Search results for: encrypted traffic classification

Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

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With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: bipartite graph, one-mode projection, clustering, web proxy detection

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Authors: Hee-Jung Seo, Yunjung Lee, Areum Han, Heeyoung Park, Se-Hyuk Park

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The purpose of this study was to analyze the relationship between happiness and depression among people with disabilities and to analyze the social phenomenon of leisure activities among them to promote physical and leisure activities for people with disabilities. The research methods included analyzing differences in happiness according to depression classification. A total of 281 people with disabilities were analyzed using SPSS WIN Ver. 29.0. In addition, the SumTrend platform was used to analyze terms related to 'leisure activities for the disabled.' The findings can be summarized into two main points: First, there were significant differences in happiness according to depression classification. Second, there were 20 mentions before COVID-19, 34 mentions after COVID-19, and currently 43 mentions, with high positive rates observed in each period. Based on these results, the following conclusions were drawn: First, measures for people with disabilities include strengthening online resources and services, social distancing response policies, improving accessibility, and providing support and financial assistance. Second, measures for non-disabled individuals emphasize the need for education and information provision, promoting dialogue and interaction, ensuring accessibility, and promoting inclusive cultural awareness and attitude change.

Keywords: leisure activities, individuals with disabilities, COVID-19 pandemic, depression

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Authors: N. Othman, J. Ujang, M. N. Ismail, R. Noordin, B. H. Lim

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Amoebiasis is caused by the Entamoeba histolytica and still endemic in many parts of the tropical region, worldwide. Currently, there is no available vaccine against amoebiasis. Hence, there is an urgent need to develop a vaccine. The excretory secretory antigen (ESA) of E. histolytica is a suitable biomarker for the vaccine candidate since it can modulate the host immune response. Hence, the objective of this study is to identify the proteome of the ESA towards finding suitable biomarker for the vaccine candidate. The non-gel based and gel-based proteomics analyses were performed to identify proteins. Two kinds of mass spectrometry with different ionization systems were utilized i.e. LC-MS/MS (ESI) and MALDI-TOF/TOF. Then, the functional proteins classification analysis was performed using PANTHER software. Combination of the LC -MS/MS for the non-gel based and MALDI-TOF/TOF for the gel-based approaches identified a total of 273 proteins from the ESA. Both systems identified 29 similar proteins whereby 239 and 5 more proteins were identified by LC-MS/MS and MALDI-TOF/TOF, respectively. Functional classification analysis showed the majority of proteins involved in the metabolic process (24%), primary metabolic process (19%) and protein metabolic process (10%). Thus, this study has revealed the proteome the E. histolytica ESA and the identified proteins merit further investigations as a vaccine candidate.

Keywords: E. histolytica, ESA, proteomics, biomarker

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Authors: Kuei-Ling Sun, Emily Chia-Yu Su

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Allergy is a hypersensitive overreaction of the immune system to environmental stimuli, and a major health problem. These overreactions include rashes, sneezing, fever, food allergies, anaphylaxis, asthmatic, shock, or other abnormal conditions. Allergies can be caused by food, insect stings, pollen, animal wool, and other allergens. Their development of allergies is due to both genetic and environmental factors. Allergies involve immunoglobulin E antibodies, a part of the body’s immune system. Immunoglobulin E antibodies will bind to an allergen and then transfer to a receptor on mast cells or basophils triggering the release of inflammatory chemicals such as histamine. Based on the increasingly serious problem of environmental change, changes in lifestyle, air pollution problem, and other factors, in this study, we both collect allergens and non-allergens from several databases and use several machine learning methods for classification, including logistic regression (LR), stepwise regression, decision tree (DT) and neural networks (NN) to do the model comparison and determine the permutations of allergen amino acid’s sequence.

