Search results for: real time anomaly detection

Authors: T. V. Dinh, I. Y. Choi, J. W. Ahn, Y. H. Oh, G. Bo, J. Y. Lee, J. C. Kim

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A Non-Dispersive Infrared (NDIR) multi-gas analyzer has been developed to monitor the emission of carbon monoxide (CO) and sulfur dioxide (SO2) from various industries. The NDIR technique for gas measurement is based on the wavelength absorption in the infrared spectrum as a way to detect particular gasses. NDIR analyzers have popularly applied in the Tele-Monitoring System (TMS). The advantage of the NDIR analyzer is low energy consumption and cost compared with other spectroscopy methods. However, zero/span drift and interference are its urgent issues to be solved. Multi-pathway technique based on optical White cell was employed to improve the sensitivity of the analyzer in this work. A pyroelectric detector was used to detect the Infrared radiation. The analytical range of the analyzer was 0 ~ 200 ppm. The instrument response time was < 2 min. The detection limits of CO and SO2 were < 4 ppm and < 6 ppm, respectively. The zero and span drift of 24 h was less than 3%. The linearity of the analyzer was less than 2.5% of reference values. The precision and accuracy of both CO and SO2 channels were < 2.5% of relative standard deviation. In general, the analyzer performed well. However, the detection limit and 24h drift should be improved to be a more competitive instrument.

Keywords: analyzer, CEMS, monitoring, NDIR, TMS

Procedia PDF Downloads 250

Authors: Giuseppe Spampinato, Salvatore Curti, Ivana Guarneri, Arcangelo Bruna

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This document describes an advanced system and methodology for Cross Traffic Alert (CTA), able to detect vehicles that move into the vehicle driving path from the left or right side. The camera is supposed to be not only on a vehicle still, e.g. at a traffic light or at an intersection, but also moving slowly, e.g. in a car park. In all of the aforementioned conditions, a driver’s short loss of concentration or distraction can easily lead to a serious accident. A valid support to avoid these kinds of car crashes is represented by the proposed system. It is an extension of our previous work, related to a clustering system, which only works on fixed cameras. Just a vanish point calculation and simple optical flow filtering, to eliminate motion vectors due to the car relative movement, is performed to let the system achieve high performances with different scenarios, cameras and resolutions. The proposed system just uses as input the optical flow, which is hardware implemented in the proposed platform and since the elaboration of the whole system is really speed and power consumption, it is inserted directly in the camera framework, allowing to execute all the processing in real-time.

Keywords: clustering, cross traffic alert, optical flow, real time, vanishing point

Procedia PDF Downloads 195

Authors: Andrey Vinajera-Zamora, Norge Coello-Machado, Elke Glistau

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The objective of this research is calculate the added value in operations of electric power meters (EPM) supply chains, specifically the EPM of 220v. The tool used is composed by six steps allowing at same time the identification of calibration of EPM as the bottleneck operation according the net added value being at same time the process of higher added value. On the other hand, this methodology allows calculate the amount of money to buy the raw material. The main conclusions are related to the analyze ‘s way and calculating of added value in supply chain integrated by the echelons procurement, production and distribution or any of these.

Keywords: economic value added, supply chain management, value chain, bottleneck detection

Procedia PDF Downloads 292

Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

Procedia PDF Downloads 120

Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

Procedia PDF Downloads 89

Authors: Ching-Sung Wang, Teng-Wei Wang, Zhen-Ting Zheng

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This study mainly concerns a safety device designed for child care. When children are out of sight or the caregivers cannot always pay attention to the situation, through the functions of this device, caregivers can immediately be informed to make sure that the children do not get lost or hurt, and thus, ensure their safety. Starting from this concept, a device is produced based on the relatively low-cost Bluetooth piconet system and a three-axis gyroscope sensor. This device can transmit data to a mobile phone app through Bluetooth, in order that the user can learn the situation at any time. By simply clipping the device in a pocket or on the waist, after switching on/starting the device, it will send data to the phone to detect the child’s fall and distance. Once the child is beyond the angle or distance set by the app, it will issue a warning to inform the phone owner.

