Search results for: edge detection algorithm

Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

Abstract:

Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

Procedia PDF Downloads 155

Authors: Seyyed Mohammad Amin Mousavi Sagharchi, Alireza Mahmoudi Nasab, Tim Bakker

Abstract:

Introduction: Mycobacterium tuberculosis (MTB) is the most intelligent bacterium that existed in the world to our best knowledge. This bacterium can cause tuberculosis (TB) which is responsible for its spread speed and murder of millions of people around the world. MTB has the practical function to escape from anti-tuberculosis drugs (AT), for this purpose, it handles some mutations in the main genes and creates new patterns for inhibited genes. Method and materials: Researchers have their best tries to safely isolate MTB from the sputum specimens of 35 patients in some hospitals in the Tehran province and detect MTB by culture on Löwenstein-Jensen (LJ) medium and microscopic examination. DNA was extracted from the established bacterial colony by enzymatic extraction method. It was amplified by the polymerase chain reaction (PCR) method, reverse hybridization, and evaluation for detection of resistance genes; generally, researchers apply GenoType MTBDRplus assay. Results: Investigations of results declare us that 21 of the isolated specimens (about 60%) have mutation in rpoB gene, which resisted to rifampicin (most prevalence), and 8 of them (about 22.8%) have mutation in katG or inhA genes which resisted to isoniazid. Also, 4 of them (about 11.4%) don't have any mutation, and 2 of them (about 5.7%) have mutation in every three genes, which makes them resistant to the two drugs mentioned above. Conclusion: Rifampicin and isoniazid are two essential AT that using in the first line of treatment. Resistance in rpoB, and katG, and inhA genes related to mentioned drugs lead to ineffective treatment.

Keywords: mycobacterium tuberculosis, tuberculosis, drug resistance, isoniazid, rifampicin

Procedia PDF Downloads 96

Authors: Matias Micheletto, Rodrigo Santos, Sergio F. Ochoa

Abstract:

In developing countries, the most roads in rural areas are dirt road. They require frequent maintenance since are affected by erosive events, such as rain or wind, and the transit of heavy-weight trucks and machinery. Early detection of damages on the road condition is a key aspect, since it allows to reduce the main-tenance time and cost, and also the limitations for other vehicles to travel through. Most proposals that help address this problem require the explicit participation of drivers, a permanent internet connection, or important instrumentation in vehicles or roads. These constraints limit the suitability of these proposals when applied into developing regions, like in Latin America. This paper proposes an alternative method, based on unattended crowdsensing, to determine the quality of dirt roads in rural areas. This method involves the use of a mobile application that complements the road condition surveys carried out by organizations in charge of the road network maintenance, giving them early warnings about road areas that could be requiring maintenance. Drivers can also take advantage of the early warnings while they move through these roads. The method was evaluated using information from a public dataset. Although they are preliminary, the results indicate the proposal is potentially suitable to provide awareness about dirt roads condition to drivers, transportation authority and road maintenance companies.

Keywords: dirt roads automatic quality assessment, collaborative system, unattended crowdsensing method, roads quality awareness provision

Procedia PDF Downloads 200

Authors: Sadia Basar, Naila Habib, Awais Adnan

Abstract:

The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

Procedia PDF Downloads 376

Authors: Jia Xue, Frederica G. Lamar, Siyu Lin, Jennifer G. Lamori, Samendra Sherchan

Abstract:

Brackish water samples from Lake Pontchartrain in Louisiana were assessed for the presence of pathogenic amoeba Naegleria fowleri, which causes primary amoebic meningoencephalitis (PAM). In our study, quantitative polymerase chain reaction (qPCR) methods were used to determine N. fowleri, E. coli, and Enterococcus in water collected from Lake Pontchartrain. A total of 158 water samples were analyzed over the 10-month sampling period. Statistically significant positive correlation between water temperature and N. fowleri concentration was observed. N. fowleri target sequence was detected at 35.4% (56/158) of the water samples from ten sites around the Lake ranged from 11.6 GC/100 ml water to 457.8 GC/100 ml water. A single factor (ANOVA) analysis shows the average concentration of N. fowleri in summer (119.8 GC/100 ml) was significantly higher than in winter (58.6 GC/100 ml) (p < 0.01). Statistically significant positive correlations were found between N. fowleri and qPCR E. coli results and N. fowleri and colilert E. coli (culture method), respectively. A weak positive correlation between E. coli and Enterococcus was observed from both qPCR (r = 0.27, p < 0.05) and culture based method (r = 0.52, p < 0.05). Meanwhile, significant positive correlation between qPCR and culture based methods for E. coli (r = 0.30, p < 0.05) and Enterococcus concentration was observed (r = 0.26, p < 0.05), respectively. Future research is needed to determine whether sediment is a source of N. fowleri found in the water column.

