Search results for: automatic classification of tremor types

Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

Procedia PDF Downloads 207

Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

Procedia PDF Downloads 198

Authors: Veselin Konstantinov Hristov

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In recent years, the lease contract has become more and more applicable and occupies a key place in commercial relations and business. In Bulgaria, the legal regulation of the leasing contract is relatively new and imperfectly developed. There are many legal loopholes and it is they that determine the need for a comparative legal analysis. The purpose of the study is to analyze the various European legislations regarding the leasing contract and to find effective solutions for the legal system of Bulgaria. First of all, are examined the concept of the leasing contract, which originated in the United States of America around the 1950s and spread in Europe, and the etymology of the term "leasing". After that, the main types of lease contracts – financial and operational – are examined and analyzed in detail. Their features and characteristics were studied, as well as a comparative analysis was made between them. Next, in the research, a comparative-legal analysis of the leasing contract in different European countries was made in terms of its development and distribution, as well as its legal characteristics. The mechanism of action and functioning of the leasing contract in several European countries is analyzed. Conclusions are made regarding the legal framework under which the lease contract is most effective. Types of leasing contracts specific only to certain European countries and their advantages are examined. In conclusion, recommendations are made to improve the legal framework of the leasing contract in Bulgaria.

Keywords: alternative financing, leasing contract, financing instruments, innovation

Procedia PDF Downloads 76

Authors: M. Abd El Hamid Ezzat

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Two field trials were conducted to study the effect of plant densities i.e., 190, 222, 266, 330 and 440 10³ plants ha⁻¹ on morphological characters, physiological and yield attributes of two faba bean types viz. determinate (FLIP-87 -117 strain) and indeterminate (c.v. Giza-461). The results showed that the indeterminate plants significantly surpassed the determinate plants in plant height at 75 and 90 days from sowing, number of leaves at all growth stages and dry matter accumulation at 45 and 90 days from sowing. Determinate plants possessed greater number of side branches than that of the indeterminate plants, but it was only significant at 90 days from sowing. Greater number of flowers were produced by the indeterminate plants than that of the determinate plants at 75 and 90 days from sowing, and although shedding was obvious in both types, it was greater in the determinate plants as compared with the indeterminate one at 90 days from sowing. Increasing plant density resulted in reductions in number of leaves, branches flowers and dry matter accumulation per plant of both faba bean types. However, plant height criteria took a reversible magnitude. Moreover, under all rates of plant densities the indeterminate type plants surpassed the determinate plants in all growth characters studied except for number of branches per plant at 90 days from sowing. The indeterminate plant leaves significantly contained greater concentrations of photosynthetic pigments i.e., chl. a, b and carotenoids than those found in the determinate plant leaves. Also, the data showed significant reduction in photosynthetic pigments concentration as planting density increases. Light extinction coefficient (K) values reached their maximum level at 60 days from sowing, then it declined sharply at 75 days from sowing. The data showed that the illumination inside the determinate faba bean canopies was better than the indeterminate plants. (K) values tended to increase as planting density increases, meanwhile, significant interactions were reported between faba bean type as planting density on (K) at all growth stages. Both of determinate and indeterminate faba bean plant leaves reached their maximum expansion at 75 days from sowing reflecting the highest LAI values, then their declined in the subsequent growth stage. The indeterminate faba bean plants significantly surpassed the determinate plants in LAI up to 75 days from sowing. Growth analysis showed that NAR, RGR and CGR reached their maximum rates at (60-75 days growth stage). Faba bean types did not differ significantly in NAR at the early growth stage. The indeterminate plants were able to grow faster with significant CGR values than the determinate plants. The indeterminate faba bean plants surpassed the determinate ones in number of seeds/pod and per plant, 100-seed weight, seed yield per plant and per hectare at all rates of plant density. Seed yield increased with increasing plant densities of both types. The highest seed yield was attained for both types 440 103 plants ha⁻¹.

Keywords: determinate, indeterminate faba bean, Physiological attributes, yield attributes

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

Procedia PDF Downloads 138

Authors: Zuraidasahana Zulkarnain, Mohd Shafry Mohd Rahim, Nor Anita Fairos Ismail, Mohd Azhar M. Arsad

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Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.

