Search results for: drought classification

Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: Khunsa Fatima, Umar K. Khattak, Allah Bakhsh Kausar

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Satellite images interpretation and analysis assist geologists by providing valuable information about geology and minerals of an area to be surveyed. A test site in Fatejang of district Attock has been studied using Landsat ETM+ and ASTER satellite images for lithological mapping. Five different supervised image classification techniques namely maximum likelihood, parallelepiped, minimum distance to mean, mahalanobis distance and spectral angle mapper have been performed on both satellite data images to find out the suitable classification technique for lithological mapping in the study area. Results of these five image classification techniques were compared with the geological map produced by Geological Survey of Pakistan. The result of maximum likelihood classification technique applied on ASTER satellite image has the highest correlation of 0.66 with the geological map. Field observations and XRD spectra of field samples also verified the results. A lithological map was then prepared based on the maximum likelihood classification of ASTER satellite image.

Keywords: ASTER, Landsat-ETM+, satellite, image classification

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Authors: E. Farg, M. M. El-Sharkawy, M. S. Mostafa, S. M. Arafat

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Mapping water stress provides important baseline data for sustainable agriculture. Recent developments in the new Sentinel-1 data which allow the acquisition of high resolution images and varied polarization capabilities. This study was conducted to detect and quantify vegetation water content from canopy backscatter for extracting spatial information to encourage drought mapping activities throughout new reclaimed sandy soils in western Nile delta, Egypt. The performance of radar imagery in agriculture strongly depends on the sensor polarization capability. The dual mode capabilities of Sentinel-1 improve the ability to detect water stress and the backscatter from the structure components improves the identification and separation of vegetation types with various canopy structures from other features. The fieldwork data allowed identifying of water stress zones based on land cover structure; those classes were used for producing harmonious water stress map. The used analysis techniques and results show high capability of active sensors data in water stress mapping and monitoring especially when integrated with multi-spectral medium resolution images. Also sub soil drip irrigation systems cropped areas have lower drought and water stress than center pivot sprinkler irrigation systems. That refers to high level of evaporation from soil surface in initial growth stages. Results show that high relationship between vegetation indices such as Normalized Difference Vegetation Index NDVI the observed radar backscattering. In addition to observational evidence showed that the radar backscatter is highly sensitive to vegetation water stress, and essentially potential to monitor and detect vegetative cover drought.

Keywords: canopy backscatter, drought, polarization, NDVI

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Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

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In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data

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Authors: Diana López-Soto, Soumaya Yacout, Francisco Ángel-Bello

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Although inventories are considered as stocks of money sitting on shelve, they are needed in order to secure a constant and continuous production. Therefore, companies need to have control over the amount of inventory in order to find the balance between excessive and shortage of inventory. The classification of items according to certain criteria such as the price, the usage rate and the lead time before arrival allows any company to concentrate its investment in inventory according to certain ranking or priority of items. This makes the decision making process for inventory management easier and more justifiable. The purpose of this paper is to present a new approach for the classification of new items based on the already existing criteria. This approach is called the Logical Analysis of Data (LAD). It is used in this paper to assist the process of ABC items classification based on multiple criteria. LAD is a data mining technique based on Boolean theory that is used for pattern recognition. This technique has been tested in medicine, industry, credit risk analysis, and engineering with remarkable results. An application on ABC inventory classification is presented for the first time, and the results are compared with those obtained when using the well-known AHP technique and the ANN technique. The results show that LAD presented very good classification accuracy.

Keywords: ABC multi-criteria inventory classification, inventory management, multi-class LAD model, multi-criteria classification

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Jaiden X. Schraut

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Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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Authors: Eric Bimmoy

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The aim of the study conducted inside screen house is to determine the effect of Vesicular Arbuscular Mycorrhiza MycoVAM Glomus mosseae or Glomus fasciculatum on the drought resistance of Betel nut (Areca catechu). The study revealed that there is a highly significant growth increment and drought resistance of planted seedling inoculated with VAM compared to uninoculated seedling. The study revealed not significant under well water condition after 60 days. Growths are higher in inoculated seedlings compared to uninoculated seedlings. Seventy days (75) days after planting there was a highly significant difference in inoculated plants. It is not significant in height increment after 90 days, although the height percentage increase in inoculated seedlings was higher. The water stressed Areca catechu seedlings inoculated with VAM significantly increases total shoot height with increment of 72.34 while days before wilting 65.89 and 88.68 in the leaf water content. This demonstrates the result provided by VAM in the development of seedlings.

