Search results for: sediment classification

Authors: Muna Al-Tarshi

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The distribution and accumulation dynamics of anthropogenic marine debris (AMD) and microplastic particles in mangrove habitats in the region are poorly understood. The abundance, sorting, and diversity aspects of AMD and microplastics were investigated in three types of mangroves creeks ( Natural mangrove, afforested mangrove, and non-planted). Abundance, concentration, and particles form of microplastics have been illustrated in three substrate in mangrove habitats e.g. sediment, water, and aquatic organisms. Density separation method by using highly saturated solution was implemented to extract the plastic particles from the sediment samples. The average size of particles in each transect was done using image software, and the polymer type was determined via FTIR. There was variability in abundance of microplastics and marine debris between the habitats and within the substrates in the habitats.Biomonitoring program was developed to detect the pollution of microplastics in mangrove habitats in Sultanate of Oman. Sediment dwelling species were the best choice. Testing whether the zooplankton (Artemia) eating the microplastics via FlowCam technique have been studied. The zooplanktons (Artemia) were eating the microplastics as mistaken food.

Keywords: microplastics, marine debris, flowcam, FTIR, polymer, artemia

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Authors: Alaa G. M. Osman, Khaled Y. AbouelFadl, Angela Krüger, Werner Kloas

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In Egypt, no previous records are available regarding possible multiple hormonal activities in the aquatic systems and especially the river Nile. In this paper, the in vitro yeast estrogen screen (YES) and yeast androgen screen (YAS) were used to assess the multiple hormonal activities in surface waters and sediment from the Egyptian river Nile for the first time. This study sought to determine if river Nile water caused changes in gonadal histology of Nile tilapia (Oreochromis niloticus niloticus). All water samples exhibited extremely low levels of estrogenicity. Estrogenicity was not detected nearly in any of the sediment samples. Unlike the estrogenicity, significant androgenic activities were recorded in the water and sediment samples along the Nile course. The present study reports for the first time quantified anti-estrogenic and anti-androgenic activities with high levels in both water and sediment of the river Nile. The greatest anti-estrogenic and anti-androgenic activities were observed in sample from downstream river Nile. These results indicated that the anti-estrogenic and anti-androgenic activities along the Nile course were great and the pollution of the sites at the downstream was more serious than the upstream sites due to industrial activities at theses sites. Good correlations were observed among some hormonal activities, suggesting coexistence of these contaminants in the environmental matrices. There were no signs of sexual disruption in any of the gonads analysed from either male or female Nile tilapia, demonstrating that any hormonal activity present along the Nile course was not sufficient to induce adverse effects on reproductive development. Further investiga¬tion is necessary to identify the chemicals responsible for the hormonal activities in the river Nile and to examine the effect of very low levels of hormonally active chemicals on gonadal histology, as well as in the development of more sensitive biomarkers.

Keywords: multiple hormonal activities, YES, YAS, river Nile, Nile tilapia, gonadal histology

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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

Procedia PDF Downloads 331

Authors: Francisco Liaño-Carrera, José Isaac Ramírez-Macías, David Salas-Monreal, Mayra Lorena Riveron-Enzastiga, Marcos Rangel-Avalos, Adriana Andrea Roldán-Ubando

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The construction of most coastal infrastructure developments around the world are usually made considering wave height, current velocities and river discharges; however, little effort has been paid to surveying sediment transport during dredging or the modification to currents outside the ports or marinas during and after the construction. This study shows a complete survey during the construction of one of the largest ports of the Gulf of Mexico. An anchored Acoustic Doppler Current Velocity profiler (ADCP), a towed ADCP and a combination of model outputs were used at the Veracruz port construction in order to describe the hourly sediment transport and current modifications in and out of the new port. Owing to the stability of the system the new port was construction inside Vergara Bay, a low wave energy system with a tidal range of up to 0.40 m. The results show a two-current system pattern within the bay. The north side of the bay has an anticyclonic gyre, while the southern part of the bay shows a cyclonic gyre. Sediment transport trajectories were made every hour using the anchored ADCP, a numerical model and the weekly data obtained from the towed ADCP within the entire bay. The sediment transport trajectories were carefully tracked since the bay is surrounded by coral reef structures which are sensitive to sedimentation rate and water turbidity. The survey shows that during dredging and rock input used to build the wave breaker sediments were locally added (< 2500 m²) and local currents disperse it in less than 4 h. While the river input located in the middle of the bay and the sewer system plant may add more than 10 times this amount during a rainy day or during the tourist season. Finally, the coastal line obtained seasonally with a drone suggests that the southern part of the bay has not been modified by the construction of the new port located in the northern part of the bay, owing to the two subsystem division of the bay.

