Search results for: Intrusion Detection System (IDS)

Authors: Po-Chun Wang, Yan-Ru Lai, Sophia I. C. Lin, Alvin W. Y. Su

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The Generative Theory of Tonal Music (GTTM) provides systematic approaches to recognizing local boundaries of music. The rules have been implemented in some automated melody segmentation algorithms. Besides, there are also deep learning methods with GTTM features applied to boundary detection tasks. However, these studies might face constraints such as a lack of or inconsistent label data. The GTTM database is currently the most widely used GTTM database, which includes manually labeled GTTM rules and local boundaries. Even so, we found some problems with these labels. They are sometimes discrepancies with GTTM rules. In addition, since it is labeled at different times by multiple musicians, they are not within the same scope in some cases. Therefore, in this paper, we examine this database with musicians from the aspect of classical music and relabel the scores. The relabeled database - GTTM Database v2.0 - will be released for academic research usage. Despite the experimental and statistical results showing that the relabeled database is more consistent, the improvement in boundary detection is not substantial. It seems that we need more clues than GTTM rules for boundary detection in the future.

Keywords: dataset, GTTM, local boundary, neural network

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Authors: Sofia G. Meirinho, Luis G. Dias, António M. Peres, Lígia R. Rodrigues

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The emerging development of electrochemical aptasen sors has enabled the easy and fast detection of protein biomarkers in standard and real samples. Biomarkers are produced by body organs or tumours and provide a measure of antigens on cell surfaces. When detected in high amounts in blood, they can be suggestive of tumour activity. These biomarkers are more often used to evaluate treatment effects or to assess the potential for metastatic disease in patients with established disease. Osteopontin (OPN) is a protein found in all body fluids and constitutes a possible biomarker because its overexpression has been related with breast cancer evolution and metastasis. Currently, biomarkers are commonly used for the development of diagnostic methods, allowing the detection of the disease in its initial stages. A previously described RNA aptamer was used in the current work to develop a simple and sensitive electrochemical aptasensor with high affinity for human OPN. The RNA aptamer was biotinylated and immobilized on a gold electrode by avidin-biotin interaction. The electrochemical signal generated from the aptamer–target molecule interaction was monitored electrochemically using cyclic voltammetry in the presence of [Fe (CN) 6]−3/− as a redox probe. The signal observed showed a current decrease due to the binding of OPN. The preliminary results showed that this aptasensor enables the detection of OPN in standard solutions, showing good selectivity towards the target in the presence of others interfering proteins such as bovine OPN and bovine serum albumin. The results gathered in the current work suggest that the proposed electrochemical aptasensor is a simple and sensitive detection tool for human OPN and so, may have future applications in cancer disease monitoring.

Keywords: osteopontin, aptamer, aptasensor, screen-printed electrode, cyclic voltammetry

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Authors: Djamil Guettiche, Ahmed Mekki, Tighilt Fatma-Zohra, Rachid Mahmoud

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The aim of this study was to synthesize and characterize conducting polymer composites of polypyrrole and graphene, including pristine and surface-treated graphene (PPy/GO, PPy/rGO, and PPy/rGO-ArCOOH), for use as sensitive elements in a homemade chemiresistive module for on-line detection of nitrogen oxides vapors. The chemiresistive module was prepared, characterized, and evaluated for performance. Structural and morphological characterizations of the composite were carried out using FTIR, Raman spectroscopy, and XRD analyses. After exposure to NO and NO₂ gases in both static and dynamic modes, the sensitivity, selectivity, limit of detection, and response time of the sensor were determined at ambient temperature. The resulting sensor showed high sensitivity, selectivity, and reversibility, with a low limit of detection of 1 ppm. A composite of polypyrrole and graphene functionalized with aryl 4-carboxy benzene diazonium salt was synthesized and characterized using FTIR, scanning electron microscopy, transmission electron microscopy, UV-visible, and X-ray diffraction. The PPy-rGOArCOOH composite exhibited a good electrical resistance response to NO₂ at room temperature and showed enhanced NO₂-sensing properties compared to PPy-rGO thin films. The selectivity and stability of the NO₂ sensor based on the PPy/rGO-ArCOOH nanocomposite were also investigated.

