Search results for: plant disease classification

Authors: G. Raviraja Shetty, K. G. Poojitha

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An experiment was conducted to study the effect of different growth regulators on seed germination and vegetative propagation by cuttings of an endangered medicinal plant species, Celastrus paniculatus Willd. at College of Horticulture, Mudigere during June- Sept 2014. Various growth parameters were recorded for seed germination and significantly higher results for Rate of germination (0.78), Plant vigour (2082.74), Plant height (22.10cm), number of leaves (7.83) fresh weight (136.58mg) and dry weight of plant (59.16mg) noticed in seeds treated with GA3 400 ppm when compared to control. In vegetative propagation the cuttings treated with IBA 2000 ppm recorded significantly highest sprouting percentage (98.00) when compared to control (71.00). The results of present investigation will be helpful for large scale multiplication of the species. It will also help for cultivation and conservation of this endangered species.

Keywords: Celastrus paniculatus Willd, seeds, germination, cuttings

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Fatima Majeed Almonajim

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Panucum miliaceum L is a plant from the genus Gramineae, In the world, millets are regarded as a significant grain, however, they are very little exploited. Millet grain is abundant in nutrients and health-beneficial phenolic compounds, making it suitable as food and feed. The plant has received considerable attention for its high content of phenolic compounds, low glycemic index, the presence of unsaturated fats and lack of gluten which are beneficial to human health, and thus, have made the plant being effective in treating celiac disease, diabetes, lowering blood lipids (cholesterol) and preventing tumors. Moreover, the plant requires little water to grow, a property that is worth considering. This study provides an overview of the nutritional and health benefits provided by millet types grown in 2 areas Iraq and Iran, aiming to compare the effect of climate on the components of millet. In this research, millet samples collected from the both Babylon (Iraqi) and Isfahan (Iranian) types were extracted and after HPTLC, the resulted pattern of the two samples were compared. As a result, the Iranian millet showed more terpenoid compounds than Iraqi millet, and therefore, Iranian millet has a higher priority than Iraqi millet in increasing the human body's immunity. On the other hand, in view of the number of essential amino acids, the Iraqi millet contains more nutritional value compared to the Iranian millet. Also, due to the higher amount of histidine in the Iranian millet, compiled to the lack of gluten found from previous studies, we came to the conclusion that the addition of millet in the diet of children, more specifically those children with irritable bowel syndrome, can be considered beneficial. Therefore, as a component of dairy products, millet can be used in preparing food for children such as dry milk.

Keywords: HPTLC, phytochemicals, specialty foods, Panucum miliaceum L, nutrition

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Authors: Mustafa Alshawaqfeh, Bilal Wajidy, Echin Serpedin, Jan Suchodolski

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Inflammatory Bowel disease (IBD) is a disease of the colon with characteristic inflammation. Clinically IBD is detected using laboratory tests (blood and stool), radiology tests (imaging using CT, MRI), capsule endoscopy and endoscopy. There are two variants of IBD referred to as Ulcerative Colitis (UC) and Crohn’s disease. This study employs a hybrid feature selection method that combines a correlation-based variable ranking approach with exhaustive search wrapper methods in order to find potential biomarkers for UC. The proposed biomarkers presented accurate discriminatory power thereby identifying themselves to be possible ingredients to UC therapeutics.

Keywords: ulcerative colitis, biomarker detection, feature selection, inflammatory bowel disease (IBD)

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Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Yavuz Bağcı, Levent Keskin

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The study was carried out in 2011-2014 period to determine plant-based hand tools uses of plants in Kadınhanı (Konya) and surrounding villages. A total of 153 individuals, who lived or were living during this study in 4 towns, 37 villages and 9 neighborhood were interviewed. It was found that of a total about 20 plants belonging to 10 families in the study area, about 60 hand-made goods were used by peoples for various purposes.

