Search results for: plant disease classification

Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

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This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

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Authors: Jamila Fakchich, Mostafa Jamila Lazaar Elachouri, Lakhder Fakchich, Fatna Ouali, Abd Errazzak Belkacem

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Diabetes is a chronic metabolic disease that has a significant impact on the health, quality of life, and life expectancy of patients as well as the health care system. By its nature diabetes, is a multisystem disease with wide-ranging complication that span nearly all region of the body. This epidemic problem, however, is not unique to the industrialized society, but has also hardly struck the developing countries. In Morocco, as developing country, there is an epidemic rise in diabetes, with ensuing concern about the management and control of this disease; it began a chronic burdensome disease of largely middle-aged and elderly people, with a long course and serious complications often resulting in high death-rate, the treatment of diabetes spent vast amount of resources including medicines, diets, physical training. Treatment of this disease is considered problematic due to the lack of effective and safe drugs capable of inducing sustained clinical, biochemical, and histological cure. In Moroccan society, the phytoremedies are some times the only affordable sources of healthcare, particularly for the people in remote areas. In this paper, we present a synthesis work obtained from the ethnobotanical data reported in different specialized journals. A Synthesis of four published ethnobotanical studies that have been carried out in different region of Morocco by different team seekers during the period from 1997 to 2015. Medicinal plants inventoried by different seekers in four Moroccan’s areas have been regrouped and codified, then, Factorial Analysis (FA) and Principal Components Analysis (PCA) are used to analyse the aggregated data from the four studies and plants are classified according to their frequency of use by population. Our work deals with an attempt to gather information on some traditional uses of medicinal plants from different regions of Morocco, also, it was designed to give a set of medicinal plants commonly used by Moroccan people in the treatment of diabetes; In this paper, we intended to provide a basic knowledge about plant species used by Moroccan society for treatment of diabetes. One of the most interesting aspects of this type of works is to assess the relative cultural importance of medicinal plants for specific illnesses and exploring its usefulness in the context of diabetes.

Keywords: Morocco, medicinal plants, ethnobotanical, diabetes, phytoremedies

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Authors: A. Al Yousef, A. Toulon, L. Petit, S. Fraitag, F. Ruemmele, S. Hadj-Rabia, C. Bodemer

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Cutaneous Crohn’s disease (CCD) refers to an extremely rare granulomatous inflammation of the skin that is non-contiguous to the bowel tract. These cutaneous lesions can occur prior to, concurrent with, or after the gastrointestinal manifestations. In adults, CCD most frequently occurs in the setting of well-documented intestinal disease. Only 20% of cases occur prior to its development. Review of CCD in children, reveals that 86% of cases (24 of 28) occurring in patients without a known diagnosis of intestinal Crohn’s disease. Overall, the genitalia was the most commonly involved location, representing 21 of the 28 cases with 16 vulvar and 5 penile/scrotal lesions.

Keywords: Crohn’s disease, cutaneous manifestations, children, atypical axillary involvement

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Authors: Somayeh Akrami, Vali-Allah Mozaffarian, Habib Onsori

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The studied region in this research, watershed Kuhkamar river, is about 112.66 square kilometers, it is located between 45º 48' 9" to 45º 2' 20" N and 38º 34' 15" to 38º 40' 28" E. The gained results of the studies on flora combinations, proved 287 plant species in 190 genera and 51 families. Asteracea with 49 and Lamiaceae with 27 plant species are the major plant families. Among collected species one interesting plant was found and determined as a new record Anemone narcissiflora L. for flora of Iran. This plant is known as a complex species that shows intraspecific speciation and is classified into about 12 subspecies and 10 varieties in world. To identify the infraspecies taxons of this species, in addition to morphological characteristics, the use of appropriate molecular markers for the better isolation of the individuals were needed.