Keywords: children care, piconet system, three-axis gyroscope, distance detection, falls detection

Procedia PDF Downloads 591

Authors: Won Hyuck Kim, Chang Hwan Kim, Hyun Wook Kim, Myoung Hoon Lee, Chan Hong Park, Hyeon Yeong Park

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We conducted submarine topography & beach survey from December 2015 and January 2016 using multi-beam echo sounder EM3001(Kongsberg corporation) & Shipborne Mobile LiDAR System. Our survey area were the Anmok beach in Gangneung, South Korea. We made Shipborne Mobile LiDAR System for these survey. Shipborne Mobile LiDAR System includes LiDAR (RIEGL LMS-420i), IMU ((Inertial Measurement Unit, MAGUS Inertial+) and RTKGNSS (Real Time Kinematic Global Navigation Satellite System, LEIAC GS 15 GS25) for beach's measurement, LiDAR's motion compensation & precise position. Shipborne Mobile LiDAR System scans beach on the movable vessel using the laser. We mounted Shipborne Mobile LiDAR System on the top of the vessel. Before beach survey, we conducted eight circles IMU calibration survey for stabilizing heading of IMU. This exploration should be as close as possible to the beach. But our vessel could not come closer to the beach because of latency objects in the water. At the same time, we conduct submarine topography survey using multi-beam echo sounder EM3001. A multi-beam echo sounder is a device observing and recording the submarine topography using sound wave. We mounted multi-beam echo sounder on left side of the vessel. We were equipped with a motion sensor, DGNSS (Differential Global Navigation Satellite System), and SV (Sound velocity) sensor for the vessel's motion compensation, vessel's position, and the velocity of sound of seawater. Shipborne Mobile LiDAR System was able to reduce the consuming time of beach survey rather than previous conventional methods of beach survey.

Keywords: Anmok, beach survey, Shipborne Mobile LiDAR System, submarine topography

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

Procedia PDF Downloads 124

Authors: Neelamshobha Nirala, R. Periyasamy, Awanish Kumar

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Diabetes is a metabolic disorder and with the increase of global prevalence of diabetes, cardiovascular diseases and mortality related to diabetes has also increased. Diabetes causes the increase of arterial stiffness by elusive hormonal and metabolic abnormalities. We used photoplethysmograph (PPG), a simple non-invasive method to study the change in arterial stiffness due to diabetes. Toe PPG signals were taken from 29 diabetic subjects with mean age of (65±8.4) years and 21 non-diabetic subjects of mean age of (49±14) years. Mean duration of diabetes is 12±8 years for diabetic group. Rise-time (RT) and area under rise time (AUR) were calculated from the PPG signal of each subject and Welch’s t-test is used to find the significant difference between two groups. We obtained a significant difference of (p-value) 0.0005 and 0.03 for RT and AUR respectively between diabetic and non-diabetic subjects. Average value of RT and AUR is 0.298±0.003 msec and 14.4±4.2 arbitrary units respectively for diabetic subject compared to 0.277±0.0005 msec and 13.66±2.3 a.u respectively for non-diabetic subjects. In conclusion, this study support that arterial stiffness is increased in diabetes and can be detected early using PPG.

Keywords: area under rise-time, AUR, arterial stiffness, diabetes, photoplethysmograph, PPG, rise-time (RT)

Procedia PDF Downloads 252

Authors: Azadeh Afrasyabi, Ali Khaleghi, Aliakbar Alijarahi

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Training at an early age is so important, because of tremendous changes in adolescence, including the formation of character, physical changes and other factors. One of the most sensitive sectors in this field is the children with a disability and are somehow special children who have trouble in communicating with their environment. One of the emerging technologies in the field of education that can be effectively profitable called augmented reality, where the combination of real world and virtual images in real time produces new concepts that can facilitate learning. The purpose of this paper is to propose an effective training method for special and disabled children based on augmented reality. Of course, in particular, the efficiency of augmented reality in teaching children with autism will consider, also examine the various aspect of this disease and different learning methods in this area.

Keywords: technology in education, augmented reality, special education, teaching methods

Procedia PDF Downloads 366

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 55

Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Layan Moussa, Darine Salam, Samir Mustapha

