Search results for: trend detection

Authors: N. Varsamis, M. Gerasimou, P. Christodoulou, S. Mantzoukis, G. Kolliopoulou, N. Zotos

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Purpose: Body fluids (pleural, peritoneal, synovial, pericardial, cerebrospinal) are an important element in the detection of microorganisms. For this reason, it is important to examine them in the Intensive Care Unit (ICU) patients. Material and Method: Body fluids are transported through sterile containers and enriched as soon as possible with Tryptic Soy Broth (TSB). After one day of incubation, the broth is poured into selective media: Blood, Mac Conkey No. 2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. The above selective media are incubated directly for 2 days. After this period, if any number of microbial colonies are detected, gram staining is performed. After that, the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) and followed by a sensitivity test on the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by Kirby Bauer-based plate test. Results: In 2017 the Laboratory of Microbiology received 60 samples of body fluids from the ICU. More specifically the Microbiology Department received 6 peritoneal fluid specimens, 18 pleural fluid specimens and 36 cerebrospinal fluid specimens. 36 positive cultures were tested. S. epidermidis was identified in 18 specimens, S. haemolyticus in 6, and E. faecium in 12. Conclusions: The results show low detection of microorganisms in body fluid cultures.

Keywords: body fluids, culture, intensive care unit, microorganisms

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Authors: Z. Abdollahnejad, M. Kheradmand, F. Pacheco-Torgal

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The use of bio-based admixtures on construction materials is a recent trend that is gaining momentum. However, to our knowledge, no studies have been reported concerning the use of biopolymers on hybrid cement based mortars. This paper reports experimental results regarding the study of the influence of mix design of 43 hybrid cement mortars containing two different biopolymers on its mechanical performance. The results show that the use of the biopolymer carrageenan is much more effective than the biopolymer xanthan concerning the increase in compressive strength. An optimum biopolymer content was found.

Keywords: waste reuse, fly ash, waste glass, hybrid cement, biopolymers, mechanical strength

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: S. Pison

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The objective of presenting this article is to analyze between Thai’s film and Thai society in political crisis, to study the development and trend of the film which reflects society in Thailand from political crisis of 14 October 1973 and the present day political crisis using a comparative study of the two era, both the similarities and differences in the film reflects the society in an era of change.

Keywords: film, political, neo-realism, social, Thailand

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: Praveena Nair Sivasankaran, Thomas Arthur Ward, Rubentheren Viyapuri

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Paper describes a methodology to fabricate a simplified dragonfly wing structure using balsa wood and red prepreg fibre glass. These simplified wing structures were created for use in Biomimetic Micro Air Vehicles (BMAV). Dragonfly wings are highly corrugated and possess complex vein structures. In order to mimic the wings function and retain its properties, a simplified version of the wing was designed. The simplified dragonfly wing structure was created using a method called spatial network analysis which utilizes Canny edge detection method. The vein structure of the wings were carved out in balsa wood and red prepreg fibre glass. Balsa wood and red prepreg fibre glass was chosen due to its ultra- lightweight property and hence, highly suitable to be used in our application. The fabricated structure was then immersed in a nanocomposite solution containing chitosan as a film matrix, reinforced with chitin nanowhiskers and tannic acid as a crosslinking agent. These materials closely mimic the membrane of a dragonfly wing. Finally, the wings were subjected to a bending test and comparisons were made with previous research for verification. The results had a margin of difference of about 3% and thus the structure was validated.

Keywords: dragonfly wings, simplified, Canny edge detection, balsa wood, red prepreg, chitin, chitosan, tannic acid

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Authors: Ayanfulu Yekini

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The hotel and tourism industry remains male-dominated, particularly in the upper echelons of management and ICT remained underutilized. While there is a massive revolution in this trend across the globe, it appears much progress has not been made in our nation Nigeria. This paper aimed at evaluating the relevance of ICT and Gender Participation to Sustainability of Hospitality and Tourism Industry in Nigeria. The research study was conducted in tourism organizations, travel agents, hotels, restaurants, resorts, professionals in tourism, travel and hospitality industry within Nigeria. The respondents are from the tourism/hospitality industries employees and entrepreneurs only.