Keywords: biometrics, euclidean classifier, features extraction, offline signature verification, voting-based classifier

Procedia PDF Downloads 370

Authors: Rong-Yang Lai, Jia-Yu Wu, Chih-Han Chang, Yung-Chung Chen

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The dental bridge is one of the clinical methods of the treatment for missing teeth. The dental bridge is generally designed for two layers, containing the inner layer of the framework(zirconia) and the outer layer of the porcelain-fused to framework restorations. The design of a conventional bridge is generally based on the antagonist tooth profile so that the framework evenly indented by an equal thickness from outer contour. All-ceramic dental bridge made of zirconia have well demonstrated remarkable potential to withstand a higher physiological occlusal load in posterior region, but it was found that there is still the risk of all-ceramic bridge failure in five years. Thus, how to reduce the incidence of failure is still a problem to be solved. Therefore, the objective of this study is to develop mechanical designs for all-ceramic dental bridges framework by reducing the stress and enhancing fracture resistance under given loading conditions by finite element method. In this study, dental design software is used to design dental bridge based on tooth CT images. After building model, Bi-directional Evolutionary Structural Optimization (BESO) Method algorithm implemented in finite element software was employed to analyze results of finite element software and determine the distribution of the materials in dental bridge; BESO searches the optimum distribution of two different materials, namely porcelain and zirconia. According to the previous calculation of the stress value of each element, when the element stress value is higher than the threshold value, the element would be replaced by the framework material; besides, the difference of maximum stress peak value is less than 0.1%, calculation is complete. After completing the design of dental bridge, the stress distribution of the whole structure is changed. BESO reduces the peak values of principle stress of 10% in outer-layer porcelain and avoids producing tensile stress failure.

Keywords: dental bridge, finite element analysis, framework, automatic program

Procedia PDF Downloads 274

Authors: Reza Hedayati, Meysam Jahanbakhshi

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Fracture Mechanics is used to predict debonding propagation in adhesive joint between aluminum and composite plates. Three types of loadings and two types of glass-epoxy composite sequences: [0/90]2s and [0/45/-45/90]s are considered for the composite plate and their results are compared. It was seen that generally the cases with stacking sequence of [0/45/-45/90]s have much shorter lives than cases with [0/90]2s. It was also seen that in cases with λ=0 the ends of the debonding front propagates forward more than its middle, while in cases with λ=0.5 or λ=1 it is vice versa. Moreover, regardless of value of λ, the difference between the debonding propagations of the ends and the middle of the debonding front is very close in cases λ=0.5 and λ=1. Another main conclusion was the non-dimensionalized debonding front profile is almost independent of sequence type or the applied load value.

Keywords: fatigue, debonding, Paris law, APDL, adhesive

Procedia PDF Downloads 354

Authors: Amira Kaddour

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To curb the problem of liquidity in times of financial crises, two cases arise; the Bailout or Bailin, two opposite choices that elicit the analysis of their effect on moral hazard. This paper attempts to empirically analyze the effect of these two types of events on the behavior of investors. For this end, we use the Emerging Market Bonds Index (EMBI-JP Morgan), and its excess of return, to detect the change in the risk premia through a Markov switching model. The results showed the transition to two types of regime and an effect on moral hazard; Bailout is an incentive of moral hazard, Bailin effectiveness remains subject of credibility.

Keywords: Bailout, Bailin, Moral hazard, financial crisis, Markov switching

Procedia PDF Downloads 455

Authors: Mansoor Aslamzai, Delaqa Del, Sayed Azam Sajid

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Introduction: Though moral disorders have a high burden, there is no separate topic regarding this problem in the International Classification of Diseases (ICD). Along with the modification of WHO ICD-11, spirituality can prevent the rapid progress of such derangement as well. Objective: This study evaluated the effects of bad moral characters on health, as well as carried out the role of spirituality in the improvement of immorality. Method: This narrative article review was accomplished in 2020-2021 and the articles were searched through the Web of Science, PubMed, BMC, and Google scholar. Results: Based on the current review, most experimental and observational studies revealed significant negative effects of unwell moral characters on the overall aspects of health and well-being. Nowadays, a lot of studies established the positive role of spirituality in the improvement of health and moral disorder. The studies concluded, facilities must be available within schools, universities, and communities for everyone to learn the knowledge of spirituality and improve their unwell moral character world. Conclusion: Considering the negative relationship between unwell moral characters and well-being, the current study proposes the addition of moral disorder as a separate topic in the WHO International Classification of Diseases. Based on this literature review, spirituality will improve moral disorder and establish excellent moral traits.