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Heavy metal contamination in agricultural soils in Lebanon poses serious environmental and health problems. Intensive efforts are employed to improve existing quantification methods of heavy metals in contaminated environments since conventional detection techniques have shown to be time-consuming, tedious, and costly. The implication of hyperspectral remote sensing in this field is possible and promising. However, factors impacting the efficiency of hyperspectral imaging in detecting and quantifying heavy metals in agricultural soils were not thoroughly studied. This study proposes to assess the use of hyperspectral imaging for the detection of Ni in agricultural clay soil collected from the Bekaa Valley, a major agricultural area in Lebanon, under different contamination levels and soil moisture content. Soil samples were contaminated with Ni, with concentrations ranging from 150 mg/kg to 4000 mg/kg. On the other hand, soil with background contamination was subjected to increased moisture levels varying from 5 to 75%. Hyperspectral imaging was used to detect and quantify Ni contamination in the soil at different contamination levels and moisture content. IBM SPSS statistical software was used to develop models that predict the concentration of Ni and moisture content in agricultural soil. The models were constructed using linear regression algorithms. The spectral curves obtained reflected an inverse correlation between both Ni concentration and moisture content with respect to reflectance. On the other hand, the models developed resulted in high values of predicted R2 of 0.763 for Ni concentration and 0.854 for moisture content. Those predictions stated that Ni presence was well expressed near 2200 nm and that of moisture was at 1900 nm. The results from this study would allow us to define the potential of using the hyperspectral imaging (HSI) technique as a reliable and cost-effective alternative for heavy metal pollution detection in contaminated soils and soil moisture prediction.

Keywords: heavy metals, hyperspectral imaging, moisture content, soil contamination

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Authors: João Madureira, Ricardo Lagido, Inês Sousa, Fraunhofer Portugal

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Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.

Keywords: inertial measurement unit (IMU), global positioning system (GPS), smartphone, surfing performance

Procedia PDF Downloads 398

Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: B. Kriheli, E. Levner, T. C. E. Cheng, C. T. Ng

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During the last decades, research interest in planning, scheduling, and control of emergency response operations, especially people rescue and evacuation from the dangerous zone of marine accidents, has increased dramatically. Until the survivors (called ‘targets’) are found and saved, it may cause loss or damage whose extent depends on the location of the targets and the search duration. The problem is to efficiently search for and detect/rescue the targets as soon as possible with the help of intelligent mobile robots so as to maximize the number of saved people and/or minimize the search cost under restrictions on the amount of saved people within the allowable response time. We consider a special situation when the autonomous mobile robots (AMR), e.g., unmanned aerial vehicles and remote-controlled robo-ships have no operator on board as they are guided and completely controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robots during the search-and rescue missions after an accident at sea. We presume that in the unknown environments, the AMR’s search-and-rescue activity is subject to two types of error: (i) a 'false-negative' detection error where a target object is not discovered (‘overlooked') by the AMR’s sensors in spite that the AMR is in a close neighborhood of the latter and (ii) a 'false-positive' detection error, also known as ‘a false alarm’, in which a clean place or area is wrongly classified by the AMR’s sensors as a correct target. As the general resource-constrained discrete search problem is NP-hard, we restrict our study to finding local-optimal strategies. A specificity of the considered operational research problem in comparison with the traditional Kadane-De Groot-Stone search models is that in our model the probability of the successful search outcome depends not only on cost/time/probability parameters assigned to each individual location but, as well, on parameters characterizing the entire history of (unsuccessful) search before selecting any next location. We provide a fast approximation algorithm for finding the AMR route adopting a greedy search strategy in which, in each step, the on-board computer computes a current search effectiveness value for each location in the zone and sequentially searches for a location with the highest search effectiveness value. Extensive experiments with random and real-life data provide strong evidence in favor of the suggested operations research model and corresponding algorithm.

Keywords: disaster management, intelligent robots, scheduling algorithm, search-and-rescue at sea

Procedia PDF Downloads 168

Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

Procedia PDF Downloads 116

Authors: Dong Wu, Michael Grenn

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Managing software projects effectively is crucial for meeting deadlines, ensuring quality, and managing resources well. Traditional methods often struggle with predicting project timelines accurately due to uncertain schedules and complex data. This study addresses these challenges by combining time prediction algorithms with Large Language Models (LLMs). It makes use of real-world software project data to construct and validate a model. The model takes detailed project progress data such as task completion dynamic, team Interaction and development metrics as its input and outputs predictions of project timelines. To evaluate the effectiveness of this model, a comprehensive methodology is employed, involving simulations and practical applications in a variety of real-world software project scenarios. This multifaceted evaluation strategy is designed to validate the model's significant role in enhancing forecast accuracy and elevating overall management efficiency, particularly in complex software project environments. The results indicate that the integration of time prediction algorithms with LLMs has the potential to optimize software project progress management. These quantitative results suggest the effectiveness of the method in practical applications. In conclusion, this study demonstrates that integrating time prediction algorithms with LLMs can significantly improve the predictive accuracy and efficiency of software project management. This offers an advanced project management tool for the industry, with the potential to improve operational efficiency, optimize resource allocation, and ensure timely project completion.