Keywords: ICT, hotel, gender participation, Nigeria, tourism

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Purba Biswas, Priyanka Dey

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Worldwide rapid urbanisation has accelerated city expansion in both developed and developing nations. This unprecedented urbanisation trend due to the increasing population and economic growth has caused challenges for the decision-makers in city planning and urban management. Metropolitan cities, class-I towns, and major urban centres undergo a continuous process of evolution due to interaction between socio-cultural and economic attributes. This constant evolution leads to urban expansion in all directions. Understanding the patterns and dynamics of urban built-up growth is crucial for policymakers, urban planners, and researchers, as it aids in resource management, decision-making, and the development of sustainable strategies to address the complexities associated with rapid urbanisation. Identifying spatio-temporal patterns of urban growth has emerged as a crucial challenge in monitoring and assessing present and future trends in urban development. Analysing urban growth patterns and tracking changes in land use is an important aspect of urban studies. This study analyses spatio-temporal urban transformations and land-use and land cover changes using remote sensing and GIS techniques. Built-up growth analysis has been done for the city of Surat as a case example, using the GIS tools of NDBI and GIS models of the Built-up Urban Density Index and Shannon Entropy Index to identify trends and the geographical direction of transformation from 2005 to 2020. Surat is one of the fastest-growing urban centres in both the state and the nation, ranking as the 4th fastest-growing city globally. This study analyses the dynamics of urban built-up area transformations both zone-wise and geographical direction-wise, in which their trend, rate, and magnitude were calculated for the period of 15 years. This study also highlights the need for analysing and monitoring the urban growth pattern of class-I cities in India using spatio-temporal and quantitative techniques like GIS for improved urban management.

Keywords: urban expansion, built-up, geographic information system, remote sensing, Shannon’s entropy

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Ahmed Saeed Mohamed, Sadok Sassi, Mohammad Roshun Paurobally

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Gears are important machine components that are widely used to transmit power and change speed in many rotating machines. Any breakdown of these vital components may cause severe disturbance to production and incur heavy financial losses. One of the most common causes of gear failure is the tooth fatigue crack. Early detection of teeth cracks is still a challenging task for engineers and maintenance personnel. So far, to analyze the vibration behavior of gears, different approaches have been tried based on theoretical developments, numerical simulations, or experimental investigations. The objective of this study was to develop a numerical model that could be used to simulate the effect of teeth cracks on the resulting vibrations and hence to permit early fault detection for gear transmission systems. Unlike the majority of published papers, where only one single crack has been considered, this work is more realistic, since it incorporates the possibility of multiple simultaneous cracks with different lengths. As cracks significantly alter the gear mesh stiffness, we performed a finite element analysis using SolidWorks software to determine the stiffness variation with respect to the angular position for different combinations of crack lengths. A simplified six degrees of freedom non-linear lumped parameter model of a one-stage gear system is proposed to study the vibration of a pair of spur gears, with and without tooth cracks. The model takes several physical properties into account, including variable gear mesh stiffness and the effect of friction, but ignores the lubrication effect. The vibration simulation results of the gearbox were obtained via Matlab and Simulink. The results were found to be consistent with the results from previously published works. The effect of one crack with different levels was studied and very similar changes in the total mesh stiffness and the vibration response, both were observed and compared to what has been found in previous studies. The effect of the crack length on various statistical time domain parameters was considered and the results show that these parameters were not equally sensitive to the crack percentage. Multiple cracks are introduced at different locations and the vibration response and the statistical parameters were obtained.

Keywords: dynamic simulation, gear mesh stiffness, simultaneous tooth cracks, spur gear, vibration-based fault detection

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Authors: K. Hiro, Y. Imai, T. Sasayama

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Cavitation under static negative pressure is not revealed well. The Berthelot method to generate such negative pressure can be a means to study cavitation inception. In this study, metal Berthelot tubes built in observation windows are newly developed and are checked whether high static negative pressure is generated or not. Negative pressure in the tube with a pair of a corundum plate and an aluminum gasket increased with temperature cycles. The trend was similar to that as reported before.