Keywords: allergy, classification, decision tree, logistic regression, machine learning

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Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

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Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

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Authors: Li Niu, Jihong Liang, Lichao Liu, Huidi Chen

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With the drawing of ancient palace painter, the layout of Beijing famous architect and the lens under photographers, a series of pictures which described whether emperors or ordinary people, whether gardens or Hutongs, whether historical events or life scenarios has emerged into our society. These precious resources are scattered around and preserved in different places Such as organizations like archives and libraries, along with individuals. The research combined decentralized photographic resources with Geographic Information System (GIS), focusing on the figure, event, time and location of the pictures to map them with geographic information in webpage and to display them productively. In order to meet the demand of reality, we designed a metadata description proposal, which is referred to DC and VRA standards. Another essential procedure is to formulate a four-tier classification system to correspond with the metadata proposals. As for visualization, we used Photo Waterfall and Time Line to display our resources in front end. Last but not the least, leading the Web 2.0 trend, the research developed an artistic, friendly, expandable, universal and user involvement platform to show the historical and culture precipitation of Beijing.

Keywords: historical picture, geographic information system, display platform, four-tier classification system

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Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Elida M. R. Bonifacio

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English language proficiency has become an essential issue in aviation communication after aviation incidents, and accidents happened. Lack of proficiency or inappropriate use of the English language has been found as one of the factors that cause most of those incidents or accidents. Therefore, the International Civil Aviation Organization (ICAO) established the requirements for minimum English language proficiency of aviation personnel, especially pilots and air traffic controllers in the 192 member states. In Brazil, the discussions about this topic became patent after an accident that occurred in 2006, which was a mid-air collision and costed the life of 154 passengers and crew members. Thus, the number of schools and private practitioners willing to teach English for aviation purposes started to increase. Although the number of teaching materials internationally used for general purposes is relatively large, it would be inappropriate to adopt the same materials in classes that focus on communication in aviation contexts. On the contrary, the options of aviation English materials are scarce; moreover, they are internationally used and may not fulfill the linguistic needs of all their users around the world. In order to diminish the problems that Brazilian practitioners may encounter in the adoption of materials that demand a great level of adaptation to meet their students’ needs, a checklist was thought to evaluate textbooks. The aim of this paper is to propose a checklist that evaluates textbooks used in English language training of Brazilian air traffic controllers. The criteria used to compound the checklist are based on materials development literature, as well as on linguistic requirements established by ICAO on its publications, on English for Specific Purposes (ESP) principles, and on Brazilian aviation English language proficiency test format. The checklist has as main indicators the language learning tenets under which the book was written, graphical features, lexical, grammatical and functional competencies required for minimum proficiency, similarities to official testing format, and support materials, totaling 117 items marked as YES, NO or PARTIALLY. In order to verify if the use of the checklist is effective, an aviation English textbook was evaluated. From this evaluation, it is possible to measure quantitatively how much the material meets the students’ needs and to offer a tool to help professionals engaged in aviation English teaching around the world to choose the most appropriate textbook according to their audience. From the results, practitioners are able to verify which items the material does not fulfill and to make proper adaptations since the perfect material will be difficult to find.

Keywords: aviation English, ICAO, materials development, English language proficiency

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Adel Francis

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Most of scheduling methods and software apply the critical path logic. This logic schedule activities, apply constraints between these activities and try to optimize and level the allocated resources. The extensive use of this logic produces a complex an erroneous network hard to present, follow and update. Planning and management building projects should tackle the coordination of works and the management of limited spaces, traffic, and supplies. Activities cannot be performed without the resources available and resources cannot be used beyond the capacity of workplaces. Otherwise, workspace congestion will negatively affect the flow of works. The objective of the space planning is to link the spatial and temporal aspects, promote efficient use of the site, define optimal site occupancy rates, and ensures suitable rotation of the workforce in the different spaces. The Chronographic scheduling modelling belongs to this category and models construction operations as well as their processes, logical constraints, association and organizational models, which help to better illustrate the schedule information using multiple flexible approaches. The model defined three categories of areas (punctual, surface and linear) and four different layers (space creation, systems, closing off space, finishing, and reduction of space). The Chronographical modelling is a more complete communication method, having the ability to alternate from one visual approach to another by manipulation of graphics via a set of parameters and their associated values. Each individual approach can help to schedule a certain project type or specialty. Visual communication can also be improved through layering, sheeting, juxtaposition, alterations, and permutations, allowing for groupings, hierarchies, and classification of project information. In this way, graphic representation becomes a living, transformable image, showing valuable information in a clear and comprehensible manner, simplifying the site management while simultaneously utilizing the visual space as efficiently as possible.

