimensions (marketers’ and consumers’ views) and three degrees of behavior evaluation (good, acceptable and misbehavior).

Keywords: aberrant consumer behavior, Libya, multi-method approach, planned behavior theory

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: Irina Mikajlo, Jaroslav Záhora, Helena Dvořáčková, Jaroslav Hynšt, Jakub Elbl

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Strong anthropogenic impact has uncontrolled consequences on the nature of the soil. Hence, up-to-date sustainable methods of soil state improvement are essential. Investigators provide the evidence that biochar can positively effects physical, chemical and biological soil properties and the abundance of mycorrhizal fungi which are in the focus of this study. The main aim of the present investigation is to demonstrate the effect of two types of plant growth promoting bacteria (PGPB) inoculums along with the beech wood biochar and mineral N additives on mycorrhizal colonization. Experiment has been set up in laboratory conditions with containers filled with arable soil from the protection zone of the main water source ‘Brezova nad Svitavou’. Lactuca sativa (lettuce) has been selected as a model plant. Based on the obtained data, it can be concluded that mycorrhizal colonization increased as the result of combined influence of biochar and PGPB inoculums amendment. In addition, correlation analyses showed that the numbers of main groups of cultivated bacteria were dependent on the degree of mycorrhizal colonization.

Keywords: Arbuscular mycorrhiza, biochar, PGPB inoculum, soil microorganisms

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Authors: John O. Jacob, Abdullahi Mann, Olanrewaju I. Adeshina, Mohammed M. Ndamitso

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Strychnos spinosa, Detarium microcarpum, Diospyros mespiliformis, Dialium guineese and Gardenia ternifolia are some of the wild fruits consume in the villages around Minna, Niger State. This investigation was conducted to assess the nutritional potentials of these fruits both for human consumption and for possible application in animal feed formulations. Standard analytical methods were employed in the determination of the various nutritional parameters. The proximate analysis results showed that the moisture contents ranged between (6.17-10.70%); crude fat (2.04-8.85%); crude protein (5.16-6.80%); crude fibre (7.23-19.65%); Ash (3.46-5.56%); carbohydrate (57.77-69.79%); energy value (284.49-407 kcal/mg); Vitamin C (7.2-39.93 mg/100g). The mineral analysis shows that the selected wild fruits could contribute considerable amount of both micro and macro elements to human nutrition potassium, sodium and calcium range between; potassium (343.27-764.71%); sodium (155.04-348.44%); calcium (52.47-101%). The macro element for the fruits pulp were in the order K>Na>Mg>Ca, hence, they could be included in diet to supplement daily nutrient requirement and in animal feed formulations. The domestication of these fruits is also encouraged.

Keywords: mineral, micro-elements, macro-elements, feed suppleme

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Wassana Naiyapo, Atichat Sangtong

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The processes in software development by Object Oriented methodology have many stages those take time and high cost. The inconceivable error in system analysis process will affect to the design and the implementation process. The unexpected output causes the reason why we need to revise the previous process. The more rollback of each process takes more expense and delayed time. Therefore, the good test process from the early phase, the implemented software is efficient, reliable and also meet the user’s requirement. Unified Modelling Language (UML) is the tool which uses symbols to describe the work process in Object Oriented Analysis (OOA). This paper presents the approach for automatically generated UML use case diagram and test cases. UML use case diagram is generated from the event table and test cases are generated from use case specifications and Graphic User Interfaces (GUI). Test cases are derived from the Classification Tree Method (CTM) that classify data to a node present in the hierarchy structure. Moreover, this paper refers to the program that generates use case diagram and test cases. As the result, it can reduce work time and increase efficiency work.

Keywords: classification tree method, test case, UML use case diagram, use case specification

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Authors: Rim Sghiri, Samia Al Shouli, Hana Benhassine, Nejla Elamri, Zahid Shakoor, Foued Slama, Adel Almogren, Hala Zeglaoui, Elyes Bouajina, Ramzi Zemni

Abstract:

Objectives: Little is known about genes predisposing to systemic bone loss (SBL) in rheumatoid arthritis (RA). Therefore, we examined the association between SBL and a variant of CD40 gene, which is known to play a critical role in both immune response and bone homeostasis among patients with RA. Methods: CD40 rs48104850 was genotyped in 176 adult RA patients. Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA). Results: Low BMD was observed in 116 (65.9%) patients. Among them, 60 (34.1%) had low femoral neck (FN) Z score, 72 (40.9%) had low total femur (TF) Z score, and 105 (59.6%) had low lumbar spine (LS) Z score. CD40 rs4810485 was found to be associated with reduced TF Z score with the CD40 rs4810485 T allele protecting against reduced TF Z score (OR = 0.40, 95% CI = 0.23-0.68, p = 0.0005). This association was confirmed in the multivariate logistic regression analysis (OR=0.31, 95% CI= 0.16-0.59, p=3.84 x 10₋₄). Moreover, median FN BMD was reduced among RA patients with CD40 rs4810485 GG genotype compared to RA patients harbouring CD40 rs4810485 TT and GT genotypes (0.788± 0.136 versus 0.826± 0.146g/cm², p=0.001). Conclusion: This study, for the first time ever, demonstrated an association between a CD40 genetic variant and SBL among patients with RA.

Keywords: rheumatoid arthritis, CD40 gene, bone mineral density, systemic bone loss, rs48104850

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Authors: Minzi Mao, Jianjun Ren, Yu Zhao

Abstract:

Objectives: The absence of a uniform staging scheme for temporal bone carcinoma (TBC) seriously impedes the improvement of its management strategies. Therefore, this research was aimed to investigate the prognostic values of two currently applying staging schemes, namely, the modified Pittsburgh staging system (MPB) and Stell’s T classification (Stell-T) in patients with TBC. Methods: Areal-world single-institution retrospectivereview of patientsdiagnosed with TBC between2008 and 2019 was performed. Baseline characteristics were extracted, and patients were retrospectively staged by both the MPB and Stell-T classifications. Cox regression analyseswereconductedtocomparetheoverall survival (OS). Results: A total of 69 consecutive TBC patients were included in thisstudy. Univariate analysis showed that both Stell-T and T- classifications of the modified Pittsburgh staging system (MPB-T) were significant prognostic factors for all TBC patients as well as temporal bone squamous cell carcinoma (TBSCC, n=50) patients (P < 0.05). However, only Stell-T was confirmed to be an independent prognostic factor in TBSCC patients (P = 0.004). Conclusions: Tumor extensions, quantified by both Stell-T and MPB-T classifications, are significant prognostic factors for TBC patients, especially for TBSCC patients. However, only the Stell-T classification is an independent prognostic factor for TBSCC patients.

Keywords: modified pittsburgh staging system, overall survival, prognostic factor, stell’s T- classification, temporal bone carcinoma

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Authors: Sona Babu Rathinam, Lavanya Dharmendran, Therraddi Mutthu

Abstract:

Objectives: The purpose of this paper is to provides an overview of the neuroendocrine tumors that arise in the oral cavity. Material and Methods: An overview of the relevant papers on neuroendocrine tumors of the oral cavity by various authors was studied and summarized. Results: On the basis of the relevant studies, this paper provides an overview of the classification and histological differentiation of the neuroendocrine tumors that arise in the oral cavity. Conclusions: The basis of classification of neuroendocrine tumors is largely determined by their histologic differentiation. Though they reveal biologic heterogeneity, there should be an awareness of the occurrence of such lesions in the oral cavity to enable them to be detected and treated early.

Keywords: malignant peripheral nerve sheath tumor, olfactory neuroblastoma, paraganglioma, schwannoma

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