Keywords: Berthelot method, cavitation, negative pressure, observation

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Authors: Erick R. Bandala, Kebret Kebede, Nicole Johnson, Rebecca Murray, Destiny Green, John Mejia, Polioptro Martinez-Austria

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Summertemperature data from Clark County was collected and used to estimate two different heat-related indexes: the heat index (HI) and excess heat factor (EHF). These two indexes were used jointly with data of health-related deaths in Clark County to assess the effect of extreme heat on the exposed population. The trends of the heat indexes were then analyzed for the 2007-2016 decadeandthe correlation between heat wave episodes and the number of heat-related deaths in the area was estimated. The HI showed that this value has increased significantly in June, July, and August over the last ten years. The same trend was found for the EHF, which showed a clear increase in the severity and number of these events per year. The number of heat wave episodes increased from 1.4 per year during the 1980-2016 period to 1.66 per yearduring the 2007-2016 period. However, a different trend was found for heat-wave-event duration, which decreasedfrom an average of 20.4 days during the trans-decadal period (1980-2016) to 18.1 days during the most recent decade(2007-2016). The number of heat-related deaths was also found to increase from 2007 to 2016, with 2016 with the highest number of heat-related deaths. Both HI and the number of deaths showeda normal-like distribution for June, July, and August, with the peak values reached in late July and early August. The average maximum HI values better correlated with the number of deaths registered in Clark County than the EHF, probably because HI uses the maximum temperature and humidity in its estimation,whereas EHF uses the average medium temperature. However, it is worth testing the EHF of the study zone because it was reported to fit properly in the case of heat-related morbidity. For the overall period, 437 heat-related deaths were registered in Clark County, with 20% of the deaths occurring in June, 52% occurring in July, 18% occurring in August,and the remaining 10% occurring in the other months of the year. The most vulnerable subpopulation was people over 50 years old, for which 76% of the heat-related deaths were registered.Most of the cases were associated with heart disease preconditions. The second most vulnerable subpopulation was young adults (20-50), which accounted for 23% of the heat-related deaths. These deathswere associated with alcoholic/illegal drug intoxication.

Keywords: heat, health, hazards, workforce

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Authors: Samaneh Nabavi, Hadi Shirzad, Arash Ghoorchian, Maryam Shanesaz, Reza Naderi

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Morphine (MO), the most effective painkiller, is considered the reference by which analgesics are assessed. It is very necessary for the biomedical applications to detect and maintain the MO concentrations in the blood and urine with in safe ranges. To date, there are many expensive techniques for detecting MO. Recently, many electrochemical sensors for direct determination of MO were constructed. The molecularly imprinted polymer (MIP) is a polymeric material, which has a built-in functionality for the recognition of a particular chemical substance with its complementary cavity.This paper reports a sensor for MO using a combination of a molecularly imprinted polymer (MIP) and differential-pulse voltammetry (DPV). Electropolymerization of MO doped polypyrrole yielded poor quality, but a well-doped, nanostructure and increased impregnation has been obtained in the pH=12. Above a pH of 11, MO is in the anionic forms. The effect of various experimental parameters including pH, scan rate and accumulation time on the voltammetric response of MO was investigated. At the optimum conditions, the concentration of MO was determined using DPV in a linear range of 7.07 × 10−6 to 2.1 × 10−4 mol L−1 with a correlation coefficient of 0.999, and a detection limit of 13.3 × 10-8 mol L−1, respectively. The effect of common interferences on the current response of MO namely ascorbic acid (AA) and uric acid (UA) is studied. The modified electrode can be used for the determination of MO spiked into urine samples, and excellent recovery results were obtained. The nanostructured polypyrrole films were characterized by field emission scanning electron microscopy (FESEM) and furrier transforms infrared (FTIR).

Keywords: morphine detection, sensor, polypyrrole, nanostructure, molecularly imprinted polymer

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: M. F. Abreu, A. I. Aguiar, R. Gaio, R. Duarte

