Search results for: plant classification

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: 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: 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: 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: 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: 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

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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: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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

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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

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Authors: John Gaber, Youssef Ahmed, Hossam A.Gabbar, Jing Ren

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Nuclear Power Plant (NPP) operators have a large responsibility on their shoulders. They must allow the plant to generate a high amount of energy while inspecting and maintaining the safety of the plant. This type of occupation comes with high amounts of mental fatigue, and a small mistake can have grave consequences. Electroencephalography (EEG) is a method of gathering the electromagnetic waves emitted by a human brain. We propose a safety system by monitoring brainwaves for signs of mental fatigue. This requires an analysis of the tasks and mental models of the NPP operator, as well as risk factors on mental fatigue and attention that NPP operators face when performing their tasks. The brain waves generated from experiencing mental fatigue can then be monitored for. These factors are analyzed, developing an EEG-based monitoring system, which aims to alert NPP operators when levels of mental fatigue and attention start affecting their performance in task completion.

Keywords: EEG, power plant operator, psychology, task analysis

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Authors: Garg Ramneesh, Uppal Sanjeev, Mittal Rajinder, Shah Sheerin, Jain Vikas, Singla Bhupinder

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Introduction: Despite advances in trauma care, a classification system for soft tissue injuries of the face still needs to be objectively defined. Aim: To develop a classification system for soft tissue injuries of the face; that is objective, easy to remember, reproducible, universally applicable, aids in surgical management and helps to develop a structured data that can be used for future use. Material and Methods: This classification system includes those patients that need surgical management of facial injuries. Associated underlying bony fractures have been intentionally excluded. Depending upon the severity of soft tissue injury, these can be graded from 0 to IV (O-Abrasions, I-lacerations, II-Avulsion injuries with no skin loss, III-Avulsion injuries with skin loss that would need graft or flap cover, and IV-complex injuries). Anatomically, the face has been divided into three zones (Zone 1/2/3), as per aesthetic subunits. Zone 1e stands for injury of eyebrows; Zones 2 a/b/c stand for nose, upper eyelid and lower eyelid respectively; Zones 3 a/b/c stand for upper lip, lower lip and cheek respectively. Suffices R and L stand for right or left involved side, B for presence of foreign body like glass or pellets, C for extensive contamination and D for depth which can be graded as D 1/2/3 if depth is still fat, muscle or bone respectively. I is for damage to facial nerve or parotid duct. Results and conclusions: This classification system is easy to remember, clinically applicable and would help in standardization of surgical management of soft tissue injuries of face. Certain inherent limitations of this classification system are inability to classify sutured wounds, hematomas and injuries along or against Langer’s lines.

Keywords: soft tissue injuries, face, avulsion, classification

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Authors: Kwounghee Choi

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Technological convergence between the various sectors is expected to have a very large impact on future industrial and economy. This study attempts to do empirical approach between specific technologies’ classification. For technological convergence classification, it is necessary to set the target technology to be analyzed. This study selected target technology from national research and development plan. At first we found a source technology for analysis. Depending on the weight of source technology, NT-based, BT-based, IT-based, ET-based, CS-based convergence types were classified. This study aims to empirically show the concept of convergence technology and convergence types. If we use the source technology to classify convergence type, it will be useful to make practical strategies of convergence technology.

Keywords: technology convergence, source technology, convergence type, R&D strategy, technology classification

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Authors: Hüseyin Biçer

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There are more than a thousand endemic plants growing in Turkey. On the other hand, apart from these plantsAnatolia is home to more plant diversitythan the neighboring countries due to its transitional zone. These plants become a part of traditional medicine in the hope of curing the people with whom they have lived for thousands of years. No matter how important the climate is for the plant, the diseases of the region have an important place in the plant's life. While the plants used for tea are in the foreground in regions with heavy winters, the use of raw plants and fruits is common in some gastrointestinal problems. The aim of this study is explaining using the area of endemic plants in Anatolia.