Keywords: Anemone narcissiflora, floristic Study, kuhkamar, molecular marker

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Amira Ben Rabeh, Faouzi Benzarti, Hamid Amiri, Mouna Bouaziz

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Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work; we proposed an application to detect Alzheimer’s diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Keywords: Alzheimer Diseases (AD), Computer Assisted Diagnostic(CAD), hippocampus, Corpus Callosum (CC), cortex, Support Vector Machine (SVM)

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Authors: Jalel Khelil

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In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Nebi Gedik

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One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Subramaniam Iyer

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The most common disease that is pertaining to all human beings is heart-related. The reasons for coronary artery disease are due to lifestyle and eating habits. Due to this, many people mentally become sick, feeling that soon they will die due to their heart problems. This results in stress and anxiety, which has become common amongst all the Indians. Medicines are the commonest curative remedy in India, but it is proposed through this article some remedies through yoga. This article does not guarantee a 100% result, but it is a preventive remedy for coronary artery disease. Yoga is giving a new lease of life to many, so to tackle chronic diseases, it provides remedies that will be lifelong. It is brought to many people by Patanjali. Yoga will provide support to patients having coronary artery disease through its various relevant postures (asanas), which can be done very easily. Yoga does not send a message that if you do it regularly, you will be relieved from a particular disease. If it is performed every day, it will add vital energy for a smooth life, even if you are suffering from any chronic disease. In this article, we will be providing 6 postures (asanas), which can be performed at any time in the day, but the early morning will always be preferred (empty stomach) to get a good result. Secondly, these postures must be implemented after due consultation with your physician. If your physician disapproves, don’t do these postures as it will be harmful to your body.

Keywords: coronary artery, yoga, disease, remedy, medicine

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Authors: Muhammad Naveed Aslam, Rida Fatima, Anam Moosa, Muhammad Taimoor Shakeel

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Bacillus strains produce antifungal secondary metabolites making them potential candidates for suppressing Fusarium wilt of chickpea disease. In this study, eighteen Bacillus strains were evaluated for their antagonistic effect against Fusarium oxysporum f. sp. ciceris causing Fusarium wilt of chickpea disease. In a direct antifungal assay, thirteen strains showed significant inhibition zones while the remaining five strains did not produce inhibition zones of FOC. Bacillus thuringiensis CHGP12 was the most promising strain exhibiting the highest inhibition of FOC. Antifungal lipopeptides were extracted from CHGP12 strain which showed significant inhibition of the pathogen. Liquid chromatography mass spectrometry (LCMS) analysis revealed that CHGP12 was positive for the presence of iturin, fengycin, surfactin, bacillaene, bacillibactin, plantazolicin, and bacilysin. CHGP12 was tested for biochemical determinants in an in vitro qualitative test where it showed the ability to produce lipase, amylase, cellulase, protease, siderophores, and indole 3-acetic acid (IAA). Furthermore, in a greenhouse experiment CHGP12 also showed a significant decrease in the disease severity in treated plants compared to control. Moreover, CHGP12 also exhibited a significant increase in plant growth parameters viz, root and shoot growth parameters, stomatal conductance, and photosynthesis rate. Conclusively, our findings present the promising potential of Bacillus strain CHGP12 to suppress Fusarium wilt of chickpea and to promote plant growth.

Keywords: liquid chromatography mass spectrometry, growth promotion, antagonism, hydrolytic enzymes, inhibition, lipopeptides.

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Authors: Bing-Bing E. Goh

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Plant counting is a labour intensive and time-consuming task for the farmers. However, it is an important indicator for farmers to make decisions on subsequent field management. This study is to evaluate the potential of Sentinel-2 images using statistical analysis to retrieve information on plant density for monitoring, especially during critical period at the beginning of March. The model was calibrated with in-situ data from 19 winter wheat fields in Republic of Ireland during the crop growing season in 2019-2020. The model for plant density resulted in R2 = 0.77, RMSECV = 103 and NRMSE = 14%. This study has shown the potential of using Sentinel-2 to estimate plant density and quantify plant establishment to effectively monitor crop progress and to ensure proper field management.

Keywords: winter wheat, remote sensing, crop monitoring, multivariate analysis

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Authors: Deepali Lal, Sudha Summerwar, Jyoutsna Pandey

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The resistance of pesticide by the pest is an important matter of concern.The pesticide of plant origin having nontoxic biodegradable and environmentally friendly qualities. The frequent spraying of toxic chemicals is developing resistance to the pesticide. Leaf powder of the plants like Argimone mexicana and Calotropis procera is prepared, Different doses of these plant extracts are given to the Fourth in star stages of Helicoverpa armigera through feeding methods, to find their efficacy the experimental findings will be put under analysis using various parameters. The effect on paritrophic membrane is also studied.

Keywords: distillation plant, acetone, alcohol, pipette, castor leaves, grams pods, larvae of helicoverpa armigera, plant extract, vails, jars, cotton

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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Planet image is a new data source from planet lab. This research is concerned with the assessment of Planet image for land cover mapping. Two pixel based classifiers and one subpixel based classifier were compared. Firstly, rectification of Planet image was performed. Secondly, a comparison between minimum distance, maximum likelihood and neural network classifications for classification of Planet image was performed. Thirdly, the overall accuracy of classification and kappa coefficient were calculated. Results indicate that neural network classification is best followed by maximum likelihood classifier then minimum distance classification for land cover mapping.

Keywords: planet image, land cover mapping, rectification, neural network classification, multilayer perceptron, soft classifiers, hard classifiers

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Authors: Lale Efe, Zeynep Gokce

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In this study carried out in 2013-14 growing season in East Mediterranean Region of Turkey, it was aimed to investigate the effects of different sowing times on the seed yield and quality of fenugreek (Trigonella foenum graceum L.). Three fenugreek genotypes (Gürarslan, Candidate Line-1 and Genotype-1) were sown on 13.11.2013 and 07.03.2014 according to factorial randomized block design with 3 replications. Plant height (cm), branch number per plant, first pod height (cm), pod length (mm), seed number per pod (g), seed yield per plant (g), seed yield per decar (kg), thousand seed weight (g), mucilage rate (%), seed protein ratio (%), seed oil ratio (%), oleic acid (%), linoleic acid (%), palmitic acid (%) and stearic acid (%) were investigated. Among genotypes, while the highest seed yield per plant was obtained from Genotype-1 (5 g/plant), the lowest seed yield per plant was obtained from cv. Gürarslan (3.4 g/plant). According to genotype x sowing date interactions, it can be said that the highest seed yield per plant was taken in autumn sowing from Genotype-1 (6.6 g/plant) and the lowest seed yield per plant was taken in spring sowing from cv. Gürarslan (2.9 g/plant). Genotype-1 had the highest linoleic acid ratio (41.6 %). Cv. Gürarslan and Candidate Line-1 had the highest oleic acid ratio (respectively 17.8 % and 17.6%).

Keywords: fenugreek, seed yield and quality, sowing times, Trigonella foenum graecum L.

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Authors: Paramjit Kaur, Harish Kundra

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In the recent years, swarm intelligence based firefly algorithm has become a great focus for the researchers to solve the real time optimization problems. Here, firefly algorithm is used for the application of satellite image classification. For experimentation, Alwar area is considered to multiple land features like vegetation, barren, hilly, residential and water surface. Alwar dataset is considered with seven band satellite images. Firefly Algorithm is based on the attraction of less bright fireflies towards more brightener one. For the evaluation of proposed concept accuracy assessment parameters are calculated using error matrix. With the help of Error matrix, parameters of Kappa Coefficient, Overall Accuracy and feature wise accuracy parameters of user’s accuracy & producer’s accuracy can be calculated. Overall results are compared with BBO, PSO, Hybrid FPAB/BBO, Hybrid ACO/SOFM and Hybrid ACO/BBO based on the kappa coefficient and overall accuracy parameters.

Keywords: image classification, firefly algorithm, satellite image classification, terrain classification

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Authors: Vo Ngoc Phu, Phan Thi Tuoi

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We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.

Keywords: sentiment classification, sentiment orientation, valence shifters, contextual, valence shifters, term counting

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Authors: Atakan Efe Akpınar, Zeynep Demir

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Seed priming technologies are frequently used nowadays to increase the germination potential and stress tolerance of seeds. These treatments might be beneficial for native species as well as crops. Different priming treatments can be used depending on the type of plant, the morphology, and the physiology of the seed. Moreover, these may be various physical, chemical, and/or biological treatments. Aiming to improve studies about seed priming, ideas need to be brought into this technological sector related to the agri-seed industry. In this study, seed priming was carried out using some plant extracts. Firstly, some plant extracts prepared from plant leaves, roots, or fruit parts were obtained for use in priming treatments. Then, seeds were kept in solutions containing plant extracts at 20°C for 48 hours. Seeds without any treatment were evaluated as the control group. At the end of priming applications, seeds are dried superficially at 25°C. Seeds were analyzed for vigor (normal germination rate, germination time, germination index etc.). In the future, seed priming applications can expand to multidisciplinary research combining with digital, bioinformatic and molecular tools.

Keywords: seed priming, plant extracts, germination, biology

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Authors: Geeta Sharma, Suma Chandra

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Sugarcane is one of the major commercial crop playing roles in agriculture and industrial economy of India. Globally sugarcane is affected by approximately 240 diseases caused by various plant pathogenic organisms. Among them, red rot disease caused by the fungus Colletotrichum falcatum, is one of the most important diseases. In the present investigation, one fungal bioagent of Trichoderma harzianum, Pant Bioagent 1 and one bacterial bioagent Pseudomonas fluorescence, Pant Bioagent 2 (PBAT 1 and PBAT 2, respectively) were tested by dual culture method against the pathogen under laboratory conditions. The effectiveness of biocontrol agents was observed against four isolates of C. falcatum. In the case of PBAT1 maximum percent inhibition of pathogen was recorded in isolated Cf 0238 (61.05%), followed by Cf 09 (60.62%) whereas, minimum percent inhibition was recorded in Cf 3220 (48.55%) and in case of PBAT2 maximum mycelial growth inhibition percent was recorded in Cf 767 (50.50%) followed by Cf 088230(48.83%), whereas minimum percent inhibition was recorded in Cf 08 (40.16%) followed by Cf 0238 (41.83%). The present study showed that these biocontrol agents have the potential of controlling the pathogen and can further be used for the management of red rot disease in field.

Keywords: biocontrol agents, Colletotrichum falcatum, isolates, sugarcane

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Authors: Faisal Alshuwaier, Ali Areshey

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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

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Authors: Evy Latifah, Eli Korlina, Hanik Anggraeni, Kuntoro Boga, Joko Mariyono

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During the rainy season, the tomato plants are vulnerable to various diseases. A disease that attacks the leaves of tomato plants (foliar diseases) such as late blight (Phytophtora infestans) and spotting bacteria (bacterial spot / Xanthomonas sp.) In addition, there is a disease that attacks the roots such as fusarium and bacterial wilt. If not immediately anticipated, it will decrease the quality and quantity of crop yields. In fact, it can lead to crop failure. The aim of this research is to know the production of tomato grafting by using Timoty and CLN 3024 tomatoes at rain shelter during rainy season in lowland. Data were analyzed using analysis of variance and tested further by Least Significant Difference (LSD) level of 5 %. The parameters measured were plant height (cm), stem diameter (cm), number of fruit space, canopy extended, number of branches, number of productive branches, and the number of stem segments. The results show at the beginning of growth until the end of the treatment without grafting with relative rain shelter displays the highest plant height. This was followed by extensive crop canopy. For tomato grafting and non-grafting using rain shelter able to produce the number of branches and number of productive branches at most. While at the end of the growth in the number of productive branches generated as much. Highest production of tomatoes produced by tomato dig rafting to use the shelter.

Keywords: field trail, wet and dry season, production, diseases, rain shelter

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Authors: Sultan A. Al Shaqaq

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Corrosion is a serious challenge for a piping system in our Gas and Oil Separation Plant (GOSP) that causes piping failures. Two GOSPs (Plant-A and Plant-B) observed chronic corrosion issue with an on-plot piping system that leads to having more piping replacement during the past years. Since it is almost impossible to avoid corrosion, it is becoming more obvious that managing the corrosion level may be the most economical resolution. Corrosion engineers are thus increasingly involved in approximating the cost of their answers to corrosion prevention, and assessing the useful life of the equipment. This case study covers the background of corrosion encountered in piping internally and externally in these two GOSPs. The collected piping replacement data from year of 2011 to 2014 was covered. These data showed the replicate corrosion levels in an on-plot piping system. Also, it is included the total piping replacement with drain lines system and other service lines in plants (Plant-A and Plant-B) at Saudi Aramco facility.

Keywords: gas and oil separation plant, on-plot piping, drain lines, Saudi Aramco

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Authors: Asho Ali

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Tuberculosis is a disease of grave concern which infects one-third of the global population. The high incidence of tuberculosis is further compounded by the increasing emergence of drug resistant strains including multi drug resistant (MDR). Global incidence MDR-TB is ~4%. Molecular epidemiological studies, based on the assumption that patients infected with clustered strains are epidemiologically linked, have helped understand the transmission dynamics of disease. It has also helped to investigate the basis of variation in Mycobacterium tuberculosis (MTB) strains, differences in transmission, and severity of disease or drug resistance mechanisms from across the globe. This has helped in developing strategies for the treatment and prevention of the disease including MDR.

Keywords: Mycobcaterium tuberculosis, molecular epidemiology, drug resistance, disease

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

Abstract:

Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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