Search results for: encrypted traffic classification

Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Hamida Qumber Ali, Waseem Muhammad Arain, Shama Siddiqui, Sayeed Ghani

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We present an implementing of RMAC in TinyOS 1.x. RMAC is a cross layer and Duty-cycle MAC protocols that was proposed to provide energy efficient transmission services for wireless sensor networks. The protocol has a unique and efficient packet transmission scheduling mechanism that enables it to overcome delivery latency and overcome traffic congestion. Design details and implementation challenges are divulged. Experiments are conducted to show the correctness of our implementation with numerous assumptions. Simulations are performed to compare the performance of RMAC and SMAC. Our results show that RMAC outperforms SMAC in energy efficiency and delay.

Keywords: MAC protocol, performance, RMAC, wireless sensor networks

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Authors: Ahmed Emad, Ahmed Salah, Abdelrahman Magdy, Omar Rashid, Mohammed Adel

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This research paper discusses vehicle-to-vehicle technology as an important application of linear algebra. This communication technology represents an efficient and promising application to help to ensure the safety of the drivers by warning them when a crash possibility is close. The major link that combines our topic with linear algebra is the Laplacian matrix. Some main definitions used in the V2V were illustrated, such as VANET and its characteristics. The V2V technology could be applied in different applications with different traffic scenarios and various ways to warn car drivers. These scenarios were simulated programs such as MATLAB and Python to test how the V2V system would respond to the different scenarios and warn the car drivers exposed to the threat of collisions.

Keywords: V2V communication, vehicle to vehicle scenarios, VANET, FCW, EEBL, IMA, Laplacian matrix

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Authors: Jyoti Bharti, Navneet Manjhi, M. K.Gupta, Bimi Jain

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A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of gray scale and silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and nearest neighbour classifier are used for classification. Both achieved higher recognition.

Keywords: gait, fuzzy logic, nearest neighbour, recognition rate, moments

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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Authors: Ferran Paulí Pla, Pere Palacín Farré, Albert Fornells Herrera, Pol Toldrà Fernández

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Poor image acquisition is limiting the promising growth of industrial vision in food control. In recent years, the food industry has witnessed a significant increase in the implementation of automation in quality control through artificial vision, a trend that continues to grow. During the packaging process, some defects may appear, compromising the proper sealing of the products and diminishing their shelf life, sanitary conditions and overall properties. While failure to detect a defective product leads to major losses, food producers also aim to minimize over-rejection to avoid unnecessary waste. Thus, accuracy in the evaluation of the products is crucial, and, given the large production volumes, even small improvements have a significant impact. Recently, efforts have been focused on maximizing the performance of classification neural networks; nevertheless, their performance is limited by the quality of the input data. Monochrome linear backlight systems are most commonly used for online inspections of food packaging thermo-sealing zones. These simple acquisition systems fit the high cadence of the production lines imposed by the market demand. Nevertheless, they provide a limited amount of data, which negatively impacts classification algorithm training. A desired situation would be one where data quality is maximized in terms of obtaining the key information to detect defects while maintaining a fast working pace. This work presents a backlight hyperspectral imaging system designed and implemented replicating an industrial environment to better understand the relationship between visual data quality and spectral illumination range for a variety of packed food products. Furthermore, results led to the identification of advantageous spectral bands that significantly enhance image quality, providing clearer detection of defects.

Keywords: artificial vision, food packaging, hyperspectral imaging, image acquisition, quality control