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Background: Prison establishments constitute high-risk environments for the transmission and spread of tuberculosis (TB), given their epidemiological context and the difficulty of implementing preventive and control measures. Guidelines for control and prevention of tuberculosis in prisons have been described as incomplete and heterogeneous internationally, due to several identified obstacles, for example scarcity of human resources and funding of prisoner health services. In Portugal, a protocol was created in 2014 with the aim to define and standardize procedures of detection and prevention of tuberculosis within prisons. Objective: The main objective of this study was to identify and describe barriers to tuberculosis detection in prisons of Porto and Lisbon districts in Portugal. Methods: A cross-sectional study was conducted from 2ⁿᵈ January 2018 till 30ᵗʰ June 2018. Semi-structured questionnaires were applied to health care professionals working in the prisons of the districts of Porto (n=6) and Lisbon (n=8). As inclusion criteria we considered having work experience in the area of tuberculosis (either in diagnosis, treatment, or follow up). The questionnaires were self-administered, in paper format. Descriptive analyses of the questionnaire variables were made using frequencies and median. Afterwards, a hierarchical agglomerative clusters analysis was performed. After obtaining the clusters, the chi-square test was applied to study the association between the variables collected and the clusters. The level of significance considered was 0.05. Results: From the total of 186 health professionals, 139 met the criteria of inclusion and 82 health professionals were interviewed (62,2% of participation). Most were female, nurses, with a median age of 34 years, with term employment contract. From the cluster analysis, two groups were identified with different characteristics and behaviors for the procedures of this protocol. Statistically significant results were found in: elements of cluster 1 (78% of the total participants) work in prisons for a longer time (p=0.003), 45,3% work > 4 years while 50% of the elements of cluster 2 work for less than a year, and more frequently answered they know and apply the procedures of the protocol (p=0.000). Both clusters answered frequently the need of having theoretical-practical training for TB (p=0.000), especially in the areas of diagnosis, treatment and prevention and that there is scarcity of funding to prisoner health services (p=0.000). Regarding procedures for TB screening (periodic and contact screening) and procedures for transferring a prisoner with this disease, cluster 1 also answered more frequently to perform them (p=0.000). They also referred that the material/equipment for TB screening is accessible and available (p=0.000). From this clusters we identified as barriers scarcity of human resources, the need to theoretical-practical training for tuberculosis, inexperience in working in health services prisons and limited knowledge of protocol procedures. Conclusions: The barriers found in this study are the same described internationally. This protocol is mostly being applied in portuguese prisons. The study also showed the need to invest in human and material resources. This investigation bridged gaps in knowledge that could help prison health services optimize the care provided for early detection and adherence of prisoners to treatment of tuberculosis.

Keywords: barriers, health care professionals, prisons, protocol, tuberculosis

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Authors: Priya Saha, Sourav Dey Roy Jr., Debotosh Bhattacharjee, Mita Nasipuri, Barin Kumar De, Mrinal Kanti Bhowmik

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Biometric face recognition technology (FRT) has gained a lot of attention due to its extensive variety of applications in both security and non-security perspectives. It has come into view to provide a secure solution in identification and verification of person identity. Although other biometric based methods like fingerprint scans, iris scans are available, FRT is verified as an efficient technology for its user-friendliness and contact freeness. Accurate facial feature localization plays an important role for many facial analysis applications including biometrics and emotion recognition. But, there are certain factors, which make facial feature localization a challenging task. On human face, expressions can be seen from the subtle movements of facial muscles and influenced by internal emotional states. These non-rigid facial movements cause noticeable alterations in locations of facial landmarks, their usual shapes, which sometimes create occlusions in facial feature areas making face recognition as a difficult problem. The paper proposes a new hybrid based technique for automatic landmark detection in both neutral and expressive frontal and near frontal face images. The method uses the concept of thresholding, sequential searching and other image processing techniques for locating the landmark points on the face. Also, a Graphical User Interface (GUI) based software is designed that could automatically detect 16 landmark points around eyes, nose and mouth that are mostly affected by the changes in facial muscles. The proposed system has been tested on widely used JAFFE and Cohn Kanade database. Also, the system is tested on DeitY-TU face database which is created in the Biometrics Laboratory of Tripura University under the research project funded by Department of Electronics & Information Technology, Govt. of India. The performance of the proposed method has been done in terms of error measure and accuracy. The method has detection rate of 98.82% on JAFFE database, 91.27% on Cohn Kanade database and 93.05% on DeitY-TU database. Also, we have done comparative study of our proposed method with other techniques developed by other researchers. This paper will put into focus emotion-oriented systems through AU detection in future based on the located features.

Keywords: biometrics, face recognition, facial landmarks, image processing

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Authors: Julius Niba Fon, Jean Hude E. Moudingo