Keywords: Acoustic Doppler Current Profiler, construction around coral reefs, dredging, port construction, sediment transport monitoring,

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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: Yan Bachtiar Muslih, Hangga Wijaya, Trio Fani, Putri Agustin

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Demand of Energy in Indonesia always increases 5-6% a year, but production of conventional energy always decreases 3-5% a year, it means that conventional energy in 20-40 years ahead will not able to complete all energy demand in Indonesia, one of the solve way is using unconventional energy that is gas hydrate, gas hydrate is gas that form by biogenic process, gas hydrate stable in condition with extremely depth and low temperature, gas hydrate can form in two condition that is in pole condition and in deep-sea condition, wherein this research will focus in gas hydrate that association with methane form methane hydrate in deep-sea condition and usually form in depth between 150-2000 m, this research will focus in process of methane hydrate formation that is biogenic process and the important role of deep-sea sediment so can produce accumulation of methane hydrate, methane hydrate usually will be accumulated in find sediment in deep-sea environment with condition high-pressure and low-temperature this condition too usually make methane hydrate change into white nodule, methodology of this research is geology field work and laboratory analysis, from geology field work will get sample data consist of 10-15 samples from Kerek Formation outcrops as random for imagine the condition of deep-sea environment that influence the methane hydrate formation and also from geology field work will get data of measuring stratigraphy in outcrops Kerek Formation too from this data will help to imagine the process in deep-sea sediment like energy flow, supply sediment, and etc, and laboratory analysis is activity to analyze all data that get from geology field work, the result of this research can used to exploration activity of methane hydrate in another prospect deep-sea environment in Indonesia.

Keywords: methane hydrate, deep-sea sediment, kerek formation, sub-basin of kendeng, central java, Indonesia

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Authors: Sepideh Beiramipour, Hadi Haghjouei, Kourosh Qaderi, Majid Rahimpour, Mohammad M. Ahmadi, Sameh A. Kantoush

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Reservoir sedimentation is one of the main challenges by which the reservoir behind the dam is filled with sediments transferred through the river flow. Pressure flushing method is an effective way to drain the deposited sediments of the reservoirs through the bottom outlet. So far, several structural methods have been proposed to increase the efficiency of pressure flushing. The aim of this study is to increase the performance of Dendritic Bottomless Extended (DBE) structure on the efficiency of pressurized sediment flushing using submerged vanes. For this purpose, the physical model of the dam reservoir with dimensions of 7.5 m in length, 3.5 m in width, and 1.8 m in height in the hydraulic and water structures research laboratory of Shahid Bahonar University of Kerman was used. In order to investigate the influence of submerged vanes on the performance of DBE structure in pressure flushing, the best arrangement and geometric parameters of the vanes were selected and combined with the DBE structure. The results showed that the submerged vanes significantly increased the performance of the DBE structure so that the volume of the sediment flushing cone with the combination of two structures increased by 3.7 times compared to the DBE structure test.

Keywords: dendritic bottomless extended structure, flushing efficiency, sedimentation, sediment flushing

