Search results for: early detection test

Authors: Gregory Sullivan

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The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

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Authors: Olesya Bolkhovskaya, Alexander Maltsev

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Antenna arrays are widely used in modern radio systems in sonar and communications. The solving of the detection problems of a useful signal on the background of noise is based on the GLRT method. There is a large number of problem which depends on the known a priori information. In this work, in contrast to the majority of already solved problems, it is used only difference spatial properties of the signal and noise for detection. We are analyzing the influence of the degree of non-coherence of signal and noise unhomogeneity on the performance characteristics of different GLRT statistics. The description of the signal and noise is carried out by means of the spatial covariance matrices C in the cases of different number of known information. The partially coherent signal is simulated as a plane wave with a random angle of incidence of the wave concerning a normal. Background noise is simulated as random process with uniform distribution function in each element. The results of investigation of degradation of performance characteristics for different cases are represented in this work.

Keywords: GLRT, Neumann-Pearson’s criterion, Test-statistics, degradation, spatial processing, multielement antenna array

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Authors: Goutam Kumar Ghorai, Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, G. Sarkar, Ashis K. Dhara

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Diabetes mellitus is one of the most common chronic diseases in all countries and continues to increase in numbers significantly. Diabetic retinopathy (DR) is damage to the retina that occurs with long-term diabetes. DR is a major cause of blindness in the Indian population. Therefore, its early diagnosis is of utmost importance towards preventing progression towards imminent irreversible loss of vision, particularly in the huge population across rural India. The barriers to eye examination of all diabetic patients are socioeconomic factors, lack of referrals, poor access to the healthcare system, lack of knowledge, insufficient number of ophthalmologists, and lack of networking between physicians, diabetologists and ophthalmologists. A few diabetic patients often visit a healthcare facility for their general checkup, but their eye condition remains largely undetected until the patient is symptomatic. This work aims to focus on the design and development of a fully automated intelligent decision system for screening retinal fundus images towards detection of the pathophysiology caused by microaneurysm in the early stage of the diseases. Automated detection of microaneurysm is a challenging problem due to the variation in color and the variation introduced by the field of view, inhomogeneous illumination, and pathological abnormalities. We have developed aconvolutional neural network for efficient detection of microaneurysm. A loss function is also developed to handle severe class imbalance due to very small size of microaneurysms compared to background. The network is able to locate the salient region containing microaneurysms in case of noisy images captured by non-mydriatic cameras. The ground truth of microaneurysms is created by expert ophthalmologists for MESSIDOR database as well as private database, collected from Indian patients. The network is trained from scratch using the fundus images of MESSIDOR database. The proposed method is evaluated on DIARETDB1 and the private database. The method is successful in detection of microaneurysms for dilated and non-dilated types of fundus images acquired from different medical centres. The proposed algorithm could be used for development of AI based affordable and accessible system, to provide service at grass root-level primary healthcare units spread across the country to cater to the need of the rural people unaware of the severe impact of DR.

Keywords: retinal fundus image, deep convolutional neural network, early detection of microaneurysms, screening of diabetic retinopathy

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Authors: Leydi Noemi Peraza Gómez, Jose Álvarez Nemegyei, Damaris Francis Estrella Castillo

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In order to have an epidemiological tool to detect biomechanical stress (ERGO-Mex), which impose physical labor or recreational activities, a questionnaire is constructed in Spanish, validated and culturally adapted to the Mayan indigenous population of Yucatan. Through the seven steps proposed by Guillemin and Beaton the procedure was: initial translation, synthesis of the translations, feed back of the translation. After that review by a committee of experts, pre-test of the preliminary version, and presentation of the results to the committee of experts and members of the community. Finally the evaluation of its internal validity (Cronbach's α coefficient) and external (intraclass correlation coefficient). The results for the validation in Spanish indicated that 45% of the participants have biomechanical stress. The ERGO-Mex correlation was 0.69 (p <0.0001). Subjects with high biomechanical stress had a higher score than subjects with low biomechanical stress (17.4 ± 8.9 vs.9.8 ± 2.8, p = 0.003). The Cronbach's α coefficient was 0.92; and for validation in Cronbach's α maya it was 0.82 and CCI = 0.70 (95% CI: 0.58-0.79; p˂0.0001); ERGO-Mex is suitable for performing early detection of musculoskeletal diseases and helping to prevent disability.