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Mangrove ecosystems in Cameroon are valuable both in services and functions as they play host to carbon sinks, fishery breeding grounds and natural coastal barriers against storms. In addition to the globally important biodiversity that they contain, they also contribute to local livelihoods. Despite these appraisals, a reduction of about 30 % over a 25 years period due to anthropogenic and natural actions has been recorded. The key drivers influencing mangrove change include population growth, climate change, economic and political trends and upstream habitat use. Reversing the trend of mangrove loss and growing vulnerability of coastal peoples requires a real commitment by the government to develop and implement robust level policies. It has been observed in Cameroon that special ecosystems like mangroves are insufficiently addressed by forestry and/or environment programs. Given these facts, the Food Agriculture Organization (FAO) in partnership with the Government of Cameroon and other development actors have put in place the project for sustainable community-based management and conservation of mangrove ecosystems in Cameroon. The aim is to address two issues notably the present weak institutional and legal framework for mangrove management, and the unrestricted and unsustainable harvesting of mangrove resources. Civil society organizations like the Cameroon Wildlife Conservation Society, Cameroon Ecology and Organization for the Environment and Development have been working to reduce the deforestation and degradation trend of Cameroon mangroves and also bringing the mangrove agenda to the fore in national and international arenas. Following a desktop approach, we found out that in situ and ex situ initiatives on mangrove management and conservation exist on propagation of improved fish smoke ovens to reduce fuel wood consumption, mangrove forest regeneration, shrimps farming and mangrove protected areas management. The evidence generated from the field experiences are inputs for processes of improving the legal and institutional framework for mangrove management in Cameroon, such as the elaboration of norms for mangroves management engaged by the government.

Keywords: mangrove ecosystem, legal and institutional framework, climate change, civil society organizations

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Authors: G. Sandhya Devi, G. Suvarna Kumar, S. Chandini

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This research work aims to develop a system that will analyze and identify students who indulge in malpractices/suspicious activities during the course of an academic offline examination. Automated Video Surveillance provides an optimal solution which helps in monitoring the students and identifying the malpractice event immediately. This work is organized into three modules. The first module deals with performing an impersonation check using a PCA-based face recognition method which is done by cross checking his profile with the database. The presence or absence of the student is even determined in this module by implementing an image registration technique wherein a grid is formed by considering all the images registered using the frontal camera at the determined positions. Second, detecting such facial malpractices in which a student gets involved in conversation with another, trying to obtain unauthorized information etc., based on the threshold range evaluated by considering his/her mouth state whether open or closed. The third module deals with identification of unauthorized material or gadgets used in the examination hall by training the positive samples of the object through various stages. Here, a top view camera feed is analyzed to detect the suspicious activities. The system automatically alerts the administration when any suspicious activities are identified, thereby reducing the error rate caused due to manual monitoring. This work is an improvement over our previous work published in identifying suspicious activities done by examinees in an offline examination.

Keywords: impersonation, image registration, incrimination, object detection, threshold evaluation

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

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Authors: Mohammad Abu-Shaira, Weishi Shi

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Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

Procedia PDF Downloads 13

Authors: M. R. Haji Hajikolaei, A. A. Nikvand, A. R. Ghadrdan, M. Ghorbanpoor, B. Mohammadian

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This study was carried out on water buffalo for detection of Leptospira interrogans in kidney and urine and its relationship with serological findings. Blood, urine and kidney samples were taken immediately after slaughter from 353 water buffalos at Ahvaz abattoir in Khouzestan province, Iran. Sera were initially screened at serum dilution of 1:100 against seven live antigens of Leptospira interrogans: pomona, hardjo, ballum, icterohemorrhagiae, tarasovi, australis and grippotyphosa using the microscopic agglutination test (MAT) and sera with positive results were titrated against reacting antigens in serial twofold dilution from 1:100 to 1:800. The samples of kidney were embedded in paraffin wax and 5µm thick sections were stained routinely with Haematoxylin and Eosin (H&E). Polymerase chain reaction (PCR) examination was done on urine and kidney by using LipL32 gene primers. Antibodies against one or more serovars at dilution >:100 were detected in sera. The most frequent reactor was hardjo (56.2%), followed by pomona (52.3%), australis (9.8%), tarassovi (5.9%), grippotyphosa (4.5%) and icterohaemorrhagiae (3.9%). The L. interrogans were detected in 43 (12.2%) of examined buffaloes, so that 26 (8.2%) of kidney tissues, 14 (4.8%) of urine samples separately and 3 (0.84%) of both kidney and urine samples were positive in PCR. From 153 (43.3%) buffaloes with positive MAT, 24 cases were positive by PCR of kidney and/or urine samples, synchronously. Renal lesions such as interstitial nephritis, acute tubular necrosis (ATN), pyelonephritis, glomerolonephritis, renal fibrosis and hydronephrosis were found in 128 (36.3%) cases. Statistical analysis indicated that there was no significant association between results of MAT, PCR and interstitial nephritis.