Keywords: building projects, chronographic modelling, CPM, critical path, precedence diagram, scheduling

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving army, moving convoys etc. The Radar operator selects one of the promising targets into Single Target Tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper we present a technique using mathematical and statistical methods like Fast Fourier Transformation (FFT) and Principal Component Analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, fft, principal component analysis, eigenvector, octave-notes, dsp

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Authors: Keiko Sakai

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In Japan, all stages of public education aim to foster a zest for life. ‘Zest’ implies solving problems by oneself, using acquired knowledge and skills. It is related to the self-regulation of metacognition. To enhance this, establishing good learning habits is important. Tardiness in undergraduate students should be examined based on self-regulation. Accordingly, we focussed on self-monitoring and self-planning strategies among self-regulated learning factors to examine the causes of tardiness. This study examines the impact of self-monitoring and self-planning learning skills on the degree and reason for tardiness in undergraduate students. A questionnaire survey was conducted, targeted to undergraduate students in University X in the autumn semester of 2018. Participants were 247 (average age 19.7, SD 1.9; 144 males, 101 females, 2 no answers). The survey contained the following items and measures: school year, the number of classes in the semester, degree of tardiness in the semester (subjective degree and objective times), active participation in and action toward schoolwork, self-planning and self-monitoring learning skills, and reason for tardiness (open-ended question). First, the relation between strategies and tardiness was examined by multiple regressions. A statistically significant relationship between a self-monitoring learning strategy and the degree of subjective and objective tardiness was revealed, after statistically controlling the school year and the number of classes. There was no significant relationship between a self-planning learning strategy and the degree of tardiness. These results suggest that self-monitoring skills reduce tardiness. Secondly, the relation between a self-monitoring learning strategy and the reason of tardiness was analysed, after classifying the reason for tardiness into one of seven categories: ‘overslept’, ‘illness’, ‘poor time management’, ‘traffic delays’, ‘carelessness’, ‘low motivation’, and ‘stuff to do’. Chi-square tests and Fisher’s exact tests showed a statistically significant relationship between a self-monitoring learning strategy and the frequency of ‘traffic delays’. This result implies that self-monitoring skills prevent tardiness because of traffic delays. Furthermore, there was a weak relationship between a self-monitoring learning strategy score and the reason-for-tardiness categories. When self-monitoring skill is higher, a decrease in ‘overslept’ and ‘illness’, and an increase in ‘poor time management’, ‘carelessness’, and ‘low motivation’ are indicated. It is suggested that a self-monitoring learning strategy is related to an internal causal attribution of failure and self-management for how to prevent tardiness. From these findings, the effectiveness of a self-monitoring learning skill strategy for reducing tardiness in undergraduate students is indicated.

Keywords: higher-education, self-monitoring, self-regulation, tardiness

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Authors: G. González, J. Ramírez, A. Rubiano

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This article describes different patented systems for safe use in vehicles based on GPS technology, speed sensors, gyroscopes, maps, communication systems, and monitors, that inform the driver about traffic jam, obstruction in the road, speed limits, among others. Once the information is analyzed and contrasted to final propose new technical needs to be solved.

Keywords: GPS, information technology, telecommunications, communication networks, gyroscope, environmental pollution

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Authors: Mostafa Gabr, Rania Abdul Galil, Nihal Salim

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Various interventions are applied in cities with the aim to improve living and acoustic environmental conditions. Noise is one of the most influential and critical factors in the environment that has an effect on the QOL (quality of life) and urban environment. It ranks second among environmental pollution issues according to EEAA. Traffic noise is a major source of noise. Noise barriers are one of the physical techniques in landscape design used to reduce the impact of noise pollution in urban areas. Roadways noise pollution can be best controlled by a noise barrier. The aim of this paper is to consider all facets of sustainability when designing a comfortable acoustic environment in roadways, through different strategies related to planning and the design process. The study focuses on the relation between the design of noise barriers as a landscape noise mitigation installation and their materiality in so far as it influences the sustainability of the open space and the acceptability of users. According to previous studies, design of noise barrier mainly depends on cost as a decisive factor. This study asserts that environmental and socioeconomic costs associated are equally important. Hence, the paper presents a strategy for sustainable soundscape design. It builds a framework focusing on materiality considering the environmental and socioeconomic impact of noise barriers shaping urban open space around the road ways, and the different academic and market positions on noise barrier types and materials. Finally, it concludes with a matrix of the relation between the noise barrier design consideration and the three pillars of sustainability (social, economic and environmental).

