Search results for: signal detection theory

Authors: Shashank Kumar, Mansi Chandra, Ujjawal Singh, Parth Gupta, Rishi Ram, Arnab Sarkar

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Milk is one of the basic and primary sources of food and energy as we start consuming milk from birth. Hence, milk quality and purity and checking the concentration of its constituents become necessary steps. Considering the importance of the purity of milk for human health, the following study has been carried out to simultaneously detect and quantify the different adulterants like urea and starch in milk with the help of a paper-based microfluidic device integrated with a smartphone. The detection of the concentration of urea and starch is based on the principle of colorimetry. In contrast, the fluid flow in the device is based on the capillary action of porous media. The microfluidic channel proposed in the study is equipped with a specialized detection zone, and it employs a colorimetric indicator undergoing a visible color change when the milk gets in touch or reacts with a set of reagents which confirms the presence of different adulterants in the milk. In our proposed work, we have used iodine to detect the percentage of starch in the milk, whereas, in the case of urea, we have used the p-DMAB. A direct correlation has been found between the color change intensity and the concentration of adulterants. A calibration curve was constructed to find color intensity and subsequent starch and urea concentration. The device has low-cost production and easy disposability, which make it highly suitable for widespread adoption, especially in resource-constrained settings. Moreover, a smartphone application has been developed to detect, capture, and analyze the change in color intensity due to the presence of adulterants in the milk. The low-cost nature of the smartphone-integrated paper-based sensor, coupled with its integration with smartphones, makes it an attractive solution for widespread use. They are affordable, simple to use, and do not require specialized training, making them ideal tools for regulatory bodies and concerned consumers.

Keywords: paper based microfluidic device, milk adulteration, urea detection, starch detection, smartphone application

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Authors: Christoph Ostermair

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We deal with the effect of correlation between prospects on human decision making under uncertainty as proposed by the comparatively new and promising model of “salience theory of choice under risk”. In this regard, we show that the theory entails the prediction that the inconsistency of choices, known as the Allais paradox, should not be an issue in the context of “real-life decision making”, which typically corresponds to situations of subjective uncertainty. The Allais paradox, probably the best-known anomaly regarding expected utility theory, would then essentially have no practical relevance. If, however, empiricism contradicts this prediction, salience theory might suffer a serious setback. Explanations of the model for variable human choice behavior are mostly the result of a particular mechanism that does not come to play under perfect correlation. Hence, if it turns out that correlation between prospects – as typically found in real-world applications – does not influence human decision making in the expected way, this might to a large extent cost the theory its explanatory power. The empirical literature regarding the Allais paradox under subjective uncertainty is so far rather moderate. Beyond that, the results are hard to maintain as an argument, as the presentation formats commonly employed, supposably have generated so-called event-splitting effects, thereby distorting subjects’ choice behavior. In our own incentivized experimental study, we control for such effects by means of two different choice settings. We find significant event-splitting effects in both settings, thereby supporting the suspicion that the so far existing empirical results related to Allais paradoxes under subjective uncertainty may not be able to answer the question at hand. Nevertheless, we find that the basic tendency behind the Allais paradox, which is a particular switch of the preference relation due to a modified common consequence, shared by two prospects, is still existent both under an event-splitting and a coalesced presentation format. Yet, the modal choice pattern is in line with the prediction of salience theory. As a consequence, the effect of correlation, as proposed by the model, might - if anything - only weaken the systematic choice pattern behind the Allais paradox.

Keywords: Allais paradox, common consequence effect, models of decision making under risk and uncertainty, salience theory

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Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

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The edges of low contrast images are not clearly distinguishable to the human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: low contrast image, fractional order differentiator, Laplacian of Gaussian (LoG) method, chebyshev polynomial

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: H. Afsari, H. Shokouhmand

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When stack is placed in a thermoacoustic resonator in a cryocooler, one extremity of the stack heats up while the other cools down due to the thermoacoustic effect. In the present, with expression a formula by linear theory, will see this temperature difference depends on what factors. The computed temperature difference is compared to the one predicted by the formula. These discrepancies can not be attributed to non-linear effects, rather they exist because of thermal effects. Two correction factors are introduced for close up results among linear theory and computed and use these correction factors to modified linear theory. In fact, this formula, is optimized by GA (Genetic Algorithm). Finally, results are shown at different Mach numbers and stack location in resonator.