Keywords: biomechanical stress, disability, musculoskeletal disorders, prevention

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Authors: Bo Liu, Chi-Man Pun

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A novel technique for digital forgery detection by signal noise inconsistency is proposed in this paper. The forged area spliced from the other picture contains some features which may be inconsistent with the rest part of the image. Noise pattern and the level is a possible factor to reveal such inconsistency. To detect such noise discrepancies, the test picture is initially segmented into small pieces. The noise pattern and level of each segment are then estimated by using various filters. The noise features constructed in this step are utilized in energy-based graph cut to expose forged area in the final step. Experimental results show that our method provides a good illustration of regions with noise inconsistency in various scenarios.

Keywords: forgery detection, splicing forgery, noise estimation, noise

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Authors: Oktia Woro Kasmini Handayani, Sri Ratna Rahayu, Efa Nugroho, Bertakalswa Hermawati

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In Indonesia, from 2007 until 2013, the prevalence of overnutrition in children under five years and school age tends to increase. Clean and Health Life Behavior of school children supporting nutrition status still below the determined target. On the other side, school institution is an ideal place to educate and form health behavior, that should be initiated as early as possible (Early Age Education/PAUD level). The objective of this research was to find out the effectivity of education model through partnership with school institution in urban region, city of Semarang, Central Java Province, Indonesia. The research used quantitative approach supported with qualitative data. The population consist of all mother having school children of ages 3-5 years within the research region; sampling technique was purposive sampling, as many as 237 mothers. Research instrument was Clean and Health Life Behavior evaluation questionaire, and video as education media. The research used experimental design. Data analysis used effectivity criteria from Sugiyono and 2 paired sampel t test. Education model optimalization in the effort to improve nutrition status indicates t test result with signification < 0.05 (there was significant effect before and after model intervention), with effectivity test result of 79% (effective), but still below expected target which is 80%. Education model need to be utilized and optimallized the implementation so that expected target reached.

Keywords: nutrition status, early age education, clean dan health life behavior, education model

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Hui Fang Huang Su, Jia Borror

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This study compared two interventions for teaching math to preschool-aged students with autism spectrum disorder (ASD). The first is considered the business as usual (BAU) intervention, which uses the Strategies for Teaching Based on Autism Research (STAR) curriculum and discrete trial teaching as the instructional methodology. The second is the Math is Not Difficult (Project MIND) activity-embedded, naturalistic intervention. These interventions were randomly assigned to four preschool students with ASD classrooms and implemented over three months for Project Mind. We used measurement gained during the same three months for the STAR intervention. In addition, we used A quasi-experimental, pre-test/post-test design to compare the effectiveness of these two interventions in building mathematical knowledge and skills. The pre-post measures include three standardized instruments: the Test of Early Math Ability-3, the Problem Solving and Calculation subtests of the Woodcock-Johnson Test of Achievement IV, and the Bracken Test of Basic Concepts-3 Receptive. The STAR curriculum-based assessment is administered to all Baudhuin students three times per year, and we used the results in this study. We anticipated that implementing these two approaches would improve the mathematical knowledge and skills of children with ASD. Still, it is crucial to see whether a behavioral or naturalistic teaching approach leads to more significant results.

Keywords: early learning, autism, math for pre-schoolers, special education, teaching strategies

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Authors: Ji-Hoon Hong, Dong-Hee Kim, Nam-Uk Kim, Tai-Myoung Chung

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Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.

Keywords: android malware detection, software-defined network, interaction environment, android malware detection, software-defined network, interaction environment

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Authors: Medjram Sofiane, Babahenini Mohamed Chaouki, Mohamed Benali Yamina

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Skin detection is an important task for computer vision systems. A good method for skin detection means a good and successful result of the system. The colour is a good descriptor that allows us to detect skin colour in the images, but because of lightings effects and objects that have a similar colour skin, skin detection becomes difficult. In this paper, we proposed a method using the YCbCr colour space for skin detection and lighting effects elimination, then we use the information of texture to eliminate the false regions detected by the YCbCr colour skin model.