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Authors: Ahmad Seiar Yasser

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Sediment is an important habitat for organisms and act as a store house for nutrients in aquatic ecosystems. Hydrogen sulfide is produced by microorganisms in the water columns and sediments, which is highly toxic and fatal to benthic organisms. However, the irons have the capacity to regulate the formation of sulfide by poising the redox sequence and to form insoluble iron sulfide and pyrite compounds. Therefore, we conducted two experiments aimed to evaluate the remediation efficiency of iron application to organically enrich and improve sediments environment. Experiments carried out in the laboratory using intact sediment cores taken from Mikawa Bay, Japan at every month from June to September 2017 and October 2018. In Experiment 1, after cores were collected, the iron powder or iron hydroxide were applied to the surface sediment with 5 g/ m2 or 5.6 g/ m2, respectively. In Experiment 2, we experimentally investigated the removal of hydrogen sulfide using (2mm or less and 2 to 5mm) of the steelmaking slag. Experiments are conducted both in the laboratory with the same boundary conditions. The overlying water were replaced with deoxygenated filtered seawater, and cores were sealed a top cap to keep anoxic condition with a stirrer to circulate the overlying water gently. The incubation experiments have been set in three treatments included the control, and each treatment replicated and were conducted with the same temperature of the in-situ conditions. Water samples were collected to measure the dissolved sulfide concentrations in the overlying water at appropriate time intervals by the methylene blue method. Sediment quality was also analyzed after the completion of the experiment. After the 21 days incubation, experimental results using iron powder and ferric hydroxide revealed that application of these iron containing materials significantly reduced sulfide release flux from the sediment into the overlying water. The average dissolved sulfides concentration in the overlying water of the treatment group was significantly decrease (p = .0001). While no significant difference was observed between the control group after 21 day incubation. Therefore, the application of iron to the sediment is a promising method to remediate contaminated sediments in a eutrophic water body, although ferric hydroxide has better hydrogen sulfide removal effects. Experiments using the steelmaking slag also clarified the fact that capping with (2mm or less and 2 to 5mm) of slag steelmaking is an effective technique for remediation of bottom sediments enriched organic containing hydrogen sulfide because it leads to the induction of chemical reaction between Fe and sulfides occur in sediments which did not occur in conditions naturally. Although (2mm or less) of slag steelmaking has better hydrogen sulfide removal effects. Because of economic reasons, the application of steelmaking slag to the sediment is a promising method to remediate contaminated sediments in the eutrophic water body.

Keywords: sedimentary, H2S, iron, iron hydroxide

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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: Redhouane Ghernaout, Amira Fredj, Boualem Remini

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Suspended sediment transport is a serious problem worldwide, but it is much more worrying in certain regions of the world, as is the case in the Maghreb and more particularly in Algeria. It continues to take disturbing proportions in Northern Algeria due to the variability of rains in time and in space and constant deterioration of vegetation. Its prediction is essential in order to identify its intensity and define the necessary actions for its reduction. The purpose of this study is to analyze the concentration data of suspended sediment measured at Wadi El-Abtal Hydrometric Station. It also aims to find and highlight regressive power relationships, which can explain the suspended solid flow by the measured liquid flow. The study strives to find models of artificial neural networks linking the flow, month and precipitation parameters with solid flow. The obtained results show that the power function of the solid transport rating curve and the models of artificial neural networks are appropriate methods for analysing and estimating suspended sediment transport in Wadi Mina at Wadi El-Abtal Hydrometric Station. They made it possible to identify in a fairly conclusive manner the model of neural networks with four input parameters: the liquid flow Q, the month and the daily precipitation measured at the representative stations (Frenda 013002 and Ain El-Hadid 013004 ) of the watershed. The model thus obtained makes it possible to estimate the daily solid flows (interpolate and extrapolate) even beyond the period of observation of solid flows (1985/86 to 1999/00), given the availability of the average daily liquid flows and daily precipitation since 1953/1954.

Keywords: suspended sediment, concentration, regression, liquid flow, solid flow, artificial neural network, modeling, mina, algeria

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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: Krishna Kumar Singh, Praveen Jain

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The collection of storm water runoff and forcing it into the groundwater is the need of the hour to sustain the ground water table. However, the runoff entraps various types of sediments and other floating objects whose removal are essential to avoid pollution of ground water and blocking of pores of aquifer. However, it requires regular cleaning and maintenance due to the problem of clogging. To evaluate the performance of filter system consisting of coarse sand (CS), gravel (G) and pebble (P) layers, a laboratory experiment was conducted in a rectangular column. The effect of variable thickness of CS, G and P layers of the filtration unit of the recharge shaft on the recharge rate and the sediment concentration of effluent water were evaluated. Medium sand (MS) of three particle sizes, viz. 0.150–0.300 mm (T1), 0.300–0.425 mm (T2) and 0.425–0.600 mm of thickness 25 cm, 30 cm, and 35 cm respectively in the top layer of the filter system and having seven influent sediment concentrations of 250–3,000 mg/l were used for the experimental study. The performance was evaluated in terms of recharge rates and clogging time. The results indicated that 100 % suspended solids were entrapped in the upper 10 cm layer of MS, the recharge rates declined sharply for influent concentrations of more than 1,000 mg/l. All treatments with a higher thickness of MS media indicated recharge rate slightly more than that of all treatment with a lower thickness of MS media respectively. The performance of storm water infiltration systems was highly dependent on the formation of a clogging layer at the filter. An empirical relationship has been derived between recharge rates, inflow sediment load, size of MS and thickness of MS with using MLR.