Keywords: bad moral characters, effect, health, spirituality and well-being

Procedia PDF Downloads 178

Authors: Kei Okubo, Yosuke Kurihara, Takashi Kaburagi, Kajiro Watanabe

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Many of the ever-growing elderly population require exercise, such as running, for health management. One important element of a runner’s training is the choice of shoes for exercise; shoes are important because they provide the interface between the feet and road. When we purchase shoes, we may instinctively choose a pair after trying on many different pairs of shoes. Selecting the shoes instinctively may work, but it does not guarantee a suitable fit for running activities. Therefore, if we could select suitable shoes for each runner from the viewpoint of brain activities, it would be helpful for validating shoe selection. In this paper, we describe how brain activities show different characteristics during particular task, corresponding to different properties of shoes. Using five subjects, we performed a verification experiment, applying weight, softness, and flexibility as shoe properties. In order to affect the shoe property’s differences to the brain, subjects run for ten min. Before and after running, subjects conducted a paced auditory serial addition task (PASAT) as the particular task; and the subjects’ brain activities during the PASAT are evaluated based on oxyhemoglobin and deoxyhemoglobin relative concentration changes, measured by near-infrared spectroscopy (NIRS). When the brain works actively, oxihemoglobin and deoxyhemoglobin concentration drastically changes; therefore, we calculate the maximum values of concentration changes. In order to normalize relative concentration changes after running, the maximum value are divided by before running maximum value as evaluation parameters. The classification of the groups of shoes is expressed on a self-organizing map (SOM). As a result, deoxyhemoglobin can make clusters for two of the three types of shoes.

Keywords: brain activities, NIRS, PASAT, running shoes

Procedia PDF Downloads 360

Authors: Giovanni Menichini, Salvatore Giacomo Morano, Gloria Terenzi, Luca Salvatori, Maurizio Orlando

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The “Guidelines for Risk Classification, Safety Assessment, and Structural Health Monitoring of Existing Bridges” were recently approved by the Italian Government to define technical standards for managing the national network of existing bridges. These guidelines provide a framework for risk mitigation and safety assessment of bridges, which are essential elements of the built environment and form the basis for the operation of transport systems. Within the guideline framework, a workflow based on three main points was proposed: (1) risk-based, i.e., based on typical parameters of hazard, vulnerability, and exposure; (2) multi-level, i.e., including six assessment levels of increasing complexity; and (3) multirisk, i.e., assessing structural/foundational, seismic, hydrological, and landslide risks. The paper focuses on applying the Italian Guidelines to specific case studies, aiming to identify the parameters that predominantly influence the determination of the “class of attention”. The significance of each parameter is determined via sensitivity analysis. Additionally, recommendations for enhancing the process of assigning the class of attention are proposed.

Keywords: bridge safety assessment, Italian guidelines implementation, risk classification, structural health monitoring

Procedia PDF Downloads 45

Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

Procedia PDF Downloads 407

Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

Procedia PDF Downloads 145

Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

Procedia PDF Downloads 107

Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

Procedia PDF Downloads 145

Authors: Teodora Vukovic, Christoph Hottiger, Noah Bubenhofer

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The development of VIAN-DH aims at bridging two linguistic approaches: conversation analysis/interactional linguistics (IL), so far a dominantly qualitative field, and computational/corpus linguistics and its quantitative and automated methods. Contemporary IL investigates the systematic organization of conversations and interactions composed of speech, gaze, gestures, and body positioning, among others. These highly integrated multimodal behaviour is analysed based on video data aimed at uncovering so called “multimodal gestalts”, patterns of linguistic and embodied conduct that reoccur in specific sequential positions employed for specific purposes. Multimodal analyses (and other disciplines using videos) are so far dependent on time and resource intensive processes of manual transcription of each component from video materials. Automating these tasks requires advanced programming skills, which is often not in the scope of IL. Moreover, the use of different tools makes the integration and analysis of different formats challenging. Consequently, IL research often deals with relatively small samples of annotated data which are suitable for qualitative analysis but not enough for making generalized empirical claims derived quantitatively. VIAN-DH aims to create a workspace where many annotation layers required for the multimodal analysis of videos can be created, processed, and correlated in one platform. VIAN-DH will provide a graphical interface that operates state-of-the-art tools for automating parts of the data processing. The integration of tools that already exist in computational linguistics and computer vision, facilitates data processing for researchers lacking programming skills, speeds up the overall research process, and enables the processing of large amounts of data. The main features to be introduced are automatic speech recognition for the transcription of language, automatic image recognition for extraction of gestures and other visual cues, as well as grammatical annotation for adding morphological and syntactic information to the verbal content. In the ongoing instance of VIAN-DH, we focus on gesture extraction (pointing gestures, in particular), making use of existing models created for sign language and adapting them for this specific purpose. In order to view and search the data, VIAN-DH will provide a unified format and enable the import of the main existing formats of annotated video data and the export to other formats used in the field, while integrating different data source formats in a way that they can be combined in research. VIAN-DH will adapt querying methods from corpus linguistics to enable parallel search of many annotation levels, combining token-level and chronological search for various types of data. VIAN-DH strives to bring crucial and potentially revolutionary innovation to the field of IL, (that can also extend to other fields using video materials). It will allow the processing of large amounts of data automatically and, the implementation of quantitative analyses, combining it with the qualitative approach. It will facilitate the investigation of correlations between linguistic patterns (lexical or grammatical) with conversational aspects (turn-taking or gestures). Users will be able to automatically transcribe and annotate visual, spoken and grammatical information from videos, and to correlate those different levels and perform queries and analyses.