Keywords: ethnobotanic, handmade, Kadınhanı, Konya, plant-human relationship

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Authors: Vadivel Ayyasamy

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The present approach deals with the identification of Emotions and classification of Emotional patterns at Phrase-level with respect to Positive and Negative Orientation. The proposed approach considers emotion triggered terms, its co-occurrence terms and also associated sentences for recognizing emotions. The proposed approach uses Part of Speech Tagging and Emotion Actifiers for classification. Here sentence patterns are broken into phrases and Neuro-Fuzzy model is used to classify which results in 16 patterns of emotional phrases. Suitable intensities are assigned for capturing the degree of emotion contents that exist in semantics of patterns. These emotional phrases are assigned weights which supports in deciding the Positive and Negative Orientation of emotions. The approach uses web documents for experimental purpose and the proposed classification approach performs well and achieves good F-Scores.

Keywords: emotions, sentences, phrases, classification, patterns, fuzzy, positive orientation, negative orientation

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: N. Samarinas, C. Evangelides, C. Vrekos

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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

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Authors: A. M. U. P. Kumari, Vidanapathirana. J., Amarasekara J., Karunanayaka L.

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Leptospirosis is a zoonotic infection with significant morbidity and mortality. As an occupational disease, it has become a global concern due to its disease burden in endemic countries and rural areas. The aim of this study was to assess disease burden in terms of DALYs of leptospirosis. A hospital-based descriptive cross-sectional study was conducted using 450 clinically diagnosed leptospirosis patients admitted to base and above hospitals in Monaragala district, Sri Lanka, using a pretested interviewer administered questionnaire. The patients were followed up till normal day today life after discharge. Estimation of DALYs was done using laboratory confirmed leptospirosis patients. Leptospirosis disease burden in the Monaragala district was 44.9 DALYs per 100,000 population which includes 33.18 YLLs and 10.9 YLDs. The incidence of leptospirosis in the Monaragala district during the study period was 59.8 per 100,000 population, and the case fatality rate (CFR) was 1.5% due to delay in health seeking behaviour; 75% of deaths were among males due to multi organ failure. The disease burden of leptospirosis in the Moneragala district was significantly high, and urgent efforts to control and prevent leptospirosis should be a priority.

Keywords: human leptospirosis, disease burden, disability adjusted life Years, Sri Lanka

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Authors: Ankitsinh Chauhan, Vimal Patel, A. D. Parekh, Ishant patil

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This study delves into the crucial influence of cooling tower fan operation on the performance of a chiller plant, with a specific focus on the Chiller Plant at SVNIT. Continuous operation of the chiller plant led to unexpected damage to the cooling tower's belt drive, rendering the cooling tower fan non-operational. Consequently, the efficiency of heat transfer in the condenser was significantly impaired. In response, we analyzed and calculated several vital parameters, including the Coefficient of Performance (COP), heat rejection in the condenser (Qc), work required for the compressor (Wc), and heat absorbed by the refrigerant in the evaporator (Qe). Our findings revealed that in the absence of the cooling tower fan, relying solely on natural convection, the COP of the chiller plant reached a minimum value of 5.49. However, after implementing a belt drive to facilitate forced convection for the cooling tower fan, the COP of the chiller plant experienced a noteworthy improvement, reaching approximately 6.27. Additionally, the utilization of forced convection resulted in an impressive reduction of 8.9% in compressor work, signifying enhanced energy efficiency. This study underscores the critical role of cooling tower fan operation in optimizing chiller plant performance, with practical implications for energy-efficient HVAC systems. It highlights the potential benefits of employing forced convection mechanisms, such as belt drives, to ensure efficient heat transfer in the condenser, ultimately contributing to improved energy utilization and reduced operational costs in cooling.

Keywords: cooling tower, chiller Plant, cooling tower fan, energy efficiency, VCRS.

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Authors: S. S. Patil, Sachidanand Kini

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Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found.