Keywords: anatolian traditional medicine, traditional medicine, anatolian medicine, herbs

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Authors: Priyanka Govender, S. Mtshali, Theresa Moonsamy, Zanele Mkwanazi, L. Mthembu

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Chemically enhanced primary treatment (CEPT) can be used at wastewater works to improve the quality of the final effluent discharge, provided that the plant has spare anaerobic digestion capacity. CEPT can transfer part of the organic load to the digesters thereby effectively relieving the hydraulic loading on the plant and in this way can allow the plant to continue operating long after the hydraulic capacity of the plant has been exceeded. This can allow a plant to continue operating well beyond its original design capacity, requiring only fairly simple and inexpensive modifications to the primary settling tanks as well as additional chemical costs, thereby delaying or even avoiding the need for expensive capital upgrades. CEPT can also be effective at plants where high organic loadings prevent the wastewater discharge from meeting discharge standards, especially in the case of COD, phosphates and suspended solids. By increasing removals of these pollutants in the primary settling tanks, CEPT can enable the plant to conform to specifications without the need for costly upgrades. Laboratory trials were carried out recently at the Umbilo WWTW in Durban and these were followed by a baseline assessment of the current plant performance and a subsequent full scale trial on the Conventional plant i.e. West Plant. The operating conditions of the plant are described and the improvements obtained in COD, phosphate and suspended solids, are discussed. The PST and plant overall suspended solids removal efficiency increased by approximately 6% during the trial. Details regarding the effect that CEPT had on sludge production and the digesters are also provided. The cost implications of CEPT are discussed in terms of capital costs as well as operation and maintenance costs and the impact of Ferric chloride on the infrastructure was also studied and found to be minimal. It was concluded that CEPT improves the final quality of the discharge effluent, thereby improving the compliance of this effluent with the discharge license. It could also allow for a delay in upgrades to the plant, allowing the plant to operate above its design capacity. This will be elaborated further upon presentation.

Keywords: chemically enhanced, ferric, wastewater, primary

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: intelligent transportation system, object detection, vehicle couting, vehicle classification, video processing

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Authors: Yasser Aldali, Farag Ahwide

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This paper investigates the application of large scale (LS-PV) two-axis tracking photovoltaic power plant in Al-Jagbob, Libya. A 50MW PV-grid connected (two-axis tracking) power plant design in Al-Jagbob, Libya has been carried out presently. A hetero-junction with intrinsic thin layer (HIT) type PV module has been selected and modeled. A Microsoft Excel-VBA program has been constructed to compute slope radiation, dew-point, sky temperature, and then cell temperature, maximum power output and module efficiency for this system, for tracking system. The results for energy production show that the total energy output is 128.5 GWh/year. The average module efficiency is 16.6%. The electricity generation capacity factor (CF) and solar capacity factor (SCF) were found to be 29.3% and 70.4% respectively. A 50MW two axis tracking power plant with a total energy output of 128.5 GWh/year would reduce CO2 pollution by 85,581 tonnes of each year. The payback time for the proposed LS-PV photovoltaic power plant was found to be 4 years.

Keywords: large PV power plant, solar energy, environmental impact, dual-axis tracking system

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Gayatri Nahak, Satyajit Kanungo, Rajani Kanta Sahu

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Holarrhena antidysenterica Linn. (Apocynaceae) is a typical Indian medicinal plant popularly known as “Indrajav”. Traditionally the plant has been considered a popular remedy for the treatment of dysentery, diarrhea, intestinal worms and the seeds of this plant are also used as an anti-diabetic remedy. In the present study axillary shoot multiplication, callus induction and shoot regeneration from callus culture were obtained on Murashige and Skoog (MS) medium supplemented with different concentrations and combinations of plant growth regulators. Then in vivo and in vitro grown healthy plants were selected for study of antioxidant activity through DPPH and OH methods. Significantly higher antioxidant activity and phenol contents were observed in vitro raised plant in comparison to in vivo plants. The findings indicated the greater amount of phenolic compounds leads to more potent radical scavenging effect as shown in in vitro raised plant in comparison to in vivo plants which showed the ability to utilize tissue culture techniques towards development of desired bioactive metabolites from in vitro culture as an alternative way to avoid using endangered plants in pharmaceutical purposes.