Keywords: multimodal analysis, corpus linguistics, computational linguistics, image recognition, speech recognition

Procedia PDF Downloads 98

Authors: M. Ndeley

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Now our days, Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimize the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand; and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.

Keywords: flow shop scheduling, uncertainty, rescheduling, stability

Procedia PDF Downloads 435

Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

Procedia PDF Downloads 342

Authors: Seyedeh Mandan Kazzazi, Elena Kranioti

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In the field of human osteology, sex estimation is an important step in developing biological profile. There are a number of methods that can be used to estimate the sex of human remains varying from visual assessments to metric analysis of sexually dimorphic traits. Teeth are one of the most durable physical elements in human body that can be used for this purpose. The present study investigated the utility of cervical measurements for sex estimation through discriminant analysis. The permanent molar teeth of 75 skeletons (28 females and 52 males) from Hasanlu site in North-western Iran were studied. Cervical mesiodistal and buccolingual measurements were taken from both maxillary and mandibular first and second molars. Discriminant analysis was used to evaluate the accuracy of each diameter in assessing sex. The results showed that males had statistically larger teeth than females for maxillary and mandibular molars and both measurements (P < 0.05). The range of classification rate was from (75.7% to 85.5%) for the original and cross-validated data. The most dimorphic teeth were maxillary and mandibular second molars providing 85.5% and 83.3% correct classification rate respectively. The data generated from the present study suggested that cervical mesiodistal and buccolingual measurements of the molar teeth can be useful and reliable for sex estimation in Iranian archaeological populations.

Keywords: cervical measurements, Hasanlu, premolars, sex estimation

Procedia PDF Downloads 321

Authors: Aniket Alam, Sravya Gurram

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This paper proposes a categorization of sport that is based on the system of rules that a sport must adhere to. We focus on these systems of rules to examine how a winner is produced in different sports. The rules of a sport dictate the game play and the direction it takes. We propose to break down the game play into events. At this junction, we observe two kinds of events that constitute the game play of a sport –ones that follow sequential logic and ones that do not. Our focus is pertained to sports that are comprised of sequential events. To examine these events further, to understand how a winner emerges, we take the help of finite-state automaton from the theory of computation (Automata theory). We showcase how sequential sports are eligible to be represented as finite state machines. We depict these finite state machines as state diagrams. We examine these state diagrams to observe how a team/player reaches the final states of the sport, with a special focus on one final state –the final state which determines the winner. This exercise has been carried out for the following sports: Hurdles, Track, Shot Put, Long Jump, Bowling, Badminton, Pacman and Weightlifting (Snatch). Based on our observations of how this final state of winning is achieved, we propose a categorization of sports.