Keywords: skin detection, YCbCr, GLCM, texture, human skin

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

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The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Recep Kesli, Huseyin Bilgin, Yasar Unlu, Gokhan Gungor

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Aim: The aim of this study was to compare diagnostic values of two different molecular-based tests (GenoType® HelicoDR ve Seeplex® H. pylori-ClaR- ACE Detection) in detection presence of the H. pylori from gastric biopsy specimens. In addition to this also was aimed to determine resistance ratios of H. pylori strains against to clarytromycine and quinolone isolated from gastric biopsy material cultures by using both the genotypic (GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection) and phenotypic (gradient strip, E-test) methods. Material and methods: A total of 266 patients who admitted to Konya Education and Research Hospital Department of Gastroenterology with dyspeptic complaints, between January 2011-June 2013, were included in the study. Microbiological and histopathological examinations of biopsy specimens taken from antrum and corpus regions were performed. The presence of H. pylori in all the biopsy samples was investigated by five differnt dignostic methods together: culture (C) (Portagerm pylori-PORT PYL, Pylori agar-PYL, GENbox microaer, bioMerieux, France), histology (H) (Giemsa, Hematoxylin and Eosin staining), rapid urease test (RUT) (CLOtest, Cimberly-Clark, USA), and two different molecular tests; GenoType® HelicoDR, Hain, Germany, based on DNA strip assay, and Seeplex ® H. pylori -ClaR- ACE Detection, Seegene, South Korea, based on multiplex PCR. Antimicrobial resistance of H. pylori isolates against clarithromycin and levofloxacin was determined by GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, and gradient strip (E-test, bioMerieux, France) methods. Culture positivity alone or positivities of both histology and RUT together was accepted as the gold standard for H. pylori positivity. Sensitivity and specificity rates of two molecular methods used in the study were calculated by taking the two gold standards previously mentioned. Results: A total of 266 patients between 16-83 years old who 144 (54.1 %) were female, 122 (45.9 %) were male were included in the study. 144 patients were found as culture positive, and 157 were H and RUT were positive together. 179 patients were found as positive with GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection together. Sensitivity and specificity rates of studied five different methods were found as follows: C were 80.9 % and 84.4 %, H + RUT were 88.2 % and 75.4 %, GenoType® HelicoDR were 100 % and 71.3 %, and Seeplex ® H. pylori -ClaR- ACE Detection were, 100 % and 71.3 %. A strong correlation was found between C and H+RUT, C and GenoType® HelicoDR, and C and Seeplex ® H. pylori -ClaR- ACE Detection (r:0.644 and p:0.000, r:0.757 and p:0.000, r:0.757 and p:0.000, respectively). Of all the isolated 144 H. pylori strains 24 (16.6 %) were detected as resistant to claritromycine, and 18 (12.5 %) were levofloxacin. Genotypic claritromycine resistance was detected only in 15 cases with GenoType® HelicoDR, and 6 cases with Seeplex ® H. pylori -ClaR- ACE Detection. Conclusion: In our study, it was concluded that; GenoType® HelicoDR and Seeplex ® H. pylori -ClaR- ACE Detection was found as the most sensitive diagnostic methods when comparing all the investigated other ones (C, H, and RUT).

Keywords: Helicobacter pylori, GenoType® HelicoDR, Seeplex ® H. pylori -ClaR- ACE Detection, antimicrobial resistance

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Authors: Retno Supriyanti, Best Leader Nababan, Yogi Ramadhani, Wahyu Siswandari

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Blood cell morphology is an important parameter in a hematology test. Currently, in developing countries, a lot of hematology is done manually, either by physicians or laboratory staff. According to the limitation of the human eye, examination based on manual method will result in a lower precision and accuracy. In addition, the hematology test by manual will further complicate the diagnosis in some areas that do not have competent medical personnel. This research aims to develop a simple tool in the detection of blood cell morphology-based computer. In this paper, we focus on the detection of the outer contour of leukocytes. The results show that the system that we developed is promising for detecting blood cell morphology automatically. It is expected, by implementing this method, the problem of accuracy, precision and limitations of the medical staff can be solved.