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Johannes Günther

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Time Sensitive Networking (TSN), standardized in the IEEE 802.1 standard, has been lent increasing attention in the context of mission critical systems. Such mission critical systems, e.g., in the automotive domain, aviation, industrial, and smart factory domain, are responsible for coordinating complex functionalities in real time. In many of these contexts, a reliable data exchange fulfilling hard time constraints and quality of service (QoS) conditions is of critical importance. TSN standards are able to provide guarantees for deterministic communication behaviour, which is in contrast to common best-effort approaches. Therefore, the superior QoS guarantees of TSN may aid in the development of new technologies, which rely on low latencies and specific bandwidth demands being fulfilled. TSN extends existing Ethernet protocols with numerous standards, providing means for synchronization, management, and overall real-time focussed capabilities. These additional QoS guarantees, as well as management mechanisms, lead to an increased attack surface for potential malicious attackers. As TSN guarantees certain deadlines for priority traffic, an attacker may degrade the QoS by delaying a packet beyond its deadline or even execute a denial of service (DoS) attack if the delays lead to packets being dropped. However, thus far, security concerns have not played a major role in the design of such standards. Thus, while TSN does provide valuable additional characteristics to existing common Ethernet protocols, it leads to new attack vectors on networks and allows for a range of potential attacks. One answer to these security risks is to deploy defense mechanisms according to a moving target defense (MTD) strategy. The core idea relies on the reduction of the attackers' knowledge about the network. Typically, mission-critical systems suffer from an asymmetric disadvantage. DoS or QoS-degradation attacks may be preceded by long periods of reconnaissance, during which the attacker may learn about the network topology, its characteristics, traffic patterns, priorities, bandwidth demands, periodic characteristics on links and switches, and so on. Here, we implemented and tested several MTD-like defense strategies against different attacker models of varying capabilities and budgets, as well as collaborative attacks of multiple attackers within a network, all within the context of TSN networks. We modelled the networks and tested our defense strategies on an OMNET++ testbench, with networks of different sizes and topologies, ranging from a couple dozen hosts and switches to significantly larger set-ups.

Keywords: network security, time sensitive networking, moving target defense, cyber security

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Authors: Shan Zhou, Wenju Li, Kehui Chai

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Along with economic globalization, marketization and regional development, strengthen planning and construction of the New Town, which is always the main way to optimize the structure and function of metropolitan spatial configuration. But, the new town is often of high-intensity development, bringing a series of natural, ecological and environmental issues, so it is difficult to achieve sustainable development. In this paper, taking the administrative center of Jiangping in Dongxing as an example. It is analyzed from the following three aspects：Vertical design of road traffic，Space layout of mountain buildings，and the design of landscape. The purpose is to elaborate the hillside design methods guided by low-intensity damage, and explore the guiding significance of sustainable development of the hillside construction in the future.

Keywords: low-intensity damage, new town construction，hillside，sustainable development, natural, ecology

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Authors: Loonibha Manandhar, Rajendra Bhandari, Kumud Raj Kafle

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Nepal’s Terai region is a part of the Ganges river basin which is one of the most disaster-prone areas of the world, with recurrent monsoon flooding causing millions in damage and the death and displacement of hundreds of people and households every year. The vulnerability of human settlements to natural disasters such as floods is increasing, and mapping changes in land use practices and hydro-geological parameters is essential in developing resilient communities and strong disaster management policies. The objective of this study was to develop a flood hazard zonation map of Orai Khola watershed and map the decadal land use/land cover dynamics of the watershed. The watershed area was delineated using SRTM DEM, and LANDSAT images were classified into five land use classes (forest, grassland, sediment and bare land, settlement area and cropland, and water body) using pixel-based semi-automated supervised maximum likelihood classification. Decadal changes in each class were then quantified using spatial modelling. Flood hazard mapping was performed by assigning weights to factors slope, rainfall distribution, distance from the river and land use/land cover on the basis of their estimated influence in causing flood hazard and performing weighed overlay analysis to identify areas that are highly vulnerable. The forest and grassland coverage increased by 11.53 km² (3.8%) and 1.43 km² (0.47%) from 1996 to 2016. The sediment and bare land areas decreased by 12.45 km² (4.12%) from 1996 to 2016 whereas settlement and cropland areas showed a consistent increase to 14.22 km² (4.7%). Waterbody coverage also increased to 0.3 km² (0.09%) from 1996-2016. 1.27% (3.65 km²) of total watershed area was categorized into very low hazard zone, 20.94% (60.31 km²) area into low hazard zone, 37.59% (108.3 km²) area into moderate hazard zone, 29.25% (84.27 km²) area into high hazard zone and 31 villages which comprised 10.95% (31.55 km²) were categorized into high hazard zone area.