Keywords: brackish water, Escherichia coli, Enterococcus, Naegleria fowleri, primary amoebic meningoencephalitis (PAM), qPCR

Procedia PDF Downloads 161

Authors: K. Petcharaporn

Abstract:

The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

Procedia PDF Downloads 363

Authors: Chaya B. M., C. Dhanush, Inti Sai Srikar, Akula Pavan Parvatalu, Chirag Gowda R

Abstract:

Recently, there has been a lot of interest in µ-LED technology because of its exceptional qualities, including auto emission, high visibility, low consumption of power, rapid response and longevity. Light-emitting diodes (LED) using III-nitride, such as lighting sources, visible light communication (VLC) devices, and high-power devices, are finding increasing use as miniaturization technology advances. The use of micro-LED displays in place of traditional display technologies like liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) is one of the most prominent recent advances, which may even represent the next generation of displays. The development of fully integrated, multifunctional devices and the incorporation of extra capabilities into micro-LED displays, such as sensing, light detection, and solar cells, are the pillars of advanced technology. Due to the wide range of applications for micro-LED technology, the effectiveness and dependability of these devices in numerous harsh conditions are becoming increasingly important. Enough research has been conducted to overcome the under-effectiveness of micro-LED devices. In this paper, different Micro LED design structures are proposed in order to achieve optimized optical properties. In order to attain improved external quantum efficiency (EQE), devices' light extraction efficiency (LEE) has also been boosted.

Keywords: finite difference time domain, light out coupling efficiency, far field intensity, power density, quantum efficiency, flat panel displays

Procedia PDF Downloads 79

Authors: Terry Teo, Sun Woh Lye, Yufei Li, Zainuddin Zakaria

Abstract:

Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that when using the channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available BCI 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities.

Keywords: assessment, neurophysiology, monitoring, EEG

Procedia PDF Downloads 76

Authors: Ali Akbar Heydari

Abstract:

Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values.

Keywords: control chart, markov chain approach, statistical design, synthetic, variable parameter

Procedia PDF Downloads 154

Authors: Sungchul Ha, Hyunwoo Kim

Abstract:

In mobile ad hoc networks, all nodes in a network only have limited resources and calculation ability. Therefore communication topology which have long lifetime is good for all nodes in mobile ad hoc networks. There are a variety of researches on security problems in wireless ad hoc networks. The existing many researches try to make efficient security schemes to reduce network power consumption and enhance network lifetime. Because a new node can join the network at any time, the wireless ad hoc networks are exposed to various threats and can be destroyed by attacks. Resource consumption is absolutely necessary to secure networks, but more resource consumption can be a critical problem to network lifetime. This paper focuses on efficient monitoring node distribution to enhance network lifetime in wireless ad hoc networks. Since the wireless ad hoc networks cannot use centralized infrastructure and security systems of wired networks, a new special IDS scheme is necessary. The scheme should not only cover all nodes in a network but also enhance the network lifetime. In this paper, we propose an efficient IDS node distribution scheme using minimum spanning tree (MST) method. The simulation results show that the proposed algorithm has superior performance in comparison with existing algorithms.