Keywords: software project management, time prediction algorithms, large language models (LLMS), forecast accuracy, project progress prediction

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Authors: Zhongjie Yu, Hancheng Yu

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In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles.

Keywords: concentric circle detection, gradient, contour, edge pre-classification, RANSAC

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Authors: Dalwinder Singh, Amandeep Verma, Manpreet Kaur, Birmohan Singh

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The detection of pre-cancerous changes using a Pap smear test of cervical cell is the important step for the early diagnosis of cervical cancer. The Pap smear test consists of a sample of human cells taken from the cervix which are analysed to detect cancerous and pre-cancerous stage of the given subject. The manual analysis of these cells is labor intensive and time consuming process which relies on expert cytotechnologist. In this paper, a computer assisted system for the automated analysis of the cervical cells has been proposed. We propose a morphology based approach to the nucleus detection and segmentation of the cytoplasmic region of the given single or multiple overlapped cell. Further, various texture and region based features are calculated from these cells to classify these into normal and abnormal cell. Experimental results on public available dataset show that our system has achieved satisfactory success rate.

Keywords: cervical cancer, cervical tissue, mathematical morphology, texture features

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Authors: M. Koray Cetin, Mehmet Mert

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The purpose of the study is to examine impacts on tourism revenues of the exchange rate and country overall security level. There are numerous studies that examine the bidirectional relation between macroeconomic factors and tourism revenues and tourism demand. Most of the studies support the existence of impact of tourism revenues on growth rate but not vice versa. Few studies examine the impact of factors like real exchange rate or purchasing power parity on the tourism revenues. In this context, firstly impact of real exchange rate on tourism revenues examination is aimed. Because exchange rate is one of the main determinants of international tourism services price in guests currency unit. Another determinant of tourism demand for a country is country’s overall security level. This issue can be handled in the context of the relationship between tourism revenues and overall security including turmoil, terrorism, border problem, political violence. In this study, factors are handled for several countries which have tourism revenues on a certain level. With this structure, it is a panel data, and it is evaluated with panel data analysis techniques. Panel data have at least two dimensions, and one of them is time dimensions. The panel data analysis techniques are applied to data gathered from Worldbank data web page. In this study, it is expected to find impacts of real exchange rate and security factors on tourism revenues for the countries that have noteworthy tourism revenues.

Keywords: exchange rate, panel data analysis, security, tourism revenues

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Nádia Pereira, Paula Ferreira, Sofia Francisco, Sofia Oliveira, Sidclay Souza, Paula Paulino, Ana Margarida Veiga Simão

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Social media has progressed into a platform for hate speech among its users, and thus, there is an increasing need to develop automatic detection classifiers of offense and conflicts to help decrease the prevalence of such incidents. Online communication can be used to intentionally harm someone, which is why such classifiers could be essential in social networks. A possible application of these classifiers is the automatic detection of cyberbullying. Even though identifying the aggressive language used in online interactions could be important to build cyberbullying datasets, there are other criteria that must be considered. Being able to capture the language, which is indicative of the intent to harm others in a specific context of online interaction is fundamental. Offense and hate speech may be the foundation of online conflicts, which have become commonly used in social media and are an emergent research focus in machine learning and natural language processing. This study presents two Portuguese language offense-related datasets which serve as examples for future research and extend the study of the topic. The first is similar to other offense detection related datasets and is entitled Aggressiveness dataset. The second is a novelty because of the use of the history of the interaction between users and is entitled the Conflicts/Attacks dataset. Both datasets were developed in different phases. Firstly, we performed a content analysis of verbal aggression witnessed by adolescents in situations of cyberbullying. Secondly, we computed frequency analyses from the previous phase to gather lexical and linguistic cues used to identify potentially aggressive conflicts and attacks which were posted on Twitter. Thirdly, thorough annotation of real tweets was performed byindependent postgraduate educational psychologists with experience in cyberbullying research. Lastly, we benchmarked these datasets with other machine learning classifiers.