Keywords: traffic noise level, acoustic sustainability, noise barrier, noise reduction, noise control, acoustical level

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Priya Jaswal, Vivek, S. K. Sinha

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One of the most important and complicated factors influencing the functional performance of unpaved roads is traffic loading. The complexity of traffic loading is caused by the variable magnitude and frequency of load, which causes unpaved roads to fail prematurely. Unpaved roads are low-volume roads, and as peri-urbanization increases, unpaved roads act as a means to boost the rural economy. This has also increased traffic on unpaved roads, intensifying the issue of settlement, rutting, and fatigue failure. This is a major concern for unpaved roads built on poor subgrade soil, as excessive rutting caused by heavy loads can cause driver discomfort, vehicle damage, and an increase in maintenance costs. Some researchers discovered that when a consistent static load is exerted as opposed to a rapidly changing load, the rate of deformation of unpaved roads increases. Previously, some of the most common methods for overcoming the problem of rutting and fatigue failure included chemical stabilisation, fibre reinforcement, and so on. However, due to their high cost, engineers' attention has shifted to geotextiles which are used as reinforcement in unpaved roads. Geotextiles perform the function of filtration, lateral confinement of base material, vertical restraint of subgrade soil, and the tension membrane effect. The use of geotextiles in unpaved roads increases the strength of unpaved roads and is an economically viable method because it reduces the required aggregate thickness, which would need less earthwork, and is thus recommended for unpaved road applications. The majority of geotextiles used previously were polymeric, but with a growing awareness of sustainable development to preserve the environment, researchers' focus has shifted to natural fibres. Coir is one such natural fibre that possesses the advantage of having a higher tensile strength than other bast fibres, being eco-friendly, low in cost, and biodegradable. However, various researchers have discovered that the surface of coir fibre is covered with various impurities, voids, and cracks, which act as a plane of weakness and limit the potential application of coir geotextiles. To overcome this limitation, chemical surface modification of coir geotextiles is widely accepted by researchers because it improves the mechanical properties of coir geotextiles. The current paper reviews the effect of using treated coir geotextiles as reinforcement on the load-deformation behaviour of a two-layered unpaved road model.

Keywords: coir, geotextile, treated, unpaved

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Authors: James Boylan, Denny Meyer, Won Sun Chen

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Background: Globally, there is an increasing trend of road traffic deaths, reaching 1.35 million in 2016 in comparison to 1.3 million a decade ago, and overall, road traffic injuries are ranked as the eighth leading cause of death for all age groups. The reported death rate for younger drivers aged 16-19 years is almost twice the rate reported for older drivers aged 25 and above, with a rate of 3.5 road traffic fatalities per annum for every 10,000 licenses held. Telematics refers to a system with the ability to capture real-time data about vehicle usage. The data collected from telematics can be used to better assess a driver's risk. It is typically used to measure acceleration, turn, braking, and speed, as well as to provide locational information. With the Australian government creating the National Telematics Framework, there has been an increase in the government's focus on using telematics data to improve road safety outcomes. The purpose of this study is to test the hypothesis that improvements in telematics measured driving behaviour to relate to improvements in road safety attitudes measured by the Driving Behaviour Questionnaire (DBQ). Methodology: 28 participants were recruited and given a telematics device to insert into their vehicles for the duration of the study. The participant's driving behaviour over the course of the first month will be compared to their driving behaviour in the second month to determine whether feedback from telematics devices improves driving behaviour. Participants completed the DBQ, evaluated using a 6-point Likert scale (0 = never, 5 = nearly all the time) at the beginning, after the first month, and after the second month of the study. This is a well-established instrument used worldwide. Trends in the telematics data will be captured and correlated with the changes in the DBQ using regression models in SAS. Results: The DBQ has provided a reliable measure (alpha = .823) of driving behaviour based on a sample of 23 participants, with an average of 50.5 and a standard deviation of 11.36, and a range of 29 to 76, with higher scores, indicating worse driving behaviours. This initial sample is well stratified in terms of gender and age (range 19-27). It is expected that in the next six weeks, a larger sample of around 40 will have completed the DBQ after experiencing in-vehicle telematics for 30 days, allowing a comparison with baseline levels. The trends in the telematics data over the first 30 days will be compared with the changes observed in the DBQ. Conclusions: It is expected that there will be a significant relationship between the improvements in the DBQ and the trends in reduced telematics measured aggressive driving behaviours supporting the hypothesis.