Keywords: MANETs, IDS, power control, minimum spanning tree

Procedia PDF Downloads 372

Authors: Sidali Khedidji, Riad Ladji, Noureddine Yassaa

Abstract:

In this study, particulate matter (PM10) which are hazardous for environment and human health were investigated in Sour El Ghozlane suburban atmosphere at a sampling point from March 2013 to April 2013. Ambient concentration measurements of polycyclic aromatic hydrocarbons were carried out at a regional study of the cement industry in Sour El Ghozlane. During sampling, the airborne particulate matter was enriched onto PTFE filters by using a two medium volume samplers with or without a size-selective inlet for PM10 and TSP were used and each sampling period lasted approximately 24 h. The organic compounds were characterized using gas chromatography coupled with mass spectrometric detection (GC-MSD). Total concentrations for PAHs recorded in sour el ghozlane suburban ranged from 101 to 204 ng m-3. Gravimeter method was applied to the black smoke concentration data for Springer seasons. The 24 h average concentrations of PM10 and TSP of Sour El Ghozlane suburban atmosphere were found in the range 4.76–165.76 μg/m3 and 28.63–800.14 μg/m3, respectively, in the sampling period. Meteorological factors, such as (relative humidity and temperature) were typically found to be affecting PMs, especially PM10. Air temperature did not seem to be significantly affecting TSP and PM10 mass concentrations.The guide value fixed by the European Community «40 μg/m3» not to exceed 35 days, were exceeded in some samples. However, it should be noted that the value limit fixed by the Algerian regulations «80 μg/m3» has been exceeded in 3 samplers during the period study.

Keywords: PAHs, PM10, TSP, particulate matter, cement industry

Procedia PDF Downloads 378

Authors: S. Ayeko, J. Milton, C. Hughes, K. Anderson, R. G. Middleton

Abstract:

Background: Periprosthetic femoral fractures (PFF) in association with hip hemiarthroplasty or total hip arthroplasty is a common and serious complication. In the Vancouver Classification system algorithm, B1 fractures should be treated with Open Reduction and Internal Fixation (ORIF) and preferentially revised in combination with ORIF if B2 or B3. This study aims to assess patient outcomes after plate osteosynthesis alone for Vancouver B1 and B2 fractures. The main outcome is the 1-year re-revision rate, and secondary outcomes are 30-day and 1-year mortality. Method: This is a retrospective single-centre case-series review from January 2016 to June 2021. Vancouver B1 and B2, non-malignancy fractures in adults over 18 years of age treated with polyaxial Non-Contact Bridging plate osteosynthesis, have been included. Outcomes were gathered from electronic notes and radiographs. Results: There were 50 B1 and 64 B2 fractures. 26 B2 fractures were managed with ORIF and revision, 39 ORIF alone. Of the revision group, one died within 30 days (3.8%), one at one year (3.8%), and two were revised within one year (7.7). Of the B2 ORIF group, three died within 30-day mortality (7.96%), eight at one year (21.1%), and 0 were revised in 1 year. Conclusion: This study has demonstrated that satisfactory outcomes can be achieved with ORIF, excluding revision in the management of B2 fractures.

Keywords: arthroplasty, bridging plate, periprosthetic fracture, revision surgery

Procedia PDF Downloads 101

Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

Abstract:

This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

Procedia PDF Downloads 114

Authors: Nasir Khalid, S. Thirumalaichelvam

Abstract:

In most buildings, according to US Department of Energy Data Book, the electrical consumption attributable to centralized heating and ventilation of air- condition (HVAC) component can be as high as 40-60% of the total electricity consumption for an entire building. To provide efficient energy management for the market today, researchers are finding new ways to develop a system that can save electrical consumption of buildings even more. In this concept paper, a system known as Intelligent Chiller Energy Efficiency (iCEE) System is being developed that is capable of saving up to 25% from the chiller’s existing electrical energy consumption. In variable frequency drives (VFDs), research has found significant savings up to 30% of electrical energy consumption. Together with the VFDs at specific Air Handling Unit (AHU) of HVAC component, this system will save even more electrical energy consumption. The iCEE System is compatible with any make, model or age of centrifugal, rotary or reciprocating chiller air-conditioning systems which are electrically driven. The iCEE system uses engineering principles of efficiency analysis, enthalpy analysis, heat transfer, mathematical prediction, modified genetic algorithm, psychometrics analysis, and optimization formulation to achieve true and tangible energy savings for consumers.