Keywords: heat transfer, thermoacoustic cryocooler, stack, resonator, mach number, genetic algorithm

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: Cemil Akcay, Goksun Yerlikaya

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Buildings are the living spaces of people with an active role in every aspect of life in today's world. While some structures have survived from the early ages, most of the buildings that completed their lifetime have not transported to the present day. Nowadays, buildings that do not meet the social, economic, and safety requirements of the age return to life with a transformation process. This transformation is called urban transformation. Urban transformation is the renewal of the areas with a risk of disaster and the technological infrastructure required by the structure. The transformation aims to prevent damage to earthquakes and other disasters by rebuilding buildings that have completed their non-earthquake-resistant economic life. It is essential to decide on other issues related to conversion and transformation in places where most of the building stock should transform into the first-degree earthquake belt, such as Istanbul. In urban transformation, property owners, local authority, and contractor must deal at a common point. Considering that hundreds of thousands of property owners are sometimes in the areas of transformation, it is evident how difficult it is to make the deal and decide. For the optimization of these decisions, the use of game theory is foreseeing. The main problem in this study is that the urban transformation is carried out in place, or the building or buildings are transport to a different location. There are many stakeholders in the Istanbul University Cerrahpaşa Medical Faculty Campus, which is planned to be carried out in the process of urban transformation, was tried to solve the game theory applications. An analysis of the decisions given on a real urban transformation project and the logical suitability of decisions taken without the use of game theory were also supervised using game theory. In each step of this study, many decision-makers are classifying according to a specific logical sequence, and in the game trees that emerged as a result of this classification, Nash balances were tried to observe, and optimum decisions were determined. All decisions taken for this project have been subjected to two significant differentiated comparisons using game theory, and as decisions are taken without the use of game theory, and according to the results, solutions for the decision phase of the urban transformation process introduced. The game theory model developed from beginning to the end of the urban transformation process, particularly as a solution to the difficulty of making rational decisions in large-scale projects with many participants in the decision-making process. The use of a decision-making mechanism can provide an optimum answer to the demands of the stakeholders. In today's world for the construction sector, it is also seeing that the game theory is a non-surprising consequence of the fact that it is the most critical issues of planning and making the right decision in future years.

Keywords: urban transformation, the game theory, decision making, multi-actor project

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Authors: K. Shiba, T. Kaburagi, Y. Kurihara

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With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.

Keywords: wander, microwave Doppler sensor, respiratory frequency band, the state transition, hidden Markov model (HMM).

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Authors: Mohamed K. Metwally, Sherif H. El-Gohary, Kyung Min Byun, Seung Moo Han, Soo Yeol Lee, Min Hyoung Cho, Gon Khang, Jinsung Cho, Tae-Seong Kim

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Photoacoustic imaging (PAI) is a non-invasive and non-ionizing imaging modality that combines the absorption contrast of light with ultrasound resolution. Laser is used to deposit optical energy into a target (i.e., optical fluence). Consequently, the target temperature rises, and then thermal expansion occurs that leads to generating a PA signal. In general, most image reconstruction algorithms for PAI assume uniform fluence within an imaging object. However, it is known that optical fluence distribution within the object is non-uniform. This could affect the reconstruction of PA images. In this study, we have investigated the influence of optical fluence distribution on PA back-propagation imaging using finite element method. The uniform fluence was simulated as a triangular waveform within the object of interest. The non-uniform fluence distribution was estimated by solving light propagation within a tissue model via Monte Carlo method. The results show that the PA signal in the case of non-uniform fluence is wider than the uniform case by 23%. The frequency spectrum of the PA signal due to the non-uniform fluence has missed some high frequency components in comparison to the uniform case. Consequently, the reconstructed image with the non-uniform fluence exhibits a strong smoothing effect.