Keywords: groundwater, medium sand-mixed storm water filter, inflow sediment load

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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: Haïfa Ben Mna, Ayed Added

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To evaluate the trace metals contamination status in the Gulf of Tunis, forty one sediment samples were analyzed using different approaches. According to certain contamination and ecological risk indices (Contamination Factor, Geoaccumulation index and Ecological risk index), Hg has the highest contamination level while pollution by Ni, Pb, Cd and Cr was absent. The highest concentrations of trace metals were found in sediments collected from the offshore and coastal areas located opposite the main exchange points with the gulf particularly, the Mejerda and Meliane Rivers, the Khalij Channel, Ghar El Melh and El Malah lagoons, Tunis Lake and Sebkhat Ariana. However, further ecological indices (Potential ecological risk index, Toxic unit and Mean effect-range median quotient) and comparison with sediment quality guidelines suggest that in addition to Mercury, Cr, Pb and Ni concentrations are detrimental to biota in both the offshore and areas near to the exchange points with the gulf. Moreover, in these areas the results from sequential extraction and individual contamination factor calculation pointed to the mobility and bioavailability of Cr, Pb and Ni.

Keywords: sediment, trace metals, contamination assessment, ecological risk, Tunis gulf

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Authors: G. O. Avwiri, E. O. Agbalagba, C. P. Ononugbo

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Activity concentrations of natural radionuclides (226Ra, 232Th and 40K) in the soil, sediment and water of oil producing communities in Delta and Rivers States were determined using γ-ray spectrometry. The mean soil/sediment activity concentration of 226Ra, 232Th and 40K in onshore west in Delta state is 40.2±5.1Bqkg-1, 29.9±4.2Bqkg-1 and 361.5±20.0Bqkg-1 respectively, the corresponding values obtained in onshore east1 of Rivers state is 20.9±2.8Bqkg-1, 19.4±2.5Bqkg-1and 260.0±14.1Bqkg-1 respectively. While the mean activity concentration of 226Ra, 232Th and 40K in onshore east2 of Rivers state is 29.3±3.5Bqkg-1, 21.6±2.6Bqkg-1 and 262.1±14.6Bqkg-1 respectively. These values obtained show enhanced NORMs but are well within the world range. All the radiation hazard indices examined in soil have mean values lower than their maximum permissible limits. In drinking water, the obtained average values of226Ra, 228Ra and 40K is 8.4±0.9, 7.3±0.7 and 29.9±2.2Bql-1 respectively for well water, 4.5±0.6, 5.1±0.4 and 20.9±2.0Bql-1 respectively for borehole water and 11.3±1.2, 8.5±0.7 and 32.4±3.7Bql-1 respectively for river water in onshore west. For onshore east1, average activity concentration of 226Ra, 228Ra and 40K is 8.3±1.0, 8.6±1.1 and 39.6±3.3Bql-1 respectively for well water, 3.8±0.8, 4.9±0.6 and 35.7±4.1Bql-1 respectively for borehole water and 5.5±0.8, 5.4±0.7 and 36.9±3.8Bql-1 respectively for river water. While in onshore east2 average value of 226Ra, 228Ra and 40K is 10.1±1.1, 8.3±1.0 and 50.0±3.9Bql-1 respectively for well water, 4.7±0.9, 4.0±0.4 and 28.8±3.0Bql-1 respectively for borehole water and 7.7±0.9, 6.1±0.8 and 27.1±2.9Bql-1 respectively for river water and the average activity concentrations in the produced water226Ra, 228Ra and 40K is 5.182.14Bql-1, 6.042.48Bql-1 and 48.7813.67Bql-1 respectively. These values obtained are well above world average values of 1.0, 0.1 and 10Bql-1 for 226Ra, 228Ra and 40K respectively, those of the control site values and most reported values around the world. Though the hazard indices (Raeq, Hex, Hin) examined in water is still within the tolerable level, the committed effective dose estimated are above ICPR 0.1 mSvy-1 permissible limits. The overall results show that soil and sediment in the area are safe radiologically, but the result indicates some level of water pollution in the studied area.