Keywords: telematics, driving behavior, young drivers, driving behaviour questionnaire

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Jin Fan, M. Ala Saadeghvaziri

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Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: bridge, construction, drones, infrastructure, information

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Authors: Ahmad Samim Ranjbar, Shoshi Mizokami

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Kabul is the heart of political, commercial, cultural, educational and social life in Afghanistan and the Kabul fifth fastest growing city in the world, since 2001 with the establishment of new government Lack of adequate employment opportunities and basic utility services in remote provinces have prompted people to move to Kabul and other urban areas. From 2001 to the present, a rapid increase in population, and also less income of the people most of residence tend to use public transport, especially buses, however there is no proper bus system exist in Kabul city, because of wars, from 1992 to 2001 Kabul suffered damage and destruction of its transportation facilities including pavements, sidewalks, traffic circles, drainage systems, traffic signs and signals, trolleybuses and almost all of the public transit buses (e.g. Millie bus). This research is a primary and very important phase into Kabul city transportation and especially an initial and important step toward using large bus in Kabul city, which the main purpose of this research is to find the demand of Kabul city residence for public transport (Large Bus) and compare it with the actual supply from government. Finding of this research shows that the demand of Kabul city residence for the public transport (Large Bus) exceed the supply from the government, means that current public transportation (Large Bus) is not sufficient to serve people of Kabul city, it is mentionable that according to this research there is no need to build a new road or exclusive way for bus, this research propose to government for investment on the public transportation and exceed the number of large buses to can handle the current demand for public transport.

Keywords: transportation, planning, public transport, large bus, Kabul, Afghanistan

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Authors: Zakari Mustapha, Clinton Aigbavboa, Wellington Didi Thwala

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Majority of the incidents and accidents in complex high-risk systems that exist in the construction industry and other sectors have been attributed to unsafe acts of workers. The purpose of this paper was to asses Human Factors Analysis and Classification System (HFACS) in construction accident prevention. The study was conducted through the use of secondary data from journals, books and internet to achieve the objective of the study. The review of literature looked into details of different views from different scholars about HFACS framework in accidents investigations. It further highlighted on various sections or disciplines of accident occurrences in human performance within the construction. The findings from literature review showed that unsafe acts of a worker and unsafe working conditions are the two major causes of accident in the construction industry.Most significant factor in the cause of site accident in the construction industry is unsafe acts of a worker. The findings also show how the application of HFACS framework in the investigation of accident will lead to the identification of common trends. Further findings show that provision for the prevention of accident will be made based on past accident records to identify and prioritize where intervention is needed within the construction industry.

Keywords: accident, construction, HFACS, unsafe acts

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Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

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Authors: Ayman M. Othman, Hassan Y. Ahmed, Tallat A. Ali

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Inadequate maintenance programs and funds allocated for highway networks in the developed countries have led to fast deterioration of road side elements. Therefore, this research focuses on developing a performance model for road side elements periodic maintenance activities. Road side elements that receive periodic maintenance include; earthen shoulder, road signs and traffic markings. Using the level of service concept, the developed model can determine the optimal periodic maintenance intervals for those elements based on a selected level of service suitable with the available periodic maintenance budget. Data related to time periods for progressive deterioration stages for the chosen elements were collected. Ten maintenance experts in Aswan, Sohag and Assiut cities were interviewed for that purpose. Time in months related to 10%, 25%, 40%, 50%, 75%, 90% and 100% deterioration of each road side element was estimated based on the experts opinion. Least square regression analysis has shown that a power function represents the best fit for earthen shoulders edge drop-off and damage of road signs with time. It was also evident that, the progressive dirtiness of road signs could be represented by a quadratic function an a linear function could represent the paint degradation nature of both traffic markings and road signs. Actual measurements of earthen shoulder edge drop-off agree considerably with the developed model.