Keywords: finite element method, fluence distribution, Monte Carlo method, photoacoustic imaging

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Authors: Sally Heyeon Hwang

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Within decision theory, there are normative principles that dictate how one should act in addition to empirical theories of actual behavior. As a normative guide to one’s actual behavior, evidential or causal decision-theoretic equations allow one to identify outcomes with maximal utility values. The choice that each person makes, however, will, of course, differ according to varying assignments of weight and probability values. Regarding these different choices, it remains a subject of considerable philosophical controversy whether individual subjects have the capacity to exercise free will with respect to the assignment of probabilities, or whether instead the assignment is in some way constrained. A version of this question is given a precise form in Richard Jeffrey’s assumption that free will is necessary for Newcomb’s paradox to count as a decision problem. This paper will argue, against Jeffrey, that decision theory does not require the assumption of libertarian freedom. One of the hallmarks of decision-making is its application across a wide variety of contexts; the implications of a background assumption of free will is similarly varied. One constant across the contexts of decision is that there are always at least two levels of choice for a given agent, depending on the degree of prior constraint. Within the context of Newcomb’s problem, when the predictor is attempting to guess the choice the agent will make, he or she is analyzing the determined aspects of the agent such as past characteristics, experiences, and knowledge. On the other hand, as David Lewis’ backtracking argument concerning the relationship between past and present events brings to light, there are similarly varied ways in which the past can actually be dependent on the present. One implication of this argument is that even in deterministic settings, an agent can have more free will than it may seem. This paper will thus argue against the view that a stable background assumption of free will or determinism in decision theory is necessary, arguing instead for a compatibilist decision theory yielding a novel treatment of Newcomb’s problem.

Keywords: decision theory, compatibilism, free will, Newcomb’s problem

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Fanqiang Kong, Jindong Zhang, Daiyin Zhu

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Space-time adaptive processing (STAP) techniques have been motivated as a key enabling technology for advanced airborne radar applications. In this paper, the notion of cognitive radar is extended to STAP technique, and cognitive STAP is discussed. The principle for improving signal-to-clutter ratio (SCNR) based on slow-time coding is given, and the corresponding optimization algorithm based on cyclic and power-like algorithms is presented. Numerical examples show the effectiveness of the proposed method.

Keywords: space-time adaptive processing (STAP), airborne radar, signal-to-clutter ratio, slow-time coding

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Authors: Mahmoud Mabrok, Channarong Rodkhum

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Flavobacterium columanre is one of the devastating pathogen that causes noticeable economic losses in freshwater cultured fish. Like other filamentous bacteria, F. columanre tends to aggregate and fluctuate to all kind of media, thus revealing obstacles in recognition of its colonies. Since the molecular typing is the only fundamental tool for rapid and precise detection of this pathgen. The present study developed a species-specific PCR assay based on cslA unique gene of F. columnare. The cslA gene sequences of 13 F. columnare, strains retrieved from gene bank database, were aligned to identify a conserved homologous segment prior to primers design. The new primers yielded amplicons of 287 bp from F. columnare strains but not from relevant or other pathogens, unlike to other published set that showed no specificity and cross-reactivity with F. indicum. The primers were sensitive and detected as few as 7 CFUs of bacteria and 3 pg of gDNA template. The sensitivity was reduced ten times when using tissue samples. These primers precisely defined all field isolates in a double-blind study, proposing their applicable use for field detection.

Keywords: Columnaris infection, cslA gene, Flavobacterium columnare, PCR

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Authors: Aditi Kashi

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While educational technologies have taken great strides, especially in Musical Instrument Digital Interface (MIDI) composition, teachers across the world are still adjusting to incorporate such technology into their curricula. While using MIDI in the classroom has become more common, limited class time and a strong focus on performance have made composition a lesser priority. The balance between music theory, performance time, and composition learning is delicate and difficult to maintain for many music educators. This makes including MIDI in the classroom. To address this issue, this paper aims to outline a general conceptual framework centered around a key element of music theory to integrate MIDI composition into the music classroom to not only introduce students to digital composition but also enhance their understanding of music theory and its applicability.