Keywords: variable frequency drives, adjustable speed drives, ac drives, chiller energy system

Procedia PDF Downloads 558

Authors: Morteza Haghi, Cigdem Yilmazbas, Ayse Zeynep Uysal, Melisa Tepedelen, Gozde Turkoz Bakirci

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Today, the increase in plastic waste accumulation is an inescapable consequence of environmental pollution; the disposal of these wastes has caused a significant problem. Variable methods have been utilized; however, biodegradation is the most environmentally friendly and low-cost method. Accordingly, the present study aimed to isolate the bacteria capable of biodegradation of plastics. In doing so, we applied the liquid carbon-free basal medium (LCFBM) prepared with deionized water for the isolation of bacterial species obtained from soil samples taken from the Izmir Menemen region. Isolates forming biofilms on plastic were selected and named (PLB3, PLF1, PLB1B) and subjected to a degradation test. FTIR analysis, 16s rDNA amplification, sequencing, identification of isolates were performed. Finally, at the end of the process, a mass loss of 16.6% in PLB3 isolate and 25% in PLF1 isolate was observed, while no mass loss was detected in PLB1B isolate. Only PLF1 and PLB1B created transparent zones on plastic texture. Considering the FTIR result, PLB3 changed plastic structure by 13.6% and PLF1 by 17%, while PLB1B did not change the plastic texture. According to the 16s rDNA sequence analysis, FLP1, PLB1B, and PLB3 isolates were identified as Streptomyces albogriseolus, Enterobacter cloacae, and Klebsiella pneumoniae, respectively.

Keywords: polyethylene, biodegradation, bacteria, 16s rDNA, FTIR

Procedia PDF Downloads 203

Authors: Farnoush Haghanipour

Abstract:

Objective: Father loss as one of the major stress factor, can causethe mental imbalances in children. It's clear that children's family condition of lacking a father is very clearly different from the condition of having a father. The goal of this research is to examine mental imbalances comparative checking in complete form and in five subsidiary categories as aggression, stress and depression, social incompatibility, anti-social behavior, and attention deficit imbalances (wackiness) do between children without father and normal ones. Method: This research is in descriptive and analytical method that reimburse to checking mental imbalances from 50 children that are student in one zone of Rasht’s education and nurture office. Material of this research is RATER behavior questionnaire (teacher form) and data analyses were did by SPSS software. Results: The results showed that there are clear different in relation with behavior imbalances between have father children and children without father and in children without a father behavior imbalance is more. Also showed that there is clearly a difference in aggression, stress, and depression and social incompatibility between children without and without fathers, and in children without a father the proportion increases. However, in antisocial behaviours and attention deficit imbalances there are not a clear difference between them. Conclusion: With upper amount of imbalance behaviour detection in children without fathers compared with children with fathers, it is essential that practitioners of society hygienic and remedy put efforts in order to primary and secondary prevention, for mental health of this group of society.

Keywords: child, behave imbalances, children without father, mental imbalances

Procedia PDF Downloads 256

Authors: Hatem Mrad, Mohamed Bouazara, Fouad Erchiqui

Abstract:

Fatigue, is considered as one of the main cause of mechanical properties degradation of mechanical parts. Probability and reliability methods are appropriate for fatigue analysis using uncertainties that exist in fatigue material or process parameters. Current work deals with the study of the effect of the number and counting Rainflow cycle on fatigue lifetime (cumulative damage) of an upper arm of the vehicle suspension system. The major part of the fatigue damage induced in suspension arm is caused by two main classes of parameters. The first is related to the materials properties and the second is the road excitation or the applied force of the passenger’s number. Therefore, Young's modulus and road excitation are selected as input parameters to conduct repetitive simulations by Monte Carlo (MC) algorithm. Latin hypercube sampling method is used to generate these parameters. Response surface method is established according to fatigue lifetime of each combination of input parameters according to strain-life method. A PYTHON script was developed to automatize finite element simulations of the upper arm according to a design of experiments.