Keywords: deterioration, level of service, periodic maintenance, performance model, road side element

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Prabesh Karki, Shyam Thapa Chettri, Bajarang Prasad Sah, Manoj Bhattarai, Sudeep Mishra

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Introduction: Frontal sinus is frequently described as the most difficult sinus to access surgically due to its proximity to the cribriform plate, orbit, and anterior ethmoid artery. Frontal sinus surgery requires a detailed understanding of the cellular structure and FSDP unique to each patient, making high-resolution CT scans an indispensable tool to assess the difficulty of planned sinus surgery. International Frontal Sinus Anatomy Classification (IFAC) was developed to provide a more precise nomenclature for cells in the frontal recess, classifying cells based on their anatomic origin. Objectives: To assess the proportion of frontal cell variants defined by IFAC, variation with respect to age and gender. Methods: 54 cases were enrolled after a detailed clinical history, thorough general and physical examinations, and CT a report ordered in a film. Assessment and tabulation of the presence of frontal cells according to the IFAC analyzed. The prevalence of each cell type was calculated, and data were entered in MS Excel and analyzed using Statistical Package for the Social Sciences (SPSS). Descriptive statistics and frequencies were defined for categorical and numerical variables. Frequency, percentage, the mean and standard deviation were calculated. Result: Among 54 patients, 30 (55.6%) were male and 24 (44.4%) were female. The patient enrolled ranged from 18 to 78 years. Majority33.3% (n=18) were in age group of >50 years.According to IFAC, Agger nasi cells (92.6%) were most common, whereas supraorbital ethmoidal cells were least common 16 (29.6%). Prevalence of other frontoethmoidal cells was SAC- 57.4%, SAFC- 38.9%, SBC- 74.1%, SBFC- 33.3%, FSC- 38.9% of 54 cases. Conclusion: IFAC is an international consensus document that describes an anatomically precise nomenclature for classifying frontoethmoidal cells' anatomy. This study has defined the prevalence, symmetry and reliability of frontoethmoidal cells as established by the IFAC system as in other parts of the world.

Keywords: frontal sinus, frontoethmoidal cells, international frontal sinus anatomy classification

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Authors: Asma Omri, Noureddine Benothman, Sofiane Sayahi, Fethi Tlili, Hichem Besbes

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Cycling, a popular mode of transportation, can also be perilous due to cyclists' vulnerability to collisions with vehicles and obstacles. This paper presents an innovative cyclist safety system based on radar technology designed to offer real-time collision risk warnings to cyclists. The system incorporates a low-power radar sensor affixed to the bicycle and connected to a microcontroller. It leverages radar point cloud detections, a clustering algorithm, and a supervised classifier. These algorithms are optimized for efficiency to run on the TI’s AWR 1843 BOOST radar, utilizing a coarse classification approach distinguishing between cars, trucks, two-wheeled vehicles, and other objects. To enhance the performance of clustering techniques, we propose a 2-Level clustering approach. This approach builds on the state-of-the-art Density-based spatial clustering of applications with noise (DBSCAN). The objective is to first cluster objects based on their velocity, then refine the analysis by clustering based on position. The initial level identifies groups of objects with similar velocities and movement patterns. The subsequent level refines the analysis by considering the spatial distribution of these objects. The clusters obtained from the first level serve as input for the second level of clustering. Our proposed technique surpasses the classical DBSCAN algorithm in terms of geometrical metrics, including homogeneity, completeness, and V-score. Relevant cluster features are extracted and utilized to classify objects using an SVM classifier. Potential obstacles are identified based on their velocity and proximity to the cyclist. To optimize the system, we used the View of Delft dataset for hyperparameter selection and SVM classifier training. The system's performance was assessed using our collected dataset of radar point clouds synchronized with a camera on an Nvidia Jetson Nano board. The radar-based cyclist safety system is a practical solution that can be easily installed on any bicycle and connected to smartphones or other devices, offering real-time feedback and navigation assistance to cyclists. We conducted experiments to validate the system's feasibility, achieving an impressive 85% accuracy in the classification task. This system has the potential to significantly reduce the number of accidents involving cyclists and enhance their safety on the road.

Keywords: 2-level clustering, coarse classification, cyclist safety, warning system based on radar technology

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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

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

Procedia PDF Downloads 342