Keywords: educational framework, education technology, MIDI, music education

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Authors: Seoi Lee, Dongjoo Chin, Heewon Kim

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Gamification refers to a technique that applies game elements to non-gaming elements, such as education and exercise, to make people more engaged in these behaviors. The purpose of this study was to identify effective elements in gamification for changing human behaviors. In order to accomplish this purpose, a survey based on learning theory was developed, especially for assessing antecedents and consequences of behaviors, and 8 popular and 8 unpopular games were selected for comparison. A total of 407 adult males and females were recruited via crowdsourcing Internet marketplace and completed the survey, which consisted of 19 questions for antecedent and 14 questions for consequences. Results showed no significant differences in consequence questions between popular and unpopular games. For antecedent questions, popular games are superior to unpopular games in character customization, play type selection, a sense of belonging, patch update cycle, and influence or dominance. This study is significant in that it reveals the elements of gamification based on learning theory. Future studies need to empirically validate whether these factors affect behavioral change.

Keywords: gamification, learning theory, antecedent, consequence, behavior change, behaviorism

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Authors: Francielle A. Vargas, Thiago A. S. Pardo

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According to the standard philosophical definition, lying is saying something that you believe to be false with the intent to deceive. For deception detection, the FBI trains its agents in a technique named statement analysis, which attempts to detect deception based on parts of speech (i.e., linguistics style). This method is employed in interrogations, where the suspects are first asked to make a written statement. In this poster, we model false statements using linguistics style. In order to achieve this, we methodically analyze linguistic features in a corpus of fake news in the Portuguese language. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Keywords: deception detection, linguistics style, computational linguistics, natural language processing

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Authors: Mohammad Abdullah-Al-Wadud

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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Gurinder Singh, Kulbir Singh

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Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques.

Keywords: Computer Forensics, Multimedia Forensics, Image Ballistics, Camera Source Identification, Forgery Detection

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Authors: Babak Safaei, Ahmad Ghanbari, Arash Rahmani

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In the present study, the free vibration characteristics of double-walled carbon nanotubes (DWCNTs) are investigated. The small-scale effects are taken into account using the Eringen’s nonlocal elasticity theory. The nonlocal elasticity equations are implemented into the different classical beam theories namely as Euler-Bernoulli beam theory (EBT), Timoshenko beam theory (TBT), Reddy beam theory (RBT), and Levinson beam theory (LBT) to analyze the free vibrations of DWCNTs in which each wall of the nanotubes is considered as individual beam with van der Waals interaction forces. Generalized differential quadrature (GDQ) method is utilized to discretize the governing differential equations of each nonlocal beam model along with four commonly used boundary conditions. Then molecular dynamics (MD) simulation is performed for a series of armchair and zigzag DWCNTs with different aspect ratios and boundary conditions, the results of which are matched with those of nonlocal beam models to extract the appropriate values of the nonlocal parameter corresponding to each type of chirality, nonlocal beam model and boundary condition. It is found that the present nonlocal beam models with their proposed correct values of nonlocal parameter have good capability to predict the vibrational behavior of DWCNTs, especially for higher aspect ratios.

Keywords: double-walled carbon nanotubes, nonlocal continuum elasticity, free vibrations, molecular dynamics simulation, generalized differential quadrature method

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Authors: Abdelmalek Ouannou, Adil Brouri, Laila Kadi, Tarik

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Switched reluctance motor (SRM) has a major interest in a large domain as in electric vehicle driving because of its wide range of speed operation, high performances, low cost, and robustness to run under degraded conditions. The purpose of the paper is to develop a new analytical approach for modeling SRM parameters. Then, an identification scheme is proposed to obtain the SRM parameters. Since the SRM is featured by a highly nonlinear behavior, modeling these devices is difficult. Then, it is convenient to develop an accurate model describing the SRM. Furthermore, it is always operated in the magnetically saturated mode to maximize the energy transfer. Accordingly, it is shown that the SRM can be accurately described by a generalized polynomial Hammerstein model, i.e., the parallel connection of several Hammerstein models having polynomial nonlinearity. Presently an analytical identification method is developed using a chirp excitation signal. Afterward, the parameters of the obtained model have been determined using Finite Element Method analysis. Finally, in order to show the effectiveness of the proposed method, a comparison between the true and estimate models has been performed. The obtained results show that the output responses are very close.