Keywords: vesicular-arbuscular mycorrhiza MycoVAM, resistance, symbiosis, water stressed

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Authors: Andrzej Wałęga, Marta Cebulska, Agnieszka Ziernicka-Wojtaszek, Wojciech Młocek, Agnieszka Wałęga, Tommaso Caloiero

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Drought is one of the natural phenomena influencing many aspects of human activities like food production, agriculture, industry, and the ecological conditions of the environment. In the area of the Polish Carpathians, there are periods with a deficit of rainwater and an increasing frequency in dry months, especially in the cold half of the year. The aim of this work is a spatial and temporal analysis of drought, expressed as SPI in a heterogenous area of the Polish Carpathian and of the highland Region in the Central part of Europe based on long-term precipitation data. Also, to our best knowledge, for the first time in this work, drought characteristics analyzed via the SPI were discussed based on the atmospheric circulation calendar. The study region is the Upper Vistula Basin, located in the southern and south-eastern part of Poland. In this work, monthly precipitation from 56 rainfall stations was analysed from 1961 to 2022. The 3-, 6-, 9-, and 12-month Standardized Precipitation Index (SPI) were used as indicators of meteorological drought. For the 3-month SPI, the main climatic mechanisms determining extreme droughts were defined based on the calendar of synoptic circulations. The Mann-Kendall test was used to detect the trend of extreme droughts. Statistically significant trends of SPI were observed on 52.7% of all analyzed stations, and in most cases, a positive trend was observed. Statistically significant trends were more frequently observed in stations located in the western part of the analyzed region. Long-term droughts, represented by the 12-month SPI, occurred in all stations but not in all years. Short-term droughts (3-month SPI) were most frequent in the winter season, 6 and 9-month SPI in winter and spring, and 12-month SPI in winter and autumn, respectively. The spatial distribution of drought was highly diverse. The most intensive drought occurred in 1984, with the 6-month SPI covering 98% of the analyzed region and the 9 and 12-month SPI covering 90% of the entire region. Droughts exhibit a seasonal pattern, with a dominant 10-year periodicity for all analyzed variants of SPI. Additionally, Fourier analysis revealed a 2-year periodicity for the 3-, 6-, and 9-month SPI and a 31-year periodicity for the 12-month SPI. The results provide insights into the typical climatic conditions in Poland, with strong seasonality in precipitation. The study highlighted that short-term extreme droughts, represented by the 3-month SPI, are often caused by anticyclonic situations with high-pressure wedges Ka and Wa, and anticyclonic West as observed in 52.3% of cases. These findings are crucial for understanding the spatial and temporal variability of short and long-term extreme droughts in Central Europe, particularly for the agriculture sector dominant in the northern part of the analyzed region, where drought frequency is highest.

Keywords: atmospheric circulation, drought, precipitation, SPI, the Upper Vistula Basin