Keywords: conducting polymers, surface treated graphene, diazonium salt, polypyrrole, Nitrogen oxide sensing

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Naydelis Brito Suárez, Deni Librado Torres Román, Fernando Hermosillo Reynoso

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Automatic detection and generation of a dynamic ROI (Region of Interest) in vehicle traffic surveillance videos based on a static camera in Intelligent Transportation Systems is challenging for computer vision-based systems. The dynamic ROI, being a changing ROI, should capture any other moving object located outside of a static ROI. In this work, the video is represented by a Tensor model composed of a Background and a Foreground Tensor, which contains all moving vehicles or objects. The values of each pixel over a time interval are represented by time series, and some pixel rows were selected. This paper proposes a pixel entropy-based algorithm for automatic detection and generation of a dynamic ROI in traffic videos under the assumption of two types of theoretical pixel entropy behaviors: (1) a pixel located at the road shows a high entropy value due to disturbances in this zone by vehicle traffic, (2) a pixel located outside the road shows a relatively low entropy value. To study the statistical behavior of the selected pixels, detecting the entropy changes and consequently moving objects, Shannon, Tsallis, and Approximate entropies were employed. Although Tsallis entropy achieved very high results in real-time, Approximate entropy showed results slightly better but in greater time.

Keywords: convex hull, dynamic ROI detection, pixel entropy, time series, moving objects

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Authors: Wanwisa Deenin, Abdulhadee Yakoh, Chahya Kreangkaiwal, Orawon Chailapakul, Kanitha Patarakul, Sudkate Chaiyo

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LipL32 is an outer membrane protein present only on pathogenic Leptospira species, which are the causative agent of leptospirosis. Leptospirosis symptoms are often misdiagnosed with other febrile illnesses as the clinical manifestations are non-specific. Therefore, an accurate diagnostic tool for leptospirosis is indeed critical for proper and prompt treatment. Typical diagnosis via serological assays is generally performed to assess the antibodies produced against Leptospira. However, their delayed antibody response and complicated procedure are undoubtedly limited the practical utilization especially in primary care setting. Here, we demonstrate for the first time an early-stage detection of LipL32 by an integrated lateral-flow immunoassay with electrochemical readout (eLFIA). A ferrocene trace tag was monitored via differential pulse voltammetry operated on a smartphone-based device, thus allowing for on-field testing. Superior performance in terms of the lowest detectable limit of detection (LOD) of 8.53 pg/mL and broad linear dynamic range (5 orders of magnitude) among other sensors available thus far was established. Additionally, the developed test strip provided a straightforward yet sensitive approach for diagnosis of leptospirosis using the collected human sera from patients, in which the results were comparable to the real-time polymerase chain reaction technique.

Keywords: leptospirosis, electrochemical detection, lateral flow immunosensor, point-of-care testing, early-stage detection

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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We describe an innovative method for highly specific detection of miRNAs using a specially modified method of poly(A) adaptor RT-qPCR. We use uniquely designed specific extension sequence, which plays important role in providing an opportunity to affect high specificity of miRNA detection. This method involves two steps of reactions as like previously reported and which are poly(A) tailing and reverse-transcription followed by real-time PCR. Firstly, miRNAs are extended by a poly(A) tailing reaction and then converted into cDNA. Here, we remarkably reduced the reaction time by the application of short length of poly(T) adaptor. Next, cDNA is hybridized to the 3’-end of a specific extension sequence which contains miRNA sequence and results in producing a novel PCR template. Thereafter, the SYBR Green-based RT-qPCR progresses with a universal poly(T) adaptor forward primer and a universal reverse primer. The target miRNA, miR-106b in human brain total RNA, could be detected quantitatively in the range of seven orders of magnitude, which demonstrate that the assay displays a dynamic range of at least 7 logs. In addition, the better specificity of this novel extension-based assay against well known poly(A) tailing method for miRNA detection was confirmed by melt curve analysis of real-time PCR product, clear gel electrophoresis and sequence chromatogram images of amplified DNAs.