Keywords: switched reluctance motor, swept-sine signal, generalized Hammerstein model, nonlinear system

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Authors: Shivani Jha, Klaus Baier, Rafiq Azzam, Ramakar Jha

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In India, generally people migrate from rural area to the urban area for better infra-structural facilities, high standard of living, good job opportunities and advanced transport/communication availability. In fact, unplanned urban development due to migration of people causes seriou damage to the land use, water pollution and available water resources. In the present work, an attempt has been made to use satellite data of different years for urban change detection of Chennai metropolitan city along with pattern analysis to generate future scenario of urban development using buffer zoning in GIS environment. In the analysis, SRTM (30m) elevation data and IRS-1C satellite data for the years 1990, 2000, and 2014, are used. The flow accumulation, aspect, flow direction and slope maps developed using SRTM 30 m data are very useful for finding suitable urban locations for industrial setup and urban settlements. Normalized difference vegetation index (NDVI) and Principal Component Analysis (PCA) have been used in ERDAS imagine software for change detection in land use of Chennai metropolitan city. It has been observed that the urban area has increased exponentially in Chennai metropolitan city with significant decrease in agriculture and barren lands. However, the water bodies located in the study regions are protected and being used as freshwater for drinking purposes. Using buffer zone analysis in GIS environment, it has been observed that the development has taken place in south west direction significantly and will do so in future.

Keywords: urban change, satellite data, the Chennai metropolis, change detection

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: Jing Niu, Jun X. Wang

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Arterial stiffness predicts mortality and morbidity, independently of other cardiovascular risk factors. And it is a major risk factor for age-related morbidity and mortality. The non-invasive industry gold standard measurement system of arterial stiffness utilizes pulse wave velocity method. However, the desktop device is expensive and requires trained professional to operate. The main objective of this research is the proof of concept of the proposed non-invasive method which uses measurement of magnetic flux disturbance and electrocardiogram (ECG) signal for measuring arterial stiffness. The method could enable accurate and easy self-assessment of arterial stiffness at home, and to help doctors in research, diagnostic and prescription in hospitals and clinics. A platform for assessing arterial stiffness through acquisition and analysis of radial artery pulse waveform and ECG signal has been developed based on the proposed method. Radial artery pulse waveform is acquired using the magnetic based sensing technology, while ECG signal is acquired using two dry contact single arm ECG electrodes. The measurement only requires the participant to wear a wrist strap and an arm band. Participants were recruited for data collection using both the developed platform and the industry gold standard system. The results from both systems underwent correlation assessment analysis. A strong positive correlation between the results of the two systems is observed. This study presents the possibility of developing an accurate, easy to use and affordable measurement device for arterial stiffness assessment.

Keywords: arterial stiffness, electrocardiogram, pulse wave velocity, Magnetic Flux Disturbance

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Authors: Eveline Muika Kabongo, Peter Delobelle, Ferdinand Mukumbang, Edward Nicol

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Introduction: Studies have been conducted to establish the effect of the MomConnect program in South Africa, but these studies did not focus on the stakeholders' and implementers' perspectives and the underlying program theory of the MomConnect initiative program. We strived to obtain stakeholders’ perspectives and assumptions on the MomConnect program and develop an initial program theory (IPT) of how the MomConnect initiative was expected to work. Methods: A realist-informed explanatory design used. The interviewer was performed with 10 key informants selected purposively among MomConnect key informants at the a national level of NDoH South Africa. The interview was done via zoom and lasted for 30 to 60 minutes. Introduction and abduction inferencing approaches were applied. The deductive and inductive approaches were performed during the analysis. ICAMO hereustic framework was used to analysed the data in order to get key informants expectations on how the MomConnect will work or not. Results: We developed three folk’s theories illustrating how the key informants’ expected the MomConnect to work. These theories showed that the MomConnect intended to provide users with health information and education that will empower and motivate them with knowledge which will allow the improvement of health services delivery among HCPs and improvement of the uptake of MCH services among pregnant women and mothers and decrease the rate of maternal and child mortality in the country. The lack of an updated mechanism to link women to the outcome was an issue. Another problem enlightened was the introduction of the WhatsApp program instead of SMS messaging, which was free of charge to women. Conclusion: The Folk’s theory developed from this study provided an insight into how the MomConnect was expected to work and what did not work. The folk’s theory will be merged with information from candidate theories on synthesis review and document review to develop our initial program theory of the MomConnect initiative.