Keywords: Mahalanobis distance, minimum distance, supervised, unsupervised, user classification accuracy, producer's classification accuracy, maximum likelihood, kappa coefficient

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Authors: He Yuhai, Ahmad Ziad Bin Sulaiman

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Tongkat Ali, or Eurycoma longifolia, is a traditional Malay and Orang Asli herb used as aphrodisiac, general tonic, anti-Malaria, and anti-Pyretic. It has been recognized as a cashcrop by Malaysia due to its high value for the pharmaceutical use. In Tongkat Ali, eurycomanone, a quassinoid is usually chosen as a marker phytochemical as it is the most abundant phytochemical. In this research, ultrasound and enzyme were used to enhance the extraction of Eurycomanone from Tongkat Ali. Ultrasonic assisted extraction (USE) enhances extraction by facilitating the swelling and hydration of the plant material, enlarging the plant pores, breaking the plant cell, reducing the plant particle size and creating cavitation bubbles that enhance mass transfer in both the washing and diffusion phase of extraction. Enzyme hydrolyses the cell wall of the plant, loosening the structure of the cell wall, releasing more phytochemicals from the plant cell, enhancing the productivity of the extraction. Possible effects of ultrasound on the activity of the enzyme during the hydrolysis of the cell wall is under the investigation by this research. The extracts was analysed by high performance liquid chromatography for the yields of Eurycomanone. In this whole process, the conventional water extraction was used as a control of comparing the performance of the ultrasound and enzyme assisted extraction.

Keywords: ultrasound, enzymatic, extraction, Eurycoma longifolia

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Authors: K. C. Sony

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This paper investigates the coping strategies of households confronting disease in large cardamom (Amomum Subulatum Roxb.) in eastern Nepal. Cardamom farmers draw on various coping strategies to reduce the impact of crop disease in their livelihoods. Yet farmers face tremendous decline in production with a constant effort for revival. Past evidences provides dearth of information about coping strategies employed by farmers and institutional intervention to combat disease. Using factual data from Ilam district, and conducting a political economic analysis, this research addresses the gap by 1) understanding the impact of crop disease in farmers’ livelihoods, 2) identifying the coping strategies adopted by farmers and, 3) examining the existing institutional arrangements to address the disease. Coping strategies vary by household’s status defined by size of land, alternative income, and access to supporting institutions. Measures adopted are burning the cardamom field, changing land use pattern, diversifying crops, and visiting institutions for support. The local government’s support is limited to providing trainings and producing new varieties of cardamom. During crisis, farmers expect institutions to help revive the cardamom production, despite customary practice to combat disease. To retain and improve the livelihoods of farmers, there needs to be institutional innovation at the community level and policies that endorse immediate and sustainable support during hazards.

Keywords: cardamom, coping strategy, disease, institutions, Nepal

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Authors: Majid Abdolrazzagh-Nezhad, Salwani Abdullah

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The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.

Keywords: job-shop scheduling, classification, constraints, objective functions

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Authors: Ilse Kreme

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To the best of this author’s knowledge, no studies have been identified on the connection between a refusal to engage in health-protective behaviors and a basic understanding of viral biology among community college students, faculty, and staff during the COVID-19 pandemic. Lack of scientific knowledge could prevent understanding of why these behaviors are important to prevent the community spread of COVID-19, even when they are not shown to offer much individual protection. In this study, a possible correlation was examined between a basic knowledge level of viral disease that comes from having taken a college biology course and disease perceptions of COVID-19. In particular, disease risk perception, disease severity percept and mask-wearing behaviors were examined as they correlated with having taken an undergraduate biology course. The effect of covariates of age, gender, and education level were investigated along with the main dependent variables. A representative sample of the population included students, faculty, and staff at Paradise Valley Community College (PVCC) in Phoenix, Arizona. Participants were recruited by an email sent to all students, faculty, and staff at PVCC using an all-college email distribution. Disease risk and severity perception were assessed with the Brief Illness Perception Questionnaire 5 (BIP-Q5), which was modified to include questions measuring participant age, education level, and whether they took or ever took a college biology course. Two additional questions measured compliance of willingness to wear a face mask. The results showed an effect of gender on mask-wearing behavior and a correlation between having taken a biology course and disease severity perception. No differences were seen in mask-wearing behavior and disease risk perception as a result of having taken a biology course. These findings suggest that taking an undergraduate biology course leads to a greater awareness of COVID-19 disease severity through an understanding of the basic biological principles of viral disease transmission. The results can be used to modify existing health education strategies. Further research is needed on how to best reach target audiences in all education brackets.