Keywords: morphology operation, developing countries, hematology test, limitation of medical personnel

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Authors: Abdulla Al Hmoudi

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The impacts and costs of natural disasters on people, properties and the environment is often severe when they occur on a large scale or when not prepared for. Factors such as impacts of climate change, urban growth, poor planning to mention a few, have continued to significantly increase the frequencies and aggravate the impacts of natural hazards across the world; the United Arab Emirates (UAE) inclusive. The lack of deployment of an early warning system, low risk and hazard knowledge and impact of natural hazard experienced in some communities in the UAE have emphasised the need for more effective early warning systems. This paper focuses on the collaborative approach taken to instituting and implementing an early warning system. Using mixed methods 888 people completed the questionnaire and eight people were interviewed in Abu Dhabi. The results indicate that the collaborative approach to early warning system is UAE is needed, but lacks essential principles of the early warning system and currently underutilised. It is recommended that the collaborative early warning system is applied at every stage of the early warning system with the specific responsibility of each stakeholder and actor.

Keywords: community, early warning system, emergency management, UAE

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Authors: Kuniaki Kudo, Raviraj Solanki, Kaushal Patel, Yash Virani

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This research mainly concerns optimizing UI test automation for large-scale web applications. The test target application is the HHAexchange homecare management WEB application that seamlessly connects providers, state Medicaid programs, managed care organizations (MCOs), and caregivers through one platform with large-scale functionalities. This study focuses on user interface automation testing for the WEB application. The quality assurance team must execute many manual users interface test cases in the development process to confirm no regression bugs. The team automated 346 test cases; the UI automation test execution time was over 17 hours. The business requirement was reducing the execution time to release high-quality products quickly, and the quality assurance automation team modernized the test automation framework to optimize the execution time. The base of the WEB UI automation test environment is Selenium, and the test code is written in Python. Adopting a compilation language to write test code leads to an inefficient flow when introducing scalability into a traditional test automation environment. In order to efficiently introduce scalability into Test Automation, a scripting language was adopted. The scalability implementation is mainly implemented with AWS's serverless technology, an elastic container service. The definition of scalability here is the ability to automatically set up computers to test automation and increase or decrease the number of computers running those tests. This means the scalable mechanism can help test cases run parallelly. Then test execution time is dramatically decreased. Also, introducing scalable test automation is for more than just reducing test execution time. There is a possibility that some challenging bugs are detected by introducing scalable test automation, such as race conditions, Etc. since test cases can be executed at same timing. If API and Unit tests are implemented, the test strategies can be adopted more efficiently for this scalability testing. However, in WEB applications, as a practical matter, API and Unit testing cannot cover 100% functional testing since they do not reach front-end codes. This study applied a scalable UI automation testing strategy to the large-scale homecare management system. It confirmed the optimization of the test case execution time and the detection of a challenging bug. This study first describes the detailed architecture of the scalable test automation environment, then describes the actual performance reduction time and an example of challenging issue detection.

Keywords: aws, elastic container service, scalability, serverless, ui automation test

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Authors: Zhang Xiaodong, Tang Zixin, Liu Xin

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In order to avoid self-collision of space manipulators during operation process, a real-time detection method is proposed in this paper. The manipulator is fitted into a cylinder enveloping surface, and then the detection algorithm of collision between cylinders is analyzed. The collision model of space manipulator self-links can be detected by using this algorithm in real-time detection during the operation process. To ensure security of the operation, a safety threshold is designed. The simulation and experiment results verify the effectiveness of the proposed algorithm for a 7-DOF space manipulator.

Keywords: space manipulator, collision detection, self-collision, the real-time collision detection

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Authors: Norzalina Othman, Nurul Na’imy Wan, Azliza Mohd Rusli, Wan Noor Syahirah Meor Idris

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Iris detection is a process where the position of the eyes is extracted from the face images. It is a current method used for many applications such as for security purpose and drowsiness detection. This paper proposes the use of eyes detection in controlling side mirror of motor vehicles. The eyes detection method aims to make driver easy to adjust the side mirrors automatically. The system will determine the midpoint coordinate of eyes detection on RGB (color) image and the input signal from y-coordinate will send it to controller in order to rotate the angle of side mirror on vehicle. The eye position was cropped and the coordinate of midpoint was successfully detected from the circle of iris detection using Viola Jones detection and circular Hough transform methods on RGB image. The coordinate of midpoint from the experiment are tested using controller to determine the angle of rotation on the side mirrors.