Keywords: fatigue, monte carlo, rainflow cycle, response surface, suspension system

Procedia PDF Downloads 256

Authors: Iman Adnan Annon, Kadhim F. Alsultan

Abstract:

This study investigate the internal corrosion of low carbon steel pipelines in the crude oil, as well as prepare and use natural and locally available plant as a natural corrosion inhibiter, the nature extraction achieved by two types of solvents in order to show the solvent effect on inhibition process, the first being distilled water and the second is diethyl ether. FT-IR spectra and using a chemical reagents achieved to detection the presence of many active groups and the presence of tannins, phenols, and alkaloids in the natural extraction. Some experiments were achieved to estimate the performance of a new inhibitor, one of these tests include corrosion measurement by simple immersion in crude oil within and without inhibitors which added in different amounts 30,40,50and 60 ppm at tow temperature 300 and 323k, where the best inhibition efficiencies which get when added the inhibitors in a critical amounts or closest to it, since for the aqueous extract (EB-A) the inhibition efficiency reached (94.4) and (86.71)% at 300 and 323k respectively, and for diethyl ether extract (EB-D) reached (82.87) and (84.6)% at 300 and 323k respectively. Optical microscopy examination have been conducted to evaluate the corrosion nature where it show a clear difference in the topography of the immersed samples surface after add the inhibitors at two temperatures. The results show that the new corrosion inhibitor is not only equivalent to a chemical inhibitor but has greatly improvement properties such as: high efficiency, low cost, non-toxic, easily to produce, and nonpolluting as compared with chemical inhibitor.

Keywords: corrosion in pipeline, inhibitors, crude oil, carbon steel, types of solvent

Procedia PDF Downloads 140

Authors: Pisit Poolprasert, Phakin Kubchanan, Keerati Tanruean, Wisanu Thongchai, Yuttasak Chammui, Wirot Likittrakulwong

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Rice insect pests are problems to rice production. Use of chemicals to minimize these problems of insect pests in paddy field can lead to the residue and affect the health of farmers. Therefore, botanical extracts applied for controlling rice serious enemies should be promoted especially use of plant extract as attractants to lure insects. This research aimed to feasibility study of bioattractants from pandanus palm extracts for trapping insect pets using two different trap models, including plastic bottle and yellow sticky traps. Two main growth and development stages of rice, namely tillering and booting stages, were selected and trapped. The results from both trap models revealed that four rice insect species, including Orseolia oryzae (Wood-Mason), Nilaparvata lugens, Recilia dorsalis, and Nephotettix nigropictus from three families (Cecidomyiidae, Cicadellidae and Delphacidae) and two main orders (Diptera and Hemiptera) were exhibited. All rice insect species mentioned could be found from the yellow sticky trap that were higher than in the bottle trap in which only O. oryzae could be only trapped. From this survey, it was indicated that the yellow sticky trap coated with pandanus palm extracts had a promising potential to use as an attractant for the detection of rice paddy insects in the next future.

Keywords: pandanus palm, bioattractant, bottle trap, yellow sticky trap

Procedia PDF Downloads 125

Authors: Mohammadreza Bakhtiari, Mehrdad Maghsoudi, Hamidreza Bakhtiari

Abstract:

The widespread adoption of laptops has become essential to modern lifestyles, supporting work, education, and entertainment. Social media platforms have emerged as key spaces where users share real-time feedback on laptop performance, providing a valuable source of data for understanding consumer preferences. This study leverages aspect-based sentiment analysis (ABSA) on 1.5 million tweets to examine how users from developed and developing countries perceive and prioritize 16 key laptop features. The analysis reveals that consumers in developing countries express higher satisfaction overall, emphasizing affordability, durability, and reliability. Conversely, users in developed countries demonstrate more critical attitudes, especially toward performance-related aspects such as cooling systems, battery life, and chargers. The study employs a mixed-methods approach, combining ABSA using the PyABSA framework with expert insights gathered through a Delphi panel of ten industry professionals. Data preprocessing included cleaning, filtering, and aspect extraction from tweets. Universal issues such as battery efficiency and fan performance were identified, reflecting shared challenges across markets. However, priorities diverge between regions, while users in developed countries demand high-performance models with advanced features, those in developing countries seek products that offer strong value for money and long-term durability. The findings suggest that laptop manufacturers should adopt a market-specific strategy by developing differentiated product lines. For developed markets, the focus should be on cutting-edge technologies, enhanced cooling solutions, and comprehensive warranty services. In developing markets, emphasis should be placed on affordability, versatile port options, and robust designs. Additionally, the study highlights the importance of universal charging solutions and continuous sentiment monitoring to adapt to evolving consumer needs. This research offers practical insights for manufacturers seeking to optimize product development and marketing strategies for global markets, ensuring enhanced user satisfaction and long-term competitiveness. Future studies could explore multi-source data integration and conduct longitudinal analyses to capture changing trends over time.