Keywords: sport classification, sport modelling, ontology, automata theory

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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

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

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

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Authors: Katerina Koliou, Scott Parr, Evangelos Kaisar

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The surface transportation of freight is particularly vulnerable to storm and hurricane disasters, while at the same time, it is the primary transportation mode for delivering medical supplies, fuel, water, and other essential goods. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The research investigation used Florida's statewide continuous-count station traffic volumes, where then compared between years, to identify locations where traffic was moving differently during the evacuation. The data was then used to identify days on which traffic was significantly different between years. While the literature on auto-based evacuations is extensive, the consideration of freight travel is lacking. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The goal of this research was to investigate the movement of vehicles by classification, with an emphasis on freight during two major evacuation events: hurricanes Irma (2017) and Michael (2018). The methodology of the research was divided into three phases: data collection and management, spatial analysis, and temporal comparisons. Data collection and management obtained continuous-co station data from the state of Florida for both 2017 and 2018 by vehicle classification. The data was then processed into a manageable format. The second phase used geographic information systems (GIS) to display where and when traffic varied across the state. The third and final phase was a quantitative investigation into which vehicle classifications were statistically different and on which dates statewide. This phase used a two-sample, two-tailed t-test to compare sensor volume by classification on similar days between years. Overall, increases in freight movement between years prevented a more precise paired analysis. This research sought to identify where and when different classes of vehicles were traveling leading up to hurricane landfall and post-storm reentry. Of the more significant findings, the research results showed that commercial-use vehicles may have underutilized rest areas during the evacuation, or perhaps these rest areas were closed. This may suggest that truckers are driving longer distances and possibly longer hours before hurricanes. Another significant finding of this research was that changes in traffic patterns for commercial-use vehicles occurred earlier and lasted longer than changes for personal-use vehicles. This finding suggests that commercial vehicles are perhaps evacuating in a fashion different from personal use vehicles. This paper may serve as the foundation for future research into commercial travel during evacuations and explore additional factors that may influence freight movements during evacuations.

Keywords: evacuation, freight, travel time, evacuation

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Authors: Taikchan Lildar, Ayesha Samad, Suraj Sookhu

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Atrial fibrillation with rapid ventricular response results in the loss of atrial kick and shortened ventricular filling time, which often leads to decompensated heart failure. Pharmacologic rhythm control is the treatment of choice, and patients frequently benefit from the restoration of sinus rhythm. When pharmacologic treatment is unsuccessful or a patient declines hemodynamically, direct cardioversion is the treatment of choice. Torsades de pointes or “twisting of the points'' in French, is a rare but under-appreciated risk of cardioversion therapy and accounts for a significant number of sudden cardiac death each year. A 61-year-old female with no significant past medical history presented to the Emergency Department with worsening dyspnea. An electrocardiogram showed atrial fibrillation with rapid ventricular response, and a chest X-ray was significant for bilateral pulmonary vascular congestion. Full-dose anticoagulation and diuresis were initiated with moderate improvement in symptoms. A transthoracic echocardiogram revealed biventricular systolic dysfunction with a left ventricular ejection fraction of 30%. After consultation with an electrophysiologist, the consensus was to proceed with the restoration of sinus rhythm, which would likely improve the patient’s heart failure symptoms and possibly the ejection fraction. A transesophageal echocardiogram was negative for left atrial appendage thrombus; the patient was treated with a loading dose of amiodarone and underwent successful direct current cardioversion with 200 Joules. The patient was placed on telemetry monitoring for 24 hours and was noted to have frequent premature ventricular contractions with subsequent degeneration to torsades de pointes. The patient was found unresponsive and pulseless; cardiopulmonary resuscitation was initiated with cardioversion, and return of spontaneous circulation was achieved after four minutes to normal sinus rhythm. Post-cardiac arrest electrocardiogram showed sinus bradycardia with heart-rate corrected QT interval of 592 milliseconds. The patient continued to have frequent premature ventricular contractions and required two additional cardioversions to achieve a return of spontaneous circulation with intravenous magnesium and lidocaine. An automatic implantable cardioverter-defibrillator was subsequently implanted for secondary prevention of sudden cardiac death. The backup pacing rate of the automatic implantable cardioverter-defibrillator was set higher than usual in an attempt to prevent premature ventricular contractions-induced torsades de pointes. The patient did not have any further ventricular arrhythmias after implantation of the automatic implantable cardioverter-defibrillator. Overdrive pacing is a method utilized to treat premature ventricular contractions-induced torsades de pointes by preventing a patient’s susceptibility to R on T-wave-induced ventricular arrhythmias. Pacing at a rate of 90 beats per minute succeeded in controlling the arrhythmia without the need for traumatic cardiac defibrillation. In our patient, conversion of atrial fibrillation with rapid ventricular response to normal sinus rhythm resulted in a slower heart rate and an increased probability of premature ventricular contraction occurring on the T-wave and ensuing ventricular arrhythmia. This case highlights direct current cardioversion for atrial fibrillation with rapid ventricular response resulting in persistent ventricular arrhythmia requiring an automatic implantable cardioverter-defibrillator placement with overdrive pacing to prevent a recurrence.