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: Nitin Bhagat

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Drought is a global phenomenon. It has a huge impact on agriculture and allied sector activities. Agriculture plays a substantial role in the economy of developing countries, which mainly depends on rainfall. The present study illustrates the drought conditions in Masurdi village of Latur district in the Marathwada region, Maharashtra. This paper is based on both primary as well as secondary data sources. The multistage sample method was used for primary data collection. The 100 households sample survey data has been collected from the village through a semi-structured questionnaire. The crop production data is collected from the Department of Agriculture, Government of Maharashtra. The rainfall data is obtained from the Department of Revenue, Office of Divisional Commissioner, Aurangabad for the period from 1988 to 2018. This paper examines the severity of drought consequences of the 2015 drought on domestic water supply, crop production, and the effect on children's schooling, livestock assets, bank credit, and migration. The study also analyzed climate variables' impact on the Latur district's total food grain production for 19 years from 2000 to 2018. This study applied multiple regression analysis to check the relationship between climatic variables and the Latur district's total food grain production. The climate variables are annual rainfall, maximum temperature and minimum temperature. The study considered that climatic variables are independent variables and total food grain as the dependent variable. It shows there is a significant relationship between rainfall and maximum temperature. The study also calculated rainfall deviations to find out the drought and normal years. According to drought manual 2016, the rainfall deviation calculated using the following formula. RF dev = {(RFi – RFn) / RFn}*100.Approximately 27.43 % of the workforce migrated from rural to urban areas for searching jobs, and crop production decreased tremendously due to inadequate rainfall in the drought year 2015. Many farm and non-farm labor, some marginal and small cultivators, migrated from rural to urban areas (like Pune, Mumbai, and Western Maharashtra).About 48 % of the households' children faced education difficulties; in the drought period, children were not going to school. They left their school and joined to bring water with their mother and fathers, sometimes they fetched water on their head or using a bicycle, near about 2 km from the village. In their school-going days, drinking water was not available in their schools, so the government declared holidays early in the academic education year 2015-16 compared to another academic year. Some college and 10th class students left their education due to financial problems. Many households benefited from state government schemes, like drought subsidies, crop insurance, and bank loans. Out of 100 households, about 50 (50 %) have obtained financial support from the state government’s subsidy scheme, 58 ( 58 %) have got crop insurance, and 41(41 %) irrigated households have got bank loans from national banks; besides that, only two families have obtained loans from their relatives and moneylenders.

Keywords: agriculture, drought, household, rainfall

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Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam

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In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.

Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen

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In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Keywords: association rules, rule-based classification, classification quality, validation

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: automatic equalization, genre classification, music segment detection, spatial audio processing

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Authors: Adriano Losso, Birgit Dämon, Stefan Mayr

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In vascular plants, water flows from roots to leaves in a metastable state, and even a small perturbation of the system can lead a sudden transition from the liquid to the vapor phase, resulting in xylem embolism (cavitation). Xylem embolism, induced by drought stress and/or freezing stress is caused by the aspiration of gaseous bubbles into xylem conduits from adjacent gas-filled compartments through pit membrane pores (‘air seeding’). At water potentials less negative than the threshold for air seeding, the surface tension (γ) stabilizes the air-water interface and thus prevents air from passing the pit pores. This hold is probably also true for conifers, where this effect occurs at the edge of the sealed torus. Accordingly, it was experimentally demonstrated that γ influences air seeding, but information on the relevance of this effect under field conditions is missing. In this study, we analyzed seasonal changes in γ of the xylem sap in two conifers growing at the alpine timberline (Picea abies and Pinus mugo). In addition, cut branches were perfused (40 min perfusion at 0.004 MPa) with different γ solutions (i.e. distilled and degassed water, 2, 5 and 15% (v/v) ethanol-water solution corresponding to a γ of 74, 65, 55 and 45 mN m-1, respectively) and their vulnerability to drought-induced embolism analyzed via the centrifuge technique (Cavitron). In both species, xylem sap γ changed considerably (ca. 53-67 and ca. 50-68 mN m-1 in P. abies and P. cembra, respectively) over the season. Branches perfused with low γ solutions showed reduced resistance against drought-induced embolism in both species. A significant linear relationship (P < 0.001) between P12, P50 and P88 (i.e. water potential at 12, 50 and 88% of the loss of conductivity) and xylem sap γ was found. Based on this correlation, a variation in P50 between -3.10 and -3.83 MPa (P. abies) and between -3.21 and -4.11 MPa (P. mugo) over the season could be estimated. Results demonstrate that changes in γ of the xylem sap can considerably influence a tree´s resistance to drought-induced embolism. They indicate that vulnerability analyses, normally conducted at a γ near that of pure water, might often underestimate vulnerabilities under field conditions. For studied timberline conifers, seasonal changes in γ might be especially relevant in winter, when frost drought and freezing stress can lead to an excessive embolism.