Keywords: COVID-19, education, gender, mask wearing, disease risk perception, disease severity perception

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Authors: Renae McNair

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Subjective well-being levels were assessed in individuals diagnosed with an autoimmune disease. The current exploratory analysis sought to examine two factors that impact subjective well-being in individuals diagnosed with a chronic health condition. The two factors, resilience, and rumination, were assessed as possible moderators in self-reported levels of subjective well-being were measured. The importance of understanding the psychological state of perceived well-being in an individual diagnosed with an autoimmune disease is important given the impact of the level of subjective well-being on life longevity. In previous research, higher levels of subjective well-being are correlated with longer life longevity, including those individuals who have been diagnosed with an autoimmune disease. Conversely, individuals who report higher levels of negative affect have a shorter length of life longevity. According to the Center for Disease Control (CDC) and a report from the National Health Council, currently, 8-10% of individuals in the United States have been diagnosed with at least one autoimmune disease. Although treatment plans are in place to help manage the physical effects of disease, the psychological state of the person impacts life longevity. Resilience and rumination impact subjective well-being as an outcome in individuals diagnosed with an autoimmune disease. Resilience is the ability to adjust or adapt effectively and positively to unfavorable life conditions or events. Resilience acts as a protective factor in life, allowing those who face adversity to successfully adapt, regardless of the health diagnosis. Rumination is the worry or dwelling on the negative aspects of a given situation. Rumination interrupts the adaptive response, leading to a decrease in well-being. The relationship between resilience and subjective well-being were examined correlated with higher levels of resilience and higher levels of self-reported subjective well-being.

Keywords: subjective well-being, rumination, resilience, autoimmune disease

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Authors: Gürcan D. Baysal, Ali Tanış

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In this study, samples of plant types including rose hip (Rosa canina L.), jujube (Ziziphus jujube), sea buckthorn (Hippophae rhamnoides), elderberry (Sambucus nigra), apricot (Prunus armeniaca), scots pine (Pinus sylvestris), and cedar of Lebanon (Cedrus libani) were grown using plant supporting units called Ekobox and drip irrigation systems in the Karapınar, Konya region of Turkey to reveal the efficiency of Ekobox and drip irrigation compared against a control with no irrigation. The plant diameter, height, and survival rates were determined, compared with each other, and statistically analyzed. According to the statistical analysis of the results, Ekobox applications resulted in the highest values for survival rate, diameter, and height measurements whereas the lowest values were determined in the control groups. These results indicate that the cultivation of plants with Ekobox may help protect against the loss of fertile soils as an effective mechanism for combating erosion and desertification. These advantages may also lead to a lasting economic effect on the cultivation of plants by locals of the Karapınar, Konya province who suffer from an ever-decreasing underground water level as a result of agricultural consumption.

Keywords: drip irrigation, ekobox, plant diameter, plant height, plant survival rate

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Kolade Olawande Owoeye, George Weir

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Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

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Authors: Geetika Barman, B. S. Daya Sagar

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In this article, we proposed a pixel-wise classification of hyperspectral images using a mathematical morphology operator-based distance metric called “dilation distance” and “erosion distance”. This method involves measuring the spatial distance between the spectral features of a hyperspectral image across the bands. The key concept of the proposed approach is that the “dilation distance” is the maximum distance a pixel can be moved without changing its classification, whereas the “erosion distance” is the maximum distance that a pixel can be moved before changing its classification. The spectral signature of the hyperspectral image carries unique class information and shape for each class. This article demonstrates how easily the dilation and erosion distance can measure spatial distance compared to other approaches. This property is used to calculate the spatial distance between hyperspectral image feature vectors across the bands. The dissimilarity matrix is then constructed using both measures extracted from the feature spaces. The measured distance metric is used to distinguish between the spectral features of various classes and precisely distinguish between each class. This is illustrated using both toy data and real datasets. Furthermore, we investigated the role of flat vs. non-flat structuring elements in capturing the spatial features of each class in the hyperspectral image. In order to validate, we compared the proposed approach to other existing methods and demonstrated empirically that mathematical operator-based distance metric classification provided competitive results and outperformed some of them.