Keywords: leptospiral infection, PCR, MAT, histopathology, river buffalo

Procedia PDF Downloads 332

Authors: Gaurav Kumar Sinha

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The oil and gas industry is a critical component of the global economy, relying heavily on industrial control systems (ICS) to manage and monitor operations. However, these systems are increasingly becoming targets for cyber-attacks, posing significant risks to operational continuity, safety, and environmental integrity. This paper explores comprehensive cybersecurity strategies for protecting oil and gas industrial control systems. It delves into the unique vulnerabilities of ICS in this sector, including outdated legacy systems, integration with IT networks, and the increased connectivity brought by the Industrial Internet of Things (IIoT). We propose a multi-layered defense approach that includes the implementation of robust network security protocols, regular system updates and patch management, advanced threat detection and response mechanisms, and stringent access control measures. We illustrate the effectiveness of these strategies in mitigating cyber risks and ensuring the resilient and secure operation of oil and gas industrial control systems. The findings underscore the necessity for a proactive and adaptive cybersecurity framework to safeguard critical infrastructure in the face of evolving cyber threats.

Keywords: cybersecurity, industrial control systems, oil and gas, cyber-attacks, network security, IoT, threat detection, system updates, patch management, access control, cybersecurity awareness, critical infrastructure, resilience, cyber threats, legacy systems, IT integration, multi-layered defense, operational continuity, safety, environmental integrity

Procedia PDF Downloads 44

Authors: Zatovska T. V., Nesterova N. V., Baranova G. V., Zagorodnya S. D.

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In recent years, biosensor technologies based on the phenomenon of surface plasmon resonance (SPR) are becoming increasingly used in biology and medicine. Their application facilitates exploration in real time progress of binding of biomolecules and identification of agents that specifically interact with biologically active substances immobilized on the biosensor surface (biochips). Special attention is paid to the use of Biosensor analysis in determining the antibody-antigen interaction in the diagnostics of diseases caused by viruses and bacteria. According to WHO, the diseases that are caused by the herpes simplex virus (HSV), take second place (15.8%) after influenza as a cause of death from viral infections. Current diagnostics of HSV infection include PCR and ELISA assays. The latter allows determination the degree of immune response to viral infection and respective stages of its progress. In this regard, the searches for new and available diagnostic methods are very important. This work was aimed to develop Biosensor chip for detection of specific antibodies to HSV-1 in the human blood serum. The proteins of HSV1 (strain US) were used as antigens. The viral particles were accumulated in cell culture MDBK and purified by differential centrifugation in cesium chloride density gradient. Analysis of the HSV1 proteins was performed by polyacrylamide gel electrophoresis and ELISA. The protein concentration was measured using De Novix DS-11 spectrophotometer. The device for detection of antigen-antibody interactions was an optoelectronic two-channel spectrometer ‘Plasmon-6’, using the SPR phenomenon in the Krechman optical configuration. It was developed at the Lashkarev Institute of Semiconductor Physics of NASU. The used carrier was a glass plate covered with 45 nm gold film. Screening of human blood serums was performed using the test system ‘HSV-1 IgG ELISA’ (GenWay, USA). Development of Biosensor chip included optimization of conditions of viral antigen sorption and analysis steps. For immobilization of viral proteins 0.2% solution of Dextran 17, 200 (Sigma, USA) was used. Sorption of antigen took place at 4-8°C within 18-24 hours. After washing of chip, three times with citrate buffer (pH 5,0) 1% solution of BSA was applied to block the sites not occupied by viral antigen. It was found direct dependence between the amount of immobilized HSV1 antigen and SPR response. Using obtained biochips, panels of 25 positive and 10 negative for the content of antibodies to HSV-1 human sera were analyzed. The average value of SPR response was 185 a.s. for negative sera and from 312 to. 1264 a.s. for positive sera. It was shown that SPR data were agreed with ELISA results in 96% of samples proving the great potential of SPR in such researches. It was investigated the possibility of biochip regeneration and it was shown that application of 10 mM NaOH solution leads to rupture of intermolecular bonds. This allows reuse the chip several times. Thus, in this study biosensor chip for detection of specific antibodies to HSV1 was successfully developed expanding a range of diagnostic methods for this pathogen.