Keywords: flood hazard, land use/land cover, Orai river, supervised maximum likelihood classification, weighed overlay analysis

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: M. Budakoglu, M. Karaman

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Lake Acıgöl is located in area with limited influences from urban and industrial pollution sources, there is nevertheless a need to understand all potential lithological and anthropogenic sources of priority contaminants in this closed basin. This study discusses vertical and horizontal distribution pattern of major, trace elements of recent lake sediments to better understand their current geochemical analog with lithological units in the Lake Acıgöl basin. This study also provides reliable background levels for the region by the detailed surfaced lithological units data. The detail results of surface, subsurface and shallow core sediments from these relatively unperturbed ecosystems, highlight its importance as conservation area, despite the high-scale industrial salt production activity. While P2O5/TiO2 versus MgO/CaO classification diagram indicate magmatic and sedimentary origin of lake sediment, Log(SiO2/Al2O3) versus Log(Na2O/K2O) classification diagrams express lithological assemblages of shale, iron-shale, vacke and arkose. The plot between TiO2 vs. SiO2 and P2O5/TiO2 vs. MgO/CaO also supports the origin of the primary magma source. The average compositions of the 20 different lithological units used as a proxy for geochemical background in the study area. As expected from weathered rock materials, there is a large variation in the major element content for all analyzed lake samples. The A-CN-K and A-CNK-FM ternary diagrams were used to deduce weathering trends. Surface and subsurface sediments display an intense weathering history according to these ternary diagrams. The most of the sediments samples plot around UCC and TTG, suggesting a low to moderate weathering history for the provenance. The sediments plot in a region clearly suggesting relative similar contents in Al2O3, CaO, Na2O, and K2O from those of lithological samples.

Keywords: Lake Acıgöl, recent lake sediment, geochemical speciation of major and trace elements, heavy metals, Denizli, Turkey

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Authors: Samhita Mummadi, Sree Divya Nagalli, Harshini Vemuri, Saketh Charan Nakka, Sumesh K. J.

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One of the most significant challenges faced by people in today’s digital era is an alarming increase in fraudulent activities on online platforms. The fascination with online shopping to avoid long queues in shopping malls, the availability of a variety of products, and home delivery of goods have paved the way for a rapid increase in vast online shopping platforms. This has had a major impact on increasing fraudulent activities as well. This loop of online shopping and transactions has paved the way for fraudulent users to commit fraud. For instance, consider a store that orders thousands of products all at once, but what’s fishy about this is the massive number of items purchased and their transactions turning out to be fraud, leading to a huge loss for the seller. Considering scenarios like these underscores the urgent need to introduce machine learning approaches to combat fraud in online shopping. By leveraging robust algorithms, namely KNN, Decision Trees, and Random Forest, which are highly effective in generating accurate results, this research endeavors to discern patterns indicative of fraudulent behavior within transactional data. Introducing a comprehensive solution to this problem in order to empower e-commerce administrators in timely fraud detection and prevention is the primary motive and the main focus. In addition to that, sentiment analysis is harnessed in the model so that the e-commerce admin can tailor to the customer’s and consumer’s concerns, feedback, and comments, allowing the admin to improve the user’s experience. The ultimate objective of this study is to ramp up online shopping platforms against fraud and ensure a safer shopping experience. This paper underscores a model accuracy of 84%. All the findings and observations that were noted during our work lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as technologies continue to evolve.

Keywords: behavior analysis, feature selection, Fraudulent pattern recognition, imbalanced classification, transactional anomalies

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Authors: Saad Algharib, Jay Lee