Keywords: iris detection, midpoint coordinates, RGB images, side mirror

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Authors: Kharouf Malika, Chkeir Aly, Khac Tuan Huynh

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Detecting change points in time series data is a challenging problem, especially in scenario where there is limited prior knowledge regarding the data’s distribution and the nature of the transitions. We present a method designed for detecting changes in the covariance structure of high-dimensional time series data, where the number of variables closely matches the data length. Our objective is to achieve unbiased test statistic estimation under the null hypothesis. We delve into the utilization of Random Matrix Theory to analyze the behavior of our test statistic within a high-dimensional context. Specifically, we illustrate that our test statistic converges pointwise to a normal distribution under the null hypothesis. To assess the effectiveness of our proposed approach, we conduct evaluations on a simulated dataset. Furthermore, we employ our method to examine changes aimed at detecting frailty in the elderly.

Keywords: change point detection, hypothesis tests, random matrix theory, frailty in elderly

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: A. Zurraini, Syed Alwi Sar, H. Helmy, H. Nazeefah

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Early Dementia Questionnaire (EDQ) was developed as a screening tool to detect patients with early dementia in primary care. It was developed based on 20 symptoms of dementia. From a preliminary study, EDQ had been shown to be a promising alternative for screening of early dementia. This study was done to further test on EDQ’s reliability and validity. Using a systematic random sampling, 200 elderly patients attending primary health care centers in Kuching, Sarawak had consented to participate in the study and were administered the EDQ. Geriatric Depression Scale (GDS) was used to exclude patients with depression. Those who scored >21 MMSE, were retested using the EDQ. Reliability was determined by Cronbach’s alpha for internal consistency and construct validity was assessed using confirmatory factor analysis (principle component with varimax rotation). The result showed that the overall Cronbach’s alpha coefficient was good which was 0.874. Confirmatory factor analysis on 4 factors indicated that the Cronbach’s alpha for each domain were acceptable with memory (0.741), concentration (0.764), emotional and physical symptoms (0.754) and lastly sleep and environment (0.720). Pearson correlation coefficient between the first EDQ score and the retest EDQ score among those with MMSE of >21 showed a very strong, positive correlation between the two variables, r = 0.992, N=160, P <0.001. The results of the validation study showed that Early Dementia Questionnaire (EDQ) is a valid and reliable tool to be used as a screening tool to detect early dementia in primary care.

Keywords: Early Dementia Questionnaire (EDQ), screening, primary care, construct validity

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Leping Chen, Daoxiang An, Xiaotao Huang

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Automatic vehicle detection using synthetic aperture radar (SAR) image has been widely researched, as well as using optical remote sensing images. However, most researches treat the detection as an independent problem, failing to make full use of SAR data information. In circular SAR (CSAR), the two long borders of vehicle will shrink if the imaging surface is set higher than the reference one. Based on above variance, an automatic vehicle detection using CSAR image is proposed to enhance detection ability under complex environment, such as vehicles’ closely packing, which confuses the detector. The detection method uses the multiple images generated by different height plane to obtain an energy-concentrated image for detecting and then uses the maximally stable extremal regions method (MSER) to detect vehicles. A result of vehicles’ detection is given to verify the effectiveness and correctness of proposed method.

Keywords: circular SAR, vehicle detection, automatic, imaging

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Authors: Bouchemha Amel, Chachoui Takieddine, H. Maalem

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Automatic detection of targets in a modern communication system RADAR is based primarily on the concept of adaptive CFAR detector. To have an effective detection, we must minimize the influence of disturbances due to the clutter. The detection algorithm adapts the CFAR detection threshold which is proportional to the average power of the clutter, maintaining a constant probability of false alarm. In this article, we analyze the performance of two variants of adaptive algorithms CA-CFAR and OS-CFAR and we compare the thresholds of these detectors in the marine environment (no-Gaussian) with a Weibull distribution.

Keywords: CFAR, threshold, clutter, distribution, Weibull, detection

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Authors: Rashid Mahmood, Muhammad Junaid Sarwar

Abstract:

Mobile ad hoc networks (MANETs) use has been well-known from the last few years in the many applications, like mission critical applications. In the (MANETS) prevention method is not adequate as the security concerned, so the detection method should be added to the security issues in (MANETs). The authentication and encryption is considered the first solution of the MANETs problem where as now these are not sufficient as MANET use is increasing. In this paper we are going to present the concept of intrusion detection and then survey some of major intrusion detection techniques in MANET and aim to comparing in some important fields.