Keywords: radioactivity, soil, sediment and water, Niger Delta, gamma detector

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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: Amir Hedjripour

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The required dewatering configuration for a contaminated sediment dam is discussed to meet no-spill criteria for a defined Average Recurrence Interval (ARI). There is an option for the sediment dam to pump the contaminated water to another storage facility before its capacity is exceeded. The system is subjected to a range of storm durations belonging to the design ARI with concurrent dewatering to the other storage facility. The model is set up in 1-minute time intervals and temporal patterns of storm events are used to de-segregate the total storm depth into partial durations. By running the model for selected storm durations, the maximum water volume in the dam is recorded as the critical volume, which indicates the required storage capacity for that storm duration. Runoff from upstream catchment and the direct rainfall over the dam open area are calculated by taking into account the time of concentration for the catchment. Total 99 different storm durations from 5 minutes to 72 hours were modelled together with five dewatering scenarios from 50 l/s to 500 l/s. The optimised dam/pump configuration is selected by plotting critical points for all cases and storage-dewatering envelopes. A simple economic analysis is also presented in the paper using Present-Value (PV) analysis to assist with the financial evaluation of each configuration and selection of the best alternative.

Keywords: contaminated water, optimisation, pump, sediment dam

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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

Procedia PDF Downloads 100

Authors: M. Fertouna Bellakhal, M. Bellakhal, A. Dhib, A. Fathalli, S. Turki, L. Aleya

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Study of dinoflagellate cysts is a precious tool to get information about environment and water quality in many aquatic ecosystems. The distribution of Alexandrium pacificum cysts, in Bizerta lagoon located in North of Tunisia, was made based on sediment samples analysis from 123 equidistant stations delimiting 125 km² surfaces. Sediment characteristics such as percentage of water, organic matter, and particle size were analyzed to determine the factors that influence the distribution of this dinoflagellate. In addition, morphological examination and ribotyping of vegetative forms from microalgal cultures made from cyst germination confirmed the identity of the species attributed to A. pacificum. A correlation between the abundance of A. pacificum cysts and the percentage of water and sediment organic matter was recorded. In addition, the sedimentary fraction < 63μm was found to be potentially favorable for the installation and initiation of the Alexandrium pacificum efflorescence at the Bizerte lagoon. The mapping of cysts in this aquatic ecosystem has also allowed us to define distinct areas with specific abundance with closed relationship with shellfish aquaculture stations located within the lagoon.

Keywords: Alexandrium pacificum, cysts, Dinoflagellate, microalgal culture

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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: 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: 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

Abstract:

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

Abstract:

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

Abstract:

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: Tiao Chang, Ivan Caballero, Hong Zhou

Abstract:

Streams carry water and sediment naturally by maintaining channel dimensions, pattern, and profile over time. Watershed erosion as a natural process has occurred to contribute sediment to streams over time. The formation of channel dimensions is complex. This study is to relate quantifiable and consistent channel dimensions at the bankfull stage to the corresponding watershed erosion estimation by the Revised Universal Soil Loss Equation (RUSLE). Twelve sites of which drainage areas range from 7 to 100 square miles in the Hocking River Basin of Ohio were selected for the bankfull geometry determinations including width, depth, cross-section area, bed slope, and drainage area. The twelve sub-watersheds were chosen to obtain a good overall representation of the Hocking River Basin. It is of interest to determine how these bankfull channel dimensions are related to the soil erosion of corresponding sub-watersheds. Soil erosion is a natural process that has occurred in a watershed over time. The RUSLE was applied to estimate erosions of the twelve selected sub-watersheds where the bankfull geometry measurements were conducted. These quantified erosions of sub-watersheds are used to investigate correlations with bankfull channel dimensions including discharge, channel width, channel depth, cross-sectional area, and pebble distribution. It is found that drainage area, bankfull discharge and cross-sectional area correlates strongly with watershed erosion well. Furthermore, bankfull width and depth are moderately correlated with watershed erosion while the particle size, D50, of channel bed sediment is not well correlated with watershed erosion.

Keywords: watershed, stream, sediment, channel

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

Abstract:

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

Abstract:

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

Procedia PDF Downloads 445