Keywords: conifers, Picea abies, Pinus mugo, timberline

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Renu Yadav, Sanjeev Kumar

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In northern India, chickpea (Cicer arietinum L.) come across with terminal high-temperature stress during reproductive stage which leads to reduced yield. Hence, stable production of chickpea will depend on the development of new methods like ‘priming’ which allow improved adaptation to the drought and heat stress. In the present experiment, 11-day chickpea seedling was primed with mild drought stress and put on recovery stage by irrigating and finally 30-day seedlings were exposed to heat stress 38°C (4 hours), 35°C (8 hours) and 32°C (12 hours). To study the effect of combinatorial stress, heat and drought stress was applied simultaneously. Analyses of various physiological parameters like membrane damage assay, photosynthetic pigments, antioxidative enzyme, total sugars were estimated at all stages. To study the effect of heat stress on the metabolites of the plants, GC-MS and HPLC were performed, while at transcriptional level Real-Time PCR of predicted heat stress-related genes was done. It was concluded that the heat stress significantly affected the chickpea plant at physiological and molecular level in all the five varieties. Results also show less damaging effect in primed plants by increasing the activity of antioxidative enzymes and increased expression of heat shock proteins and heat shock factors.

Keywords: chickpea, combinatorial stress, heat stress, oxidative stress, priming, RT-PCR

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

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The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

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Authors: Muhammad Naeem Chaudhry, Fahim Nawaz, Rana Nauman Shabbir

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New strategies aimed at increasing the resilience of crop plants to the negative effects of climate change represent important research priorities of plant scientists. The use of soil microorganisms to alleviate abiotic stresses like drought has gained particular importance in recent past. A field experiment was planned to investigate the effect of phosphorous solubilizing bacteria on yield and seed quality of Camelina (Camelina sativa L.) under water deficit conditions. The study was conducted at Agronomic Research Farm, University College of Agriculture and Environmental Sciences, The Islamia University Bahawalpur, during 4th week of November, 2013. The available seeds of Camelina sativa were inoculated with two bacterial strains (pseudomonas and Bacillus spp.) and grown under various water stress levels i.e. D0, (four irrigations), D3 (three irrigation), D2 (two irrigations), and D1 (one irrigation). The results revealed that drought stress significantly reduced the plant growth and yield, consequently reducing protein contents and oil concentration in camelina. The exposure to drought stress decreased plant height (16%), plant population (27%), number of fertile branches (41-59%), number of pods per plant (35%) and seed per pod (33%). Drought stress also exerted a negative impact on yield characteristics by reducing the 1000-seed weight (65%), final seed yield (52%), biological yield (22%) and harvest index (39%) of camelina. However, the inoculation of seeds with Pseudomonas and Bacillus spp. promoted the plant growth characterized by increased plant height and enhanced plant population. It was noted that inoculation of seeds with Pseudomonas resulted in the maximum plant population (113.4 cm), primary branches (19 plant-1), and number of pods (664 plant-1), whereas Bacillus inoculation resulted in maximum plant height (113.4 cm), seeds per pod (15.9), 1000-seed weight (1.85 g), and seed yield (3378.8 kg ha-1). Moreover, the inoculation with Bacillus also significantly improved the quality attributes of camelina and gave 3.5% and 2.1% higher oil contents than Pseudomonas and control (no-inoculation), respectively. Similarly, the same strain also resulted in maximum protein contents (33.3%). Our results confirmed the hypothesis that inoculation of seeds with phosphorous solubilizing bacterial strains is an effective, viable and environment-friendly approach to improve yield and quality of camelina under water deficit conditions. However, further studies are suggested to investigate the physiological and molecular processes, stimulated by bacterial strains, for increasing drought tolerance in food crops.

Keywords: Camelina, drought stress, phosphate solubilizing bacteria, seed quality

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

Abstract:

Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

Procedia PDF Downloads 318

Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 330

Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

Procedia PDF Downloads 449