Keywords: biochip, herpes virus, SPR

Procedia PDF Downloads 417

Authors: Kanokwan Tangchitnusorn, Patcharawalai Wongboonsin

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Following the ‘Golden Age of Welfare’ which enabled post-war prosperity to European citizens in 1950s, the world has witnessed the increasing mobility across borders of older citizens of First World countries. Then, in 1990s, the international retirement migration (IRM) of older persons has become a prominent trend, in which, it requires the integration of several fields of knowledge to explain, i.e. migration studies, tourism studies, as well as, social gerontology. However, while the studies of the IRM to developed destinations in Europe (e.g. Spain, Malta, Portugal, Italy), and the IRM to developing countries like Mexico, Panama, and Morocco have been largely studied in recent decades due to their massive migration volume, the study of the IRM to remoter destinations has been far more relatively sparse and incomplete. Developing countries in Southeast Asia have noticed the increasing number of retired expats, particularly to Thailand, where the number of foreigners applying for retirement visa increased from 10,709 in 2005 to 60,046 in 2014. Additionally, it was evident that the majority of Thailand’s retirement visa applicants were Westerners, i.e. citizens of the United Kingdom, the United States, Germany, and the Nordic countries, respectively. As such trend just becoming popular in Thailand in recent decades, little is known about the IRM populations, their well-being, and their future migration plans. This study aimed to examine the subjective wellbeing or the self-evaluations of own well-being among Western retirees in Thailand, as well as, their future migration plans as whether they planned to stay here for life or otherwise. The author employed a mixed method to obtain both quantitative and qualitative data during October 2015 – May 2016, including 330 self-administered questionnaires (246 online and 84 hard-copied responses), and 21 in-depth interviews of the Western residents in Nan (2), Pattaya (4), and Chiang Mai (15). As derived from the integration of previous subjective well-being measurements (i.e. Personal Wellbeing Index (PWI), Global AgeWatch Index, and OECD guideline on measuring subjective wellbeing), this study would measure the subjective well-being of Western retirees in Thailand in 7 dimensions, including standard of living, health status, personal relationships, social connections, environmental quality, personal security and local infrastructure.

Keywords: international retirement migration, ageing, mobility, wellbeing, Western, Thailand

Procedia PDF Downloads 344

Authors: Maryam Torabi, Habibi, Abdolahi, Mohammadi, Hassanzadeh, Darban Maghami, Baghi

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Sheeppox Virus (SPPV) and goatpox virus (GTPV) are considerable diseases of sheep, and goats, caused by viruses of the Capripoxvirus (CaPV) genus. They are responsible for economic losses. Animal mortality, morbidity, cost of vaccinations, and restrictions in animal products’ trade are the reasons of economic losses. Control and eradication of CaPV depend on early detection of outbreaks so that molecular detection and genetic analysis could be effective to this aim. This study was undertaken to molecularly characterize SPPV and GTPV strains that have been circulating in Iran. 120 skin papules and nodule biopsies were collected from different regions of Iran and were examined for SPPV, GTPV viruses using TaqMan Real -Time PCR. Some of these amplified genes were sequenced, and phylogenetic trees were constructed. Out of the 120 samples analysed, 98 were positive for CaPV by Real- Time PCR (81.6%), and most of them wereSPPV. then 10 positive samples were sequenced and characterized by amplifying the ORF 103CaPV gene. sequencing and phylogenetic analysis for these positive samples revealed a high percentage of identity with SPPV isolated from different countries in Middle East. In conclusions, molecular characterization revealed nearly complete identity with all recent SPPVs strains in local countries that requires further studies to monitor the virus evolution and transmission pathways to better understand the virus pathobiology that will help for SPPV control.

Keywords: molecular epidemiology, Real-Time PCR, phylogenetic analysis, capripoxviruses

Procedia PDF Downloads 149

Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

Procedia PDF Downloads 64

Authors: Tabassum Riaz

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This study provides a complete examination of the stock prices behavior in the Karachi stock exchange. It examines that whether Karachi stock exchange can be described as mean reversion or not. For this purpose daily, weekly and monthly index data from Karachi stock exchange ranging from period July 1, 1997 to July 2, 2011 was taken. After employing the Multiple variance ratio and unit root tests it is concluded that stock market follow mean reversion behavior and hence have reverting trend which opens the door for the active invest management. Thus technical analysis may be help to identify the potential areas for value creation.

Keywords: mean reversion, random walk, technical analysis, Karachi stock exchange

Procedia PDF Downloads 432