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Urbanization is a complex phenomenon that occurs during the city’s development from one form to another. In other words, it is the process when the activities in the land use/land cover change from rural to urban. Since the oil exploration, Kuwait City has been growing rapidly due to its urbanization and population growth by both natural growth and inward immigration. The main objective of this study is to detect changes in urban land use/land cover and to examine the changing spatial patterns of urban growth in and around Kuwait City between 1989 and 2001. In addition, this study also evaluates the spatial patterns of the changes detected and how they can be related to the spatial configuration of the city. Recently, the use of remote sensing and geographic information systems became very useful and important tools in urban studies because of the integration of them can allow and provide the analysts and planners to detect, monitor and analyze the urban growth in a region effectively. Moreover, both planners and users can predict the trends of the growth in urban areas in the future with remotely sensed and GIS data because they can be effectively updated with required precision levels. In order to identify the new urban areas between 1989 and 2001, the study uses satellite images of the study area and remote sensing technology for classifying these images. Unsupervised classification method was applied to classify images to land use and land cover data layers. After finishing the unsupervised classification method, GIS overlay function was applied to the classified images for detecting the locations and patterns of the new urban areas that developed during the study period. GIS was also utilized to evaluate the distribution of the spatial patterns. For example, Moran’s index was applied for all data inputs to examine the urban growth distribution. Furthermore, this study assesses if the spatial patterns and process of these changes take place in a random fashion or with certain identifiable trends. During the study period, the result of this study indicates that the urban growth has occurred and expanded 10% from 32.4% in 1989 to 42.4% in 2001. Also, the results revealed that the largest increase of the urban area occurred between the major highways after the forth ring road from the center of Kuwait City. Moreover, the spatial distribution of urban growth occurred in cluster manners.

Keywords: geographic information systems, remote sensing, urbanization, urban growth

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Authors: Khaled Shaaban

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Distracted driving, which includes anything that distracts a driver from the main task of driving, is one of the main causes of traffic accidents in modern societies. The objective of this research was to understand the type of activities that young drivers perform while driving in Qatar and to identify which activities cause the most distraction to the driver based on their experience. The data was collected through administered questionnaires in the city of Doha, Qatar. According to the participants, the majority reported that they use their cell phone all the time or occasionally while driving. Other significantly cited activities while driving included listening to music or radio, talking with passengers, and eating, drinking or smoking. When asked about the activities that distract the driver, using cell phone was listed as the most distracting activity followed by mental activities and adjusting GPS and audio device vehicle.

Keywords: driver distraction, young drivers, cell phone use, Qatar

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Authors: Yuan Zhang

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Along with large population and great demands for urban development, Hong Kong serves as a typical high-density city with multiple altitudes, advanced three-dimensional traffic system, rich city open space, etc. This paper contributes to analyzing its complex urban form and evolution mechanism from three aspects of view, separately as time, space and buildings. Taking both horizontal and vertical dimension into consideration, this paper provides a perspective to explore the fascinating process of growing and space folding in the urban form of high-density city, also as a research reference for related high-density urban design.

Keywords: evolution mechanism, high-density city, Hong Kong, urban form

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Authors: Lakshay Kharbanda, Aabhas Chauhan

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In this paper, an application of Normalized Compression Distance (NCD) to detect notable scene alterations occurring in videos is presented. Several research groups have been developing methods to perform image classification using NCD, a computable approximation to Normalized Information Distance (NID) by studying the degree of similarity in images. The timeframes where significant aberrations between the frames of a video have occurred have been identified by obtaining a threshold NCD value, using two compressors: LZMA and BZIP2 and defining scene alterations using Pixel Difference Percentage metrics.

Keywords: image compression, Kolmogorov complexity, normalized compression distance, root mean square error

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

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Authors: Ahmad Dawabsheh

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The science of prosthetics is a renewable science that serves all humanity, regardless of gender, religion and race, and its causes are many: wars, conflicts, traffic accidents, and others. The researcher believes that there are challenges facing the specialization, including that society views a negative view of the amputee, especially if it is a female. This research aims to focus on the reality of teaching prosthetics in Palestine, especially in the Arab American University, as it is the only major. As well as the challenges facing this major: financial, human, academic, laboratories, and others. The researcher used the descriptive and analytical approach, which is the closest approach to studying the subject. The researcher believes that there is a failure on the part of the state and the Ministry of Health in this matter. In addition to the lack of societal culture, as well as the large quantities of prosthetic fittings.