Keywords: consumer behavior, durability, laptop industry, sentiment analysis, social media analytics

Procedia PDF Downloads 15

Authors: Budoor Al Abid

Abstract:

Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only.

Keywords: machine learning, adaptive, fuzzy logic, data mining

Procedia PDF Downloads 197

Authors: K. Petcharaporn, N. Prathengjit

Abstract:

The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

Procedia PDF Downloads 519

Authors: Xiaoming Jiang, Jinqiao Shi, Qingfeng Tan, Wentao Zhang, Xuebin Wang, Muqian Chen

Abstract:

Nowadays, big companies such as Google, Microsoft, which have adequate proxy servers, have perfectly implemented their web crawlers for a certain website in parallel. But due to lack of expensive proxy servers, it is still a puzzle for researchers to crawl large amounts of information from a single website in parallel. In this case, it is a good choice for researchers to use free public proxy servers which are crawled from the Internet. In order to improve efficiency of web crawler, the following two issues should be considered primarily: (1) Tasks may fail owing to the instability of free proxy servers; (2) A proxy server will be blocked if it visits a single website frequently. In this paper, we propose Proxisch, an optimization approach of large-scale unstable proxy servers scheduling, which allow anyone with extremely low cost to run a web crawler efficiently. Proxisch is designed to work efficiently by making maximum use of reliable proxy servers. To solve second problem, it establishes a frequency control mechanism which can ensure the visiting frequency of any chosen proxy server below the website’s limit. The results show that our approach performs better than the other scheduling algorithms.

Keywords: proxy server, priority queue, optimization algorithm, distributed web crawling

Procedia PDF Downloads 212

Authors: Yuling Cui, Danhao Deng, Chaowei Wang, Weidong Wang

Abstract:

In recent years, unmanned aerial vehicles (UAVs) have been widely used in wireless communication, attracting more and more attention from researchers. UAVs can not only serve as a relay for auxiliary communication but also serve as an aerial base station for ground users (GUs). However, limited energy means that they cannot work all the time and cover a limited range of services. In this paper, we investigate 2D UAV trajectory design and power allocation in order to maximize the UAV's service time and downlink throughput. Based on deep reinforcement learning, we propose a depth deterministic strategy gradient algorithm for trajectory design and power distribution (TDPA-DDPG) to solve the energy-efficient and communication service quality problem. The simulation results show that TDPA-DDPG can extend the service time of UAV as much as possible, improve the communication service quality, and realize the maximization of downlink throughput, which is significantly improved compared with existing methods.

Keywords: UAV trajectory design, power allocation, energy efficient, downlink throughput, deep reinforcement learning, DDPG

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Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Paffrath Meinhard, Zhou Yayun, Guo Yiqing, Ertl Harald

Abstract:

Winding geometry design is an important part of power transformer electrical design. Conventionally, the winding geometry is designed manually, which is a time-consuming job because it involves many iteration steps in order to meet all cost, manufacturing and electrical requirements. Here a method is presented which automatically generates the winding geometry for given user parameters and allows the user to interactively set and change parameters. To achieve this goal, the winding problem is transferred to a mixed integer nonlinear optimization problem. The relevant geometrical design parameters are defined as optimization variables. The cost and other requirements are modeled as constraints. For the solution, a stochastic ant colony optimization algorithm is applied. It is well-known, that an optimizer can get stuck in a local minimum. For the winding problem, we present efficient strategies to come out of local minima, furthermore a reduced variable search range helps to accelerate the solution process. Numerical examples show that the optimization result is delivered within seconds such that the user can interactively change the variable search area and constraints to improve the design.