Keywords: microRNA(miRNA), specific extension sequence, RT-qPCR, poly(A) tailing assay, reverse transcription

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Dewan Ali Ahsan

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Hilsa (Tenualosa ilisha) is one of the most important anadromous fish species of the trans-boundary ecosystem of Bangladesh, India and Myanmar. Hilsa is not only an economically important species specially for Bangladesh and India, but also for the integral part of the culture of the Bangladesh and India. This flag-ship species in Bangladesh contributed alone of 10.82% of the total fish production of the country and about 75% of world’s total catch of hilsa comes from Bangladesh alone. As hilsa is an anadromous fish, it migrates from the Bay of Bengal to rivers for spawning, nursing and growing and for all of these purposes hilsa needs freshwaters. Ripe broods prefer turbid, fast flowing freshwater for spawning but young prefer clear and slow flowing freshwater. Climate change (salinity intrusion, sea level rise, temperature rise, impact of fresh water flow), unplanned developmental activities and other anthropogenic activities all together are severely damaging the hilsa stock and its habitats. So, climate change and human interferences are predicted to have a range of direct and indirect impacts on marine and freshwater hilsa fishery, with implications for fisheries-dependent economies, coastal communities and fisherfolk. The present study identified that salinity intrusion, siltation in river bed, decrease water flow from upstream, fragmentation of river in dry season, over exploitation, use of small mesh nets are the major reasons to affect the upstream migration of hilsa and its sustainable management. It has been also noticed that Bangladesh government has taken some actions for hilsa management. Government is trying to increase hilsa production not only by conserving jatka (juvenile hilsa) but also protecting the brood hilsa during the breeding seasons by imposing seasonal ban on fishing, restricted mesh size etc. Unfortunately, no such management plans are available for Indian and Myanmar territory. As hilsa is a highly migratory trans-boundary fish in the Bay of Bengal (and all of these countries share the same stock), it is essential to adopt a joint management policy (by Bangladesh-India-Myanmar) for the sustainable management for the hilsa stock.

Keywords: hilsa, climate change, south-east Asia, fishery management

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Authors: Jaemyoung Lee

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We present a method to set up system functions through a near filed communication (NFC) of a smartphone. The short communication distance of the NFC which is usually less than 4 cm could prevent any interferences from other devices and establish a secure communication channel between a system and the smartphone. The proposed set-up method for system function values is demonstrated for a blacbox system in a car. In demonstration, system functions of a blackbox which is manipulated through NFC of a smartphone are controls of image quality, sound level, shock sensing level to store images, etc. The proposed set-up method for system function values can be used for any devices with NFC.

Keywords: system set-up, near field communication, smartphone, android

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Authors: Goutham Kumar Dogiparti, D. R. Seshu

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In the present study, wavelet analysis was used for locating damage in simply supported and cantilever beams. Study was carried out varying different levels and locations of damage. In numerical method, ANSYS software was used for modal analysis of damaged and undamaged beams. The mode shapes obtained from numerical analysis is processed using MATLAB wavelet toolbox to locate damage. Effect of several parameters such as (damage level, location) on the natural frequencies and mode shapes were also studied. The results indicated the potential of wavelets in identifying the damage location.

Keywords: damage, detection, beams, wavelets

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Authors: Prasad Urvashi, Wynn Yezarni, Williams Shehan, Ravindran Arun