Keywords: prothetics, orthotics, Arab American University, Palestine

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Authors: Manoj Gupta, Nirmendra Singh Bhadauria

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Most of the applications in image processing require high spatial and high spectral resolution in a single image. For example satellite image system, the traffic monitoring system, and long range sensor fusion system all use image processing. However, most of the available equipment is not capable of providing this type of data. The sensor in the surveillance system can only cover the view of a small area for a particular focus, yet the demanding application of this system requires a view with a high coverage of the field. Image fusion provides the possibility of combining different sources of information. In this paper, we have decomposed the image using DTCWT and then fused using average and hybrid of (maxima and average) pixel level techniques and then compared quality of both the images using PSNR.

Keywords: image fusion, DWT, DT-CWT, PSNR, average image fusion, hybrid image fusion

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Authors: Kishor J. shinde

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We determine the values of k and p such that the Sturm-Liouville differential operator τu=-(d^2 u)/(dx^2) + kx^p u is in limit point case or limit circle case at infinity. In particular it is shown that τ is in the limit point case when (i) for p=2 and ∀k, (ii) for ∀p and k=0, (iii) for all p and k>0, (iv) for 0≤p≤2 and k<0, (v) for p<0 and k<0. τ is in the limit circle case when (i) for p>2 and k<0.

Keywords: limit point case, limit circle case, Sturm-Liouville, infinity

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Authors: Burçin Saltık, Levent Genç

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In this study, it was aimed to determine a route for identification of rice cultivation areas within Thrace and Marmara regions of Turkey using remote sensing and GIS. Landsat 8 (OLI-TIRS) imageries acquired in production season of 2013 with 181/32 Path/Row number were used. Four different seasonal images were generated utilizing original bands and different transformation techniques. All images were classified individually using supervised classification techniques and Land Use Land Cover Maps (LULC) were generated with 8 classes. Areas (ha, %) of each classes were calculated. In addition, district-based rice distribution maps were developed and results of these maps were compared with Turkish Statistical Institute (TurkSTAT; TSI)’s actual rice cultivation area records. Accuracy assessments were conducted, and most accurate map was selected depending on accuracy assessment and coherency with TSI results. Additionally, rice areas on over 4° slope values were considered as mis-classified pixels and they eliminated using slope map and GIS tools. Finally, randomized rice zones were selected to obtain maximum-minimum value ranges of each date (May, June, July, August, September images separately) NDVI, LSWI, and LST images to test whether they may be used for rice area determination via raster calculator tool of ArcGIS. The most accurate classification for rice determination was obtained from seasonal LSWI LULC map, and considering TSI data and accuracy assessment results and mis-classified pixels were eliminated from this map. According to results, 83151.5 ha of rice areas exist within study area. However, this result is higher than TSI records with an area of 12702.3 ha. Use of maximum-minimum range of rice area NDVI, LSWI, and LST was tested in Meric district. It was seen that using the value ranges obtained from July imagery, gave the closest results to TSI records, and the difference was only 206.4 ha. This difference is normal due to relatively low resolution of images. Thus, employment of images with higher spectral, spatial, temporal and radiometric resolutions may provide more reliable results.

Keywords: landsat 8 (OLI-TIRS), LST, LSWI, LULC, NDVI, rice

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Authors: Antonio Pratelli, Lorenzo Brocchini, Reginald Roy Souleyrette

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A rotary is a traffic circle intersection where vehicles entering from branches give priority to circulating flow. Vehicles entering the intersection from converging roads move around the central island and weave out of the circle into their desired exiting branch. This creates merging and diverging conflicts among any entry and its successive exit, i.e., a section. Therefore, rotary capacity models are usually based on the weaving of the different movements in any section of the circle, and the maximum rate of flow value is then related to each weaving section of the rotary. Nevertheless, the single-section capacity value does not lead to the typical performance characteristics of the intersection, such as the entry average delay which is directly linked to its level of service. From another point of view, modern roundabout capacity models are based on the limitation of the flow entering from the single entrance due to the amount of flow circulating in front of the entrance itself. Modern roundabouts capacity models generally lead also to a performance evaluation. This paper aims to incorporate a modern roundabout capacity model into an old rotary capacity