Keywords: MANET, IDS, intrusions, signature, detection, prevention

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

Abstract:

Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Dinko Vuleta, Dejan Madić, Goran Sporiš, Nebojša Trajković

Abstract:

The aim of this study was to determine the effects of gymnastics program in school on skill-related fitness in early adolescent students. The study involved 58 adolescent students (12.82±0.54 years; Height 156.81±8.16 cm; 53.46±12.31 kg) from primary school divided into two groups, following the randomization. The gymnastic group was involved in a 12 week of gymnastics classes, while the control group only participated in usual PE classes which consisted of multi-sport activities. The variables were selected within the several fitness batteries, measuring coordination (polygon backwards), upper and lower body strength standing long jump and medicine ball throw), speed (20 m sprint) and agility (4x10 test). Pre-test to post-test values showed significant improvements in all tested variables (p<0.05), except for the 4x10m test, where there were no significant improvements in neither of the groups (p>0.05). Significant interactions of time by group were observed for coordination, sprint speed, standing long jump and medicine ball throw (p<0.05). The results showed significant increase in skill-related fitness of the participants in the gymnastic group compared to the control group. Therefore, participation in gymnastics must be recommended as a positive foundational activity for school-aged children, from early childhood to adulthood. Additionally, the results can provide useful information in optimizing the training loads of pupils involved in gymnastic training throughout PE classes.

Keywords: effects, PE classes, physical fitness, training

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Authors: B. T. Naveen Kumar, Anuj Tyagi, Niraj Kumar Singh, Visanu Boonyawiwat, A. H. Shanthanagouda, Orawan Boodde, K. M. Shankar, Prakash Patil, Shubhkaramjeet Kaur

Abstract:

Early mortality syndrome (EMS)/Acute Hepatopancreatic Necrosis Disease (AHPND) has emerged as a major obstacle for the shrimp farming around the world. It is caused by a strain of Vibrio parahaemolyticus. The possible preventive and control measure is, early and rapid detection of the pathogen in the broodstock, post-larvae and monitoring the shrimp during the culture period. Polymerase chain reaction (PCR) based early detection methods are good, but they are costly, time taking and requires a sophisticated laboratory. The present study was conducted to develop a simple, sensitive and rapid diagnostic farmer level kit for the reliable detection of AHPND in shrimp. A panel of monoclonal antibodies (MAbs) were raised against the recombinant Pir B protein (rPirB). First, an immunodot was developed by using MAbs G3B8 and Mab G3H2 which showed specific reactivity to purified r-PirB protein with no cross-reactivity to other shrimp bacterial pathogens (AHPND free Vibrio parahaemolyticus (Indian strains), V. anguillarum, WSSV, Aeromonas hydrophila, and Aphanomyces invadans). Immunodot developed using Mab G3B8 is more sensitive than that with the Mab G3H2. However, immunodot takes almost 2.5 hours to complete with several hands-on steps. Therefore, the flow-through assay (FTA) was developed by using a plastic cassette containing the nitrocellulose membrane with absorbing pads below. The sample was dotted in the test zone on the nitrocellulose membrane followed by continuos addition of five solutions in the order of i) blocking buffer (BSA) ii) primary antibody (MAb) iii) washing Solution iv) secondary antibody and v) chromogen substrate (TMB) clear purple dots against a white background were considered as positive reactions. The FTA developed using MAbG3B8 is more sensitive than that with MAb G3H2. In FTA the two MAbs showed specific reactivity to purified r-PirB protein and not to other shrimp bacterial pathogens. The FTA is simple to farmer/field level, sensitive and rapid requiring only 8-10 min for completion. Tests can be developed to kits, which will be ideal for use in biosecurity, for the first line of screening (at the port or pond site) and during monitoring and surveillance programmes overall for the good management practices to reduce the risk of the disease.

Keywords: acute hepatopancreatic necrosis disease, AHPND, flow-through assay, FTA, farmer level, immunodot, pond site, shrimp

Procedia PDF Downloads 157