Keywords: refractory atrial fibrillation, atrial fibrillation, overdrive pacing, torsades de pointes

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Authors: Khashayar Jafari, Mostafa Jafarian Abyaneh, Vahab Toufigh

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Polymer concrete (PC) is a distinct concrete with superior characteristics in comparison to ordinary cement concrete. It has become well-known for its applications in thin overlays, floors and precast components. In this investigation, the mechanical properties of PC with different epoxy resin contents, ordinary cement concrete (OCC) and lightweight concrete (LC) have been studied under uniaxial compression test. The study involves five types of concrete, with each type being tested four times. Their complete elastic-plastic behavior was compared with each other through the measurement of volumetric strain during the tests. According to the results, PC showed higher strength, ductility and energy absorption with respect to OCC and LC.

Keywords: polymer concrete, ordinary cement concrete, lightweight concrete, uniaxial compression test, volumetric strain

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Authors: Shantam Gupta

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The objective of this study is to address the critical need for automation in the domestic food processing sector and study its impact. Food is the one of the most basic physiological needs essential for the survival of a living being. Some of them have the capacity to prepare their own food (like most plants) and henceforth are designated as primary food producers; those who depend on these primary food producers for food form the primary consumers’ class (herbivores). Some of the organisms relying on the primary food are the secondary food consumers (carnivores). There is a third class of consumers called tertiary food consumers/apex food consumers that feed on both the primary and secondary food consumers. Humans form an essential part of the apex predators and are generally at the top of the food chain. But still further disintegration of the food habits of the modern human i.e. Homo sapiens, reveals that humans depend on other individuals for preparing their own food. The old notion of eating raw/brute food is long gone and food processing has become very trenchant in lives of modern human. This has led to an increase in dependence on other individuals for ‘processing’ the food before it can be actually consumed by the modern human. This has led to a further shift of humans in the classification of food chain of consumers. The effects of the shifts shall be systematically investigated in this paper. The processing of food has a direct impact on the economy of the individual (consumer). Also most individuals depend on other processing individuals for the preparation of food. This dependency leads to establishment of a vital link of dependency in the food web which when altered can adversely affect the food web and can have dire consequences on the health of the individual. This study investigates the challenges arising out due to this dependency and the impact of food processing on the economy of the individual. A comparison of Industrial food processing and processing at domestic platforms (households and restaurants) has been made to provide an idea about the present scenario of automation in the food processing sector. A lot of time and energy is also consumed while processing food at home for consumption. The high frequency of consumption of meals (greater than 2 times a day) makes it even more laborious. Through the medium of this study a pressing need for development of an automatic cooking machine is proposed with a mission to reduce the inter-dependency & human effort of individuals required for the preparation of food (by automation of the food preparation process) and make them more self-reliant The impact of development of this product has also further been profoundly discussed. Assumption used: The individuals those who process food also consume the food that they produce. (They are also termed as ‘independent’ or ‘self-reliant’ modern human beings.)

Keywords: automation, food processing, impact on economy, processing individual

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Authors: Arman Ameen, Mathias Cehlin

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The use of Building Energy Simulation (BES) software has increased in the last two decades, parallel to the development of increased computing power and easy to use software applications. This type of software is primarily used to simulate the energy use and the indoor environment for a building. The rapid development of these types of software has raised their level of user-friendliness, better parameter input options and the increased possibility of analysis, both for a single building component or an entire building. This, in turn, has led to many researchers utilizing BES software in their research in various degrees. The aim of this paper is to carry out a literature review concerning the use of the BES software IDA Indoor Climate and Energy (IDA ICE) in the scientific community. The focus of this paper will be specifically the use of the software for whole building energy simulation, number and types of articles and publications dates, the area of application, types of parameters used, the location of the studied building, type of building, type of analysis and solution methodology. Another aspect that is examined, which is of great interest, is the method of validations regarding the simulation results. The results show that there is an upgoing trend in the use of IDA ICE and that researchers use the software in their research in various degrees depending on case and aim of their research. The satisfactory level of validation of the simulations carried out in these articles varies depending on the type of article and type of analysis.

Keywords: building simulation, IDA ICE, literature review, validation

Procedia PDF Downloads 126