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Introduction: Primary health services are often the first point of contact that patients with mental illness have with the healthcare system. A number of tools have been developed to increase detection of depression in the context of primary care. However, one challenge amongst many includes utilizing these tools within the limited primary care consultation timeframe. Therefore, short questionnaires that screen for depression that are just as effective as more comprehensive diagnostic tools may be beneficial in improving detection rates of patients visiting a primary care setting. Objective: To develop and determine the sensitivity and specificity of a 2-Question Questionnaire (2-QQ) to screen for depression in in a suburban primary care clinic in Ragama, Sri Lanka. The purpose is to develop a short screening tool for depression that is culturally adapted in order to increase the detection of depression in the Sri Lankan patient population. Methods: This was a cross-sectional study involving two steps. Step one: verbal administration of 2-QQ to patients by their primary care physician. Step two: completion of the Peradeniya Depression Scale, a validated diagnostic tool for depression, the patient after their consultation with the primary care physician. The results from the PDS were then correlated to the results from the 2-QQ for each patient to determine sensitivity and specificity of the 2-QQ. Results: A score of 1/+ on the 2-QQ was most sensitive but least specific. Thus, setting the threshold at this level is effective for correctly identifying depressed patients, but also inaccurately captures patients who are not depressed. A score of 6 on the 2-QQ was most specific but least sensitive. Setting the threshold at this level is effective for correctly identifying patients without depression, but not very effective at capturing patients with depression. Discussion: In the context of primary care, it may be worthwhile setting the 2-QQ screen at a lower threshold for positivity (such as a score of 1 or above). This would generate a high test sensitivity and thus capture the majority of patients that have depression. On the other hand, by setting a low threshold for positivity, patients who do not have depression but score higher than 1 on the 2-QQ will also be falsely identified as testing positive for depression. However, the benefits of identifying patients who present with depression may outweigh the harms of falsely identifying a non-depressed patient. It is our hope that the 2-QQ will serve as a quick primary screen for depression in the primary care setting and serve as a catalyst to identify and treat individuals with depression.

Keywords: depression, primary care, screening tool, Sri Lanka

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Authors: Zhao Kuo, Chen Liang, Zhang Zhongbing, Ruan Jinlu. He Shiyi, Xu Mengxuan

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Stilbene, C₁₄H₁₂, is well known as one of the most useful organic scintillators for pulse shape discrimination (PSD) technique for its good scintillation properties. An on-line acquisition system and an off-line acquisition system were developed with several CAMAC standard plug-ins, NIM plug-ins, neutron/γ discriminating plug-in named 2160A and a digital oscilloscope with high sampling rate respectively for which stilbene crystals and photomultiplier tube detectors (PMT) as detector for accelerator neutron sources measurement carried out in China Institute of Atomic Energy. Pulse amplitude spectrums and charge amplitude spectrums were real-time recorded after good neutron/γ discrimination whose best PSD figure-of-merits (FoMs) are 1.756 for D-D accelerator neutron source and 1.393 for D-T accelerator neutron source. The probability of neutron events in total events was 80%, and neutron detection efficiency was 5.21% for D-D accelerator neutron sources, which were 50% and 1.44% for D-T accelerator neutron sources after subtracting the background of scattering observed by the on-line acquisition system. Pulse waveform signals were acquired by the off-line acquisition system randomly while the on-line acquisition system working. The PSD FoMs obtained by the off-line acquisition system were 2.158 for D-D accelerator neutron sources and 1.802 for D-T accelerator neutron sources after waveform digitization off-line processing named charge integration method for just 1000 pulses. In addition, the probabilities of neutron events in total events obtained by the off-line acquisition system matched very well with the probabilities of the on-line acquisition system. The pulse information recorded by the off-line acquisition system could be repetitively used to adjust the parameters or methods of PSD research and obtain neutron charge amplitude spectrums or pulse amplitude spectrums after digital analysis with a limited number of pulses. The off-line acquisition system showed equivalent or better measurement effects compared with the online system with a limited number of pulses which indicated a feasible method based on stilbene crystals detectors for the measurement of prompt neutrons neutron sources like prompt accelerator neutron sources emit a number of neutrons in a short time.

Keywords: stilbene crystal, accelerator neutron source, neutron / γ discrimination, figure-of-merits, CAMAC, waveform digitization

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Authors: A. Ghodbane, M. Saad, J. F. Boland, C. Thibeault

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Historically, actuators’ redundancy was used to deal with faults occurring suddenly in flight systems. This technique was generally expensive, time consuming and involves increased weight and space in the system. Therefore, nowadays, the on-line fault diagnosis of actuators and accommodation plays a major role in the design of avionic systems. These approaches, known as Fault Tolerant Flight Control systems (FTFCs) are able to adapt to such sudden faults while keeping avionics systems lighter and less expensive. In this paper, a (FTFC) system based on the Geometric Approach and a Reconfigurable Flight Control (RFC) are presented. The Geometric approach is used for cosmic ray fault reconstruction, while Sliding Mode Control (SMC) based on Lyapunov stability theory is designed for the reconfiguration of the controller in order to compensate the fault effect. Matlab®/Simulink® simulations are performed to illustrate the effectiveness and robustness of the proposed flight control system against actuators’ faulty signal caused by cosmic rays. The results demonstrate the successful real-time implementation of the proposed FTFC system on a non-linear 6 DOF aircraft model.