Keywords: aggression, classifiers, cyberbullying, datasets, hate speech, machine learning

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Authors: Federico Hahn Schlam, Fermín Martínez Solís

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Leaf-cutting ants (Atta cephalotes) forage from mango tree leaves and flowers to feed their colony. Farmers find it difficult to control ants due to the great quantity of trees grown in commercial orchards. In this article, IoT can support farmers for ant detection in real time, as production losses can be considered of 324 US per tree.A wireless sensor network, WSN, was developed to warn the farmer from ant presence in trees during a night. Mango trees were gathered into groups of 9 trees, where the central tree holds the master microcontroller, and the other eight trees presented slave microcontrollers (nodes). At each node, anemitter diode-photodiode unitdetects ants climbing up. A capacitor is chargedand discharged after being sampled every ten minutes. The system usesBLE (Bluetooth Low Energy) to communicate between the master microcontroller by BLE.When ants were detected the number of the tree was transmitted via LoRa from the masterto the producer smartphone to warn him. In this paper, BLE, LoRa, and energy consumption were studied under variable vegetation in the orchard. During 2018, 19 trees were attacked by ants, and ants fed 26.3% of flowers and 73.7% of leaves.

Keywords: BLE, atta cephalotes, LoRa, WSN-smartphone, energy consumption

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

Abstract:

Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Prajakta Musale, Bhargav Parlikar, Sakshi Parkhi, Anshu Parihar, Aryan Parikh, Diksha Parasharam, Parth Jadhav

Abstract:

AR technology is basically a development of VR technology that harnesses the power of computers to be able to read the surroundings and create projections of digital models in the real world for the purpose of visualization, demonstration, and education. It has been applied to education, fields of prototyping in product design, development of medical models, battle strategy in the military and many other fields. Our Engineering Design and Innovation (EDAI) project focuses on the usage of augmented reality, visual mapping, and 3d-visualization along with animation and text boxes to help students in fields of education get a rough idea of the concepts such as flow and mechanical movements that may be hard to visualize at first glance.

Keywords: spatial mapping, ARKit, depth sensing, real-time rendering

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Authors: nafees mahbub, blake tindol, utkarsh shrivastava, kuanchin chen

Abstract:

Online shopping has become an integral part of businesses today. Big players such as Amazon are setting the bar for delivery services, and many businesses are working towards meeting them. However, what happens if a seller underestimates or overestimates the delivery time? Does it translate to consumer comments, ratings, or lost sales? Although several prior studies have investigated the impact of poor logistics on customer satisfaction, that impact of under estimation of delivery times has been rarely considered. The study uses real-time customer online purchase data to study the impact of missed delivery times on satisfaction.

Keywords: LOST SALES, DELIVERY TIME, CUSTOMER SATISFACTION, CUSTOMER REVIEWS

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Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

Abstract:

The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

Procedia PDF Downloads 164

Authors: Navpreet Kaur, Himkusha Thakur, Nirmal Prabhakar

Abstract:

The most controversial issue in crop production is the use of Organophosphate insecticides. This is evident in many reports that Organophosphate (OP) insecticides, among the broad range of pesticides are mainly involved in acute and chronic poisoning cases. OPs detection is of crucial importance for health protection, food and environmental safety. In our study, a nanocomposite of poly (3,4 ethylenedioxythiophene) (PEDOT) and multi-walled carbon nanotubes (MWCNTs) has been deposited electrochemically onto the surface of fluorine doped tin oxide sheets (FTO) for the analysis of malathion OP. The -COOH functionalization of MWCNTs has been done for the covalent binding with amino groups of AChE enzyme. The use of PEDOT-MWCNT films exhibited an excellent conductivity, enables fast transfer kinetics and provided a favourable biocompatible microenvironment for AChE, for the significant malathion OP detection. The prepared biosensors were characterized by Fourier transform infrared spectrometry (FTIR), Field emission-scanning electron microscopy (FE-SEM) and electrochemical studies. Various optimization studies were done for different parameters including pH (7.5), AChE concentration (50 mU), substrate concentration (0.3 mM) and inhibition time (10 min). Substrate kinetics has been performed and studied for the determination of Michaelis Menten constant. The detection limit for malathion OP was calculated to be 1 fM within the linear range 1 fM to 1 µM. The activity of inhibited AChE enzyme was restored to 98% of its original value by 2-pyridine aldoxime methiodide (2-PAM) (5 mM) treatment for 11 min. The oxime 2-PAM is able to remove malathion from the active site of AChE by means of trans-esterification reaction. The storage stability and reusability of the prepared biosensor is observed to be 30 days and seven times, respectively. The application of the developed biosensor has also been evaluated for spiked lettuce sample. Recoveries of malathion from the spiked lettuce sample ranged between 96-98%. The low detection limit obtained by the developed biosensor made them reliable, sensitive and a low cost process.

Keywords: PEDOT-MWCNT, malathion, organophosphates, acetylcholinesterase, biosensor, oxime (2-PAM)

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

Abstract:

In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

Procedia PDF Downloads 234