Keywords: mHealth, MomConnect program, realist evaluation, maternal and child health, maternal and child health services, introduction, theory-driven

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Authors: Behrooz Nikahval, Mohammad Saeed Ahrari-Khafi, Sakineh Behroozpoor, Saeed Nazifi

Abstract:

Osteoarthritis (OA) is a progressive and degenerative condition of the articular cartilage and other joints’ structures. It is essential to diagnose this condition as early as possible. The present research was performed to measure the Osteoprotegrin (OPG) and Tumor Necrosis Factor α (TNF-α) in synovial fluid and blood serum of dogs with surgically transected cruciate ligament as a model of OA, to evaluate if measuring of these parameters can be used as a way of early diagnosis of OA. In the present study, four mature, clinically healthy dogs were selected to investigate the effect of experimental OA, on OPG and TNF-α as a way of early detection of OA. OPG and TNF-α were measured in synovial fluid and blood serum on days 0, 14, 28, 90 and 180 after surgical transaction of cranial cruciate ligament in one stifle joint. Statistical analysis of the results showed that there was a significant increase in TNF-α in both synovial fluid and blood serum. OPG showed a decrease two weeks after OA induction. However, it fluctuated afterward. In conclusion, TNF-α could be used in both synovial fluid and blood serum as a way of early detection of OA; however, further research still needs to be conducted on OPG values in OA detection.

Keywords: osteoarthritis, osteoprotegrin, tumor necrosis factor α, synovial fluid, serum, dog

Procedia PDF Downloads 307

Authors: Saman Ghaffarian, Ilgin Gökaşar

Abstract:

This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

Procedia PDF Downloads 367

Authors: Shashank Gahlaut, Chandrashekhar Sharan, J. P. Singh

Abstract:

Increasing incidence of antibiotic-resistant bacteria is a big concern for the treatment of pathogenic diseases. The effect of treatment of patients with antibiotics often leads to the evolution of antibiotic resistance in the pathogens. The detection of antibiotic or antimicrobial resistant bacteria (microbes) is quite essential as it is becoming one of the big threats globally. Here we propose a novel technique to tackle this problem. We are taking a step forward to prevent the infections and diseases due to drug resistant microbes. This detection is based on some unique features of silver (a noble metal) nanorods (AgNRs) which are fabricated by a physical deposition method called thermal glancing angle deposition (GLAD). Silver nanorods are found to be highly sensitive and selective for hydrogen sulfide (H2S) gas. Color and water wetting (contact angle) of AgNRs are two parameters what are effected in the presence of this gas. H₂S is one of the major gaseous products evolved in the bacterial metabolic process. It is also known as gasotransmitter that transmits some biological singles in living systems. Nitric Oxide (NO) and Carbon mono oxide (CO) are two another members of this family. Orlowski (1895) observed the emission of H₂S by the bacteria for the first time. Most of the microorganism produce these gases. Here we are focusing on H₂S gas evolution to determine live/dead and antibiotic-resistant bacteria. AgNRs array has been used for the detection of H₂S from micro-organisms. A mobile app is also developed to make it easy, portable, user-friendly, and cost-effective.

Keywords: antibiotic resistance, hydrogen sulfide, live and dead bacteria, mobile app

Procedia PDF Downloads 131

Authors: Divya Pradip Roy, Md. Zahirul Alam Chowdhury

Abstract:

Electromyography (EMG) sensors are generally used to record the electrical activity produced by skeletal muscles. The conventional EMG sensors available in the market are expensive. This research suggests a low cost EMG sensor design which can be built with simple devices within our reach. In this research, one instrumentation amplifier, two high pass filters, two low pass filters and an inverting amplifier is connected sequentially. The output from the circuit exhibits electrical potential generated by the muscle cells when they are neurologically activated. This electromyography signal is used to control prosthetic devices, identifying neuromuscular diseases and for various other purposes.

Keywords: EMG, high pass filter, instrumentation amplifier, inverting amplifier, low pass filter, neuromuscular

Procedia PDF Downloads 158