Keywords: Holarrhena antidysenterica, in vitro, in vivo, antioxidant activity

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Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Kamel A. Abd-Elsalam, Alexei R. Khokhlov

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Studies on bioefficacy (in vitro and in vivo) and mode of action of the nanocides against the most important plant diseases in Egypt and Russia might assist in the goal of sustainable agriculture. To our knowledge, few researchers have evaluated the combined antimicrobial effect of inorganic nanoparticles (NPs) with bioorganic pesticides for controlling plant pathogens in the greenhouse and open field, decontrol investigated synergistic effect. In the current project, we will develop eco-friendly alternative management strategies including the use of heavy nanometal-tolerant Trichoderma strains and the main effective material in conventional fungicides (curpic, sulfur, phosphorus and zinc) for controlling plant diseases. Studies on bioefficacy and the mechanism of the nanocides against the most important plant diseases in Egypt were evaluated. There is a growing need to establish mechanisms of action for nano bio and/or fungicides to assist the design of new compounds or combinations of compounds, in order to understand resistance mechanisms and to provide a focus for toxicological attention. Nanofungicides represent an emerging technological development that could offer a range of benefits including increased efficacy, durability, and a reduction in the amounts of active ingredients that need to be used.

Keywords: biohybrids, biocides, bioagent, plant pathogenic fungi

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Authors: Kedibone Masenya, Memory Tekere, Jasper Rees

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Sorghum is an important cereal crop in the world. In particular, it has attracted breeders due to capacity to serve as food, feed, fiber and bioenergy crop. Like any other plant, sorghum hosts a variety of microbes, which can either, have a neutral, negative and positive influence on the plant. In the current study, regions (V3/V4) of 16 S rRNA were targeted to extensively assess bacterial multitrophic interactions in the phyllosphere of sorghum. The results demonstrated that the presence of a pathogen has a significant effect on the endophytic bacterial community. Understanding these interactions is key to develop new strategies for plant protection.

Keywords: bacteria, multitrophic, sorghum, target sequencing

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Abdulsalam Abiodun Salman, Abdulmumin Isah

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The study investigated the motivational factors that could enhance the teaching and understanding of ICT-driven cataloguing and classification (Cat and Class) practical classes among students of library and information science (LIS) in Kwara State Library Schools, Nigeria. It deployed a positivist research paradigm using a quantitative method by deploying the use of questionnaires for data collection. The population of the study is one thousand, one hundred and twenty-five (1,125) which was obtained from the department of each respective library school (the University of Ilorin, Ilorin (Unilorin); Federal Polytechnic Offa, (Fedpoffa); and Kwara State University (KWASU). The sample size was determined using the research advisor table. Hence, the study sample of one hundred and ten (110) was used. The findings revealed that LIS students were averagely motivated toward ICT-driven Cataloguing and Classification practical classes. The study recommended that modern cataloguing and classification tools for practical classes should be made available in the laboratories as motivational incentives for students. It was also recommended that library schools should motivate the students beyond the provision of these ICT-driven tools but also extend the practical class periods. Availability and access to medical treatment in case of injuries during the practical classes should be made available. Technologists/Tutors of Cat and Class practical classes should also be exposed to further training in modern trends, especially emerging digital knowledge and skills in cataloguing and classification. This will keep both the tutors and students abreast of the new development in the technological arena.

Keywords: cataloguing and classification, motivational factors, ICT-driven practical classes, LIS students, Nigeria

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Authors: Aghoutane Basma, Naama Amal, Talbi Hayat, El Manfalouti Hanae, Kartah Badreddine

Abstract:

Aromatic and medicinal plants are used by man for different needs, including food and medicinal needs for their biological properties attributed mainly to phenolic compounds and for their antioxidant capacity. In our study, the aim is to compare three extraction solvents by evaluating the contents of phenolic compounds, the contents of flavonoids, and the antioxidant activities of extracts from different methods of extracting the aerial part of an endemic medicinal plant from Morocco. This activity was also confirmed by three methods (2,2-diphenyl-1-picrylhydrazyl (DPPH), antioxidant reducing power of iron (FRAP), and total antioxidant capacity (CAT)). The results showed that this plant is rich in polyphenols and flavonoids, as well as it has a very important antioxidant capacity in whatever the solvent or the extraction method. This suggests the importance of using extracts from this plant as a new natural source of food additives and potent antioxidants in the food industry.

Keywords: endemic plant of Morocco, phenolic compound, solvent, extraction technique, antioxidant activity

Procedia PDF Downloads 297

Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

Abstract:

Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

Procedia PDF Downloads 347