Keywords: actuators’ faults, fault detection and diagnosis, fault tolerant flight control, sliding mode control, geometric approach for fault reconstruction, Lyapunov stability

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Meltem Agar, Maisem Laabei, Hannah Leese, Pedro Estrela

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Pathogenic bacteria and the diseases they cause have become a global problem. Their early detection is vital and can only be possible by detecting the bacteria causing the disease accurately and rapidly. Great progress has been made in this field with the use of biosensors. Molecularly imprinted polymers have gain broad interest because of their excellent properties over natural receptors, such as being stable in a variety of conditions, inexpensive, biocompatible and having long shelf life. These properties make molecularly imprinted polymers an attractive candidate to be used in biosensors. In this study it is aimed to produce an aptamer-molecularly imprinted polymer based electrochemical sensor by utilizing the properties of molecularly imprinted polymers coupled with the enhanced specificity offered by DNA aptamers. These ‘apta-MIP’ sensors were used for the detection of Staphylococcus aureus and Escherichia coli. The experimental parameters for the fabrication of sensor were optimized, and detection of the bacteria was evaluated via Electrochemical Impedance Spectroscopy. Sensitivity and selectivity experiments were conducted. Furthermore, molecularly imprinted polymer only and aptamer only electrochemical sensors were produced separately, and their performance were compared with the electrochemical sensor produced in this study. Aptamer-molecularly imprinted polymer based electrochemical sensor showed good sensitivity and selectivity in terms of detection of Staphylococcus aureus and Escherichia coli. The performance of the sensor was assessed in buffer solution and tap water.

Keywords: aptamer, electrochemical sensor, staphylococcus aureus, molecularly imprinted polymer

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Arabi Abderrazak, Bourouba Nacerdine, Ayad Mouloud, Belaout Abdeslam

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In this paper, a new test technique of analog circuits using time mode simulation is proposed for the single catastrophic faults detection in analog circuits. This test process is performed to overcome the problem of catastrophic faults being escaped in a DC mode test applied to the inverter amplifier in previous research works. The circuit under test is a second-order low pass filter constructed around this type of amplifier but performing a function that differs from that of the previous test. The test approach performed in this work is based on two key- elements where the first one concerns the unique square pulse signal selected as an input vector test signal to stimulate the fault effect at the circuit output response. The second element is the filter response conversion to a square pulses sequence obtained from an analog comparator. This signal conversion is achieved through a fixed reference threshold voltage of this comparison circuit. The measurement of the three first response signal pulses durations is regarded as fault effect detection parameter on one hand, and as a fault signature helping to hence fully establish an analog circuit fault diagnosis on another hand. The results obtained so far are very promising since the approach has lifted up the fault coverage ratio in both modes to over 90% and has revealed the harmful side of faults that has been masked in a DC mode test.

Keywords: analog circuits, analog faults diagnosis, catastrophic faults, fault detection

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Bilal Saoud

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In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: bee colony, networks, modularity, normalized mutual information

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

Abstract:

Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

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Authors: Ka Ho Yau, Jan Frederick Engels, Kwok Kei Lai, Reinhard Renneberg

Abstract:

Lateral flow test is a prevalent technology in various sectors such as food, pharmacology and biomedical sciences. Colloidal gold (CG) is widely used as the signalling molecule because of the ease of synthesis, bimolecular conjugation and its red colour due to intrinsic SPRE. However, the production of colloidal gold is costly and requires vigorous conditions. The stability of colloidal gold are easily affected by environmental factors such as pH, high salt content etc. Silica nanoparticles are well known for its ease of production and stability over a wide range of solvents. Using reverse micro-emulsion (w/o), silica nanoparticles with different sizes can be produced precisely by controlling the amount of water. By incorporating different water-soluble dyes, a rainbow colour of the silica nanoparticles could be produced. Conjugation with biomolecules such as antibodies can be achieved after surface modification of the silica nanoparticles with organosilane. The optimum amount of the antibodies to be labelled was determined by Bradford Assay. In this work, we have demonstrated the ability of the dye-doped silica nanoparticles as a signalling molecule in lateral flow test, which showed a semi-quantitative measurement of the analyte. The image was further analysed for the LOD=10 ng of the analyte. The working range and the linear range of the test were from 0 to 2.15μg/mL and from 0 to 1.07 μg/mL (R2=0.988) respectively. The performance of the tests was comparable to those using colloidal gold with the advantages of lower cost, enhanced stability and having a wide spectrum of colours. The positives lines can be imaged by naked eye or by using a mobile phone camera for a better quantification. Further research has been carried out in multicolour detection of different biomarkers simultaneously. The preliminary results were promising as there was little cross-reactivity being observed for an optimized system. This approach provides a platform for multicolour detection for a set of biomarkers that enhances the accuracy of diseases diagnostics.

Keywords: colorimetric detection, immunosensor, paper-based biosensor, silica

Procedia PDF Downloads 367

Authors: Zakir Husain, Neem Sagar, Neeraj Gupta

Abstract:

Due to the increased penetration of renewable energy resources in the distribution system, the system is no longer passive in nature. In this paper, a steady state analysis of the distribution system has been done with the inclusion of wind generation. The modeling of wind turbine generator system and wind generator has been made to obtain the average active and the reactive power injection into the system. The study has been conducted on a IEEE-33 bus system with two wind generators. The present research work is useful not only to utilities but also to customers.

Keywords: distributed generation, distribution network, radial network, wind turbine generating system

Procedia PDF Downloads 386

Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

Abstract:

Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

Procedia PDF Downloads 141

Authors: Lae-Jeong Park

Abstract:

The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: pedestrian detection, color segmentation, false positive, feature extraction

Procedia PDF Downloads 260

Authors: Yagahira E. Castro-Sesquen, Chloe Kim, Robert H. Gilman, David J. Sullivan, Peter C. Searson

Abstract:

Diagnosis of severe malaria is particularly important in highly endemic regions since most patients are positive for parasitemia and treatment differs from non-severe malaria. Diagnosis can be challenging due to the prevalence of diseases with similar symptoms. Accurate diagnosis is increasingly important to avoid overprescribing antimalarial drugs, minimize drug resistance, and minimize costs. A nanoparticle-based assay for detection and quantification of Plasmodium falciparum histidine-rich protein 2 (HRP2) in urine and serum is reported. The assay uses magnetic beads conjugated with anti-HRP2 antibody for protein capture and concentration, and antibody-conjugated quantum dots for optical detection. Western Blot analysis demonstrated that magnetic beads allows the concentration of HRP2 protein in urine by 20-fold. The concentration effect was achieved because large volume of urine can be incubated with beads, and magnetic separation can be easily performed in minutes to isolate beads containing HRP2 protein. Magnetic beads and Quantum Dots 525 conjugated to anti-HRP2 antibodies allows the detection of low concentration of HRP2 protein (0.5 ng mL-1), and quantification in the range of 33 to 2,000 ng mL-1 corresponding to the range associated with non-severe to severe malaria. This assay can be easily adapted to a non-invasive point-of-care test for classification of severe malaria.

Keywords: HRP2 protein, malaria, magnetic beads, Quantum dots

Procedia PDF Downloads 312

Authors: A. J. Babajanyan, T. A. Abrahamyan, H. A. Minasyan, K. V. Nerkararyan

Abstract:

Optical radiation emitted from a metal-coated fiber tip apex at liquid-air interface was measured. The intensity of the output radiation was strongly depending on the relative position of the tip to a liquid-air interface and varied with surface fluctuations. This phenomenon permits in-situ real-time investigation of nano-metric vibrations of the liquid surface and provides a basis for development of various origin ultrasensitive vibration detecting sensors. The described method can be used for detection of week seismic vibrations.

Keywords: fiber-tip, liquid-air interface, nano vibration, opto-mechanical sensor

Procedia PDF Downloads 467