Keywords: ant colony optimization, mixed integer nonlinear programming, power transformer, winding design

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Authors: T. Garba, Y. Y. Sabo A. Babanyara, K. G. Ilellah, A. K. Mutari

Abstract:

This study used the Normalized Difference Vegetation Index (NDVI) and maps compiled from the classification of Landsat TM and Landsat ETM images of 1986 and 1999 respectively and Nigeria sat 1 images of 2007 to quantify changes in land use and land cover in selected areas of Nigeria covering 143,609 hectares that are threatened by the encroaching Sahara desert. The results of this investigation revealed a decrease in natural vegetation over the three time slices (1986, 1999 and 2007) which was characterised by an increase in high positive pixel values from 0.04 in 1986 to 0.22 and 0.32 in 1999 and 2007 respectively and, a decrease in natural vegetation from 74,411.60ha in 1986 to 28,591.93ha and 21,819.19ha in 1999 and 2007 respectively. The same results also revealed a periodic trend in which there was progressive increase in the cultivated area from 60,191.87ha in 1986 to 104,376.07ha in 1999 and a terminal decrease to 88,868.31ha in 2007. These findings point to expansion of vegetated and cultivated areas in in the initial period between 1988 and 1996 and reversal of these increases in the terminal period between 1988 and 1996. The study also revealed progressive expansion of built-up areas from 1, 681.68ha in 1986 to 2,661.82ha in 1999 and to 3,765.35ha in 2007. These results argue for the urgent need to protect and conserve the depleting natural vegetation by adopting sustainable human resource use practices i.e. intensive farming in order to minimize persistent depletion of natural vegetation.

Keywords: changes, classification, desertification, vegetation changes

Procedia PDF Downloads 387

Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

Abstract:

The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: Masahiro Miyaji

Abstract:

When a driver happens to be involved in some traffic congestion or after traffic incidents, the driver may fall in a state of anger. State of anger may encounter decisive risk resulting in severer traffic accidents. Preventive safety function using driver’s psychosomatic state with regard to anger may be one of solutions which would avoid that kind of risks. Identifying driver’s anger state is important to create countermeasures to prevent the risk of traffic accidents. As a first step, this research figured out root cause of traffic incidents by means of using Internet survey. From statistical analysis of the survey, dominant psychosomatic states immediately before traffic incidents were haste, distraction, drowsiness and anger. Then, we replicated anger state of a driver while driving, and then, replicated it by means of using driving simulator on bench test basis. Six types of facial expressions including anger were introduced as alternative characteristics. Kohonen neural network was adopted to classify anger state. Then, we created a methodology to detect anger state of a driver in high accuracy. We presented a driving support safety function. The function adapts driver’s anger state in cooperation with an autonomous driving unit to reduce the number of traffic accidents. Consequently, e evaluated reduction rate of driver’s anger in the traffic accident. To validate the estimation results, we referred the reduction rate of Advanced Safety Vehicle (ASV) as well as Intelligent Transportation Systems (ITS).

Keywords: Kohonen neural network, driver’s anger state, reduction of traffic accidents, driver’s state adaptive driving support safety

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Authors: Alime Cengiz, Talip Kahyaoglu

Abstract:

Roasting process has the major importance to obtain desired aromatic taste of nuts. In this study, two kinds of roasting process were applied to hulled sesame seeds - vacuum oven and hot air roasting. Efficiency of Gene Expression Programming (GEP), a new soft computing technique of evolutionary algorithm that describes the cause and effect relationships in the data modelling system, and response surface methodology (RSM) were examined in the modelling of roasting processes over a range of temperature (120-180°C) for various times (30-60 min). Color attributes (L*, a*, b*, Browning Index (BI)), textural properties (hardness and fracturability) and moisture content were evaluated and modelled by RSM and GEP. The GEP-based formulations and RSM approach were compared with experimental results and evaluated according to correlation coefficients. The results showed that both GEP and RSM were found to be able to adequately learn the relation between roasting conditions and physical and textural parameters of roasted seeds. However, GEP had better prediction performance than the RSM with the high correlation coefficients (R2 >0.92) for the all quality parameters. This result indicates that the soft computing techniques have better capability for describing the physical changes occuring in sesame seeds during roasting process.

Keywords: genetic expression programming, response surface methodology, roasting, sesame seed

Procedia PDF Downloads 418