Keywords: dilation distance, erosion distance, hyperspectral image classification, mathematical morphology

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Authors: Nazakat Ismailzada, Carol Jones

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Tomato drilling moth Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is the main pest of tomato plants in many countries. The larvae of tomato leaves, the stems inside, in the end buds, they opened the gallery in green and ripe fruit. In this way the harmful products can be fed with all parts of the tomato plant can cause damage to 80-100%. Pest harms all above ground parts of the tomato plant. After the seedlings are planted in areas and during blossoming holder traps with tomato moth’s rubber capsule inside should be placed in the area by using five-tomato moth’s feremon per ha. Then there should be carried out observations in the fields in every three days regularly. During the researches, it was showed that in field condition Carogen 20 SC besides high-level biological efficiency also has low ecological load for environment, and should be used against tomato moth in farms. Therefore it was showed that in field condition Carogen 20 SC besides high-level biological efficiency also has low ecological load for environment, and should be used against tomato moth in farms with insecticide expenditure norm 320 qr\ha. In farms should be used plant rotation, plant fields should be plowed on the 25-30 sm depth, before sowing seeds should be proceeded by insecticides. As element of integrated plant protection activities, should be used pheromones trap. In tomato plant fields as an insecticide should be used AGROSAN 240 SC and Carogen 20 SP.

Keywords: lepidoptera, Tuta absoluta, chemical control, integrated pest management

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Authors: Mohammed H. Abu-Dieyeh, Mohammad M. Zalloum

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Plant growth-promoting rhizobacteria (PGPR) are known to enhance plant growth and/or reduce plant damage due to soil-borne pathogens. Tomato is the highest consumable vegetable world-wide including Jordan. Fusarium oxysporum is a pathogen that causes well-known damages and losses to many vegetable crops including tomato. In this study, purification of 112 isolates of PGPR strains from rhizosphere environment of different regions in Jordan was accomplished. All bacterial isolates were In-vitro screened for antagonistic effects against F. oxysporum. The eleven most effective isolates that caused 30%-50% in-vitro growth reduction of F. oxysporum were selected. 8 out of 11 of these isolates were collected from Al-Halabat (arid-land). 7 isolates of Al-Halabat exerted 40-54% In-vitro growth reduction of F. oxysporum. Four-week-old seedlings of tomato cultivar (Anjara, the most susceptible indigenous cultivar to F. oxysporum) treated with PGPR5 (Bacillus amyloliquefaciens), and exposed to F. oxysporum, showed no disease symptoms and no significant changes in biomasses or chlorophyll contents indicating a non-direct mechanism of action of PGPR on tomato plants. However PGPR3 (Bacillus sp.), PGPR4 (Bacillus cereus), and PGPR38 (Paenibacillus sp.) treated plants or PGPR treated and exposed to F. oxysporum showed a significant increasing growth of shoot and root biomasses as well as chlorophyll contents of leaves compared to control untreated plants or plants exposed to the fungus without PGPR treatment. A significant increase in number of flowers per plant was also recorded in all PGPR treated plants. The characterization of rhizobacterial strains were accomplished using 16S rRNA gene sequence analysis in addition to microscopic characterization. Further research is necessary to explore the potentiality of other collected PGPR isolates on tomato plants in addition to investigate the efficacy of the identified isolates on other plant pathogens and then finding a proper and effective methods of formulation and application of the successful isolates on selected crops.

Keywords: antagonism, arid land, growth promoting, rhizobacteria, tomato

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: availability modeling, Markov process, milling system, rice milling plant

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Authors: Kemal Polat

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In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Magdalena Barbara Kaziuk, Ilona Hubner, Jacek Hubner, Slawomir Kroczka

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Background: The motor neuron disease is a progressive neurodegenerative disease causing malfunction. Irrespective of the form of the disease and its onset always leads to the worsening of the quality of life, with patients usually depending on the family. Materials and methods: The study included 20 persons (5 females, 15 males, aged 65,5 ± 20 years) with clinically certain or probable diagnosis of the motor neuron disease. Patients were examined three times in the period of six months. The diagnosis was established based on the criteria of El Escorial. Manual dexterity was assessed using the test of the card Rene Zazzo and the test of shading in with lines Mira Stambak. Results: All patients achieved unsatisfactory results in Rene Zazzo’s test of the card and most of the patients (60%) in Mira Stambak’s test of shading with lines. Significantly higher test results were achieved for Rene Zazzo’s test and lower test results for Mira Stambak’s test in consecutive measurements. Conclusions: Impairment of manual dexterity is present already at the moment of diagnosing the disease and is growing significantly during its course. The quality of life for MND patients undergoes gradual deterioration as a result of the malfunction.

Keywords: manual dexterity, motor neuron disease, quality of life, malfunction

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Authors: Amna Khan, Zareena Kausar, Saad Malik

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Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA.

Keywords: classification accuracies, electromyography, linear discriminant analysis (LDA), Myo armband sensor, support vector machine (SVM)

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Authors: Zehra Aytac, Nurdilek Gulmezoglu

Abstract:

Safflower has been grown for centuries for many purposes worldwide. Especially it is important for the orange-red dye from its petal and for its high-quality oil obtained from the seeds. The crop is high adaptable to areas with insufficient rainfall and poor soil conditions. The plant has a deep taproot that can draw moisture and plant nutrients from deep to the subsoil. The research was carried out to study the nitrogen (N) uptake of different safflower cultivars and lines at different stages of growth and different plant parts in the experimental field of Faculty of Agriculture, Eskişehir Osmangazi University under semi-arid conditions. Different safflower cultivars and lines of varied origins were used as the material. The cultivars and lines were planted in a Randomized Complete Block Design with three replications. Two different growth stages (flowering and harvest) and three different plant parts (head, stem+leaf and seed) were determined. The nitrogen concentration of different plant parts was determined by the Kjeldahl method. Statistical analysis were performed by analysis of variance for each growth stage and plant parts taking a level of p < 0.05 and p < 0.01 as significant according to the LSD test. As a result, N concentration showed significant differences among different plant parts and different growth stages for different safflower genotypes of varied origins.

Keywords: Carthamus tinctorius L., growth stages, head N, leaf N, N uptake, seed N, Safflower

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

Abstract:

We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

Procedia PDF Downloads 422

Authors: Nikhil A. Gavhane, Sachin Shah, Ishant S. Mahajan, Pawan D. Bahekar

Abstract:

Introduction: Millions of people are affected with dengue fever every year, which drives up healthcare expenses in many low-income countries. Organ failure and other serious symptoms may result. Another worldwide public health problem is sickle cell anaemia, which is most prevalent in Africa, the Caribbean, and Europe. Dengue epidemics have reportedly occurred in locations with a high frequency of sickle cell disease, compounding the health problems in these areas. Aims and Objectives: This study examines dengue infection in sickle cell disease-afflicted pre-schoolers. Method:This Retrospective cohort study examined paediatric patients. Young people with sickle cell disease (SCD), dengue infection, and a control group without SCD or dengue were studied. Data on demographics, SCD consequences, medical treatments, and laboratory findings were gathered to analyse the influence of SCD on dengue severity and clinical outcomes, classified as severe or non-severe by the 2009 WHO classification. Using fever or admission symptoms, the research estimated acute illness duration. Result: Table 1 compares haemoglobin genotype-based dengue episode features in SS, SC, and controls. Table 2 shows that severe dengue cases are older, have longer admission delays, and have particular symptoms. Table 3's multivariate analysis indicates SS genotype's high connection with severe dengue, multiorgan failure, and acute pulmonary problems. Table 4 relates severe dengue to greater white blood cell counts, anaemia, liver enzymes, and reduced lactate dehydrogenase. Conclusion: This study is valuable but confined to hospitalised dengue patients with sickle cell illness. Small cohorts limit comparisons. Further study is needed since findings contradict predictions.

Keywords: dengue, chills, headache, severe myalgia, vomiting, nausea, prostration

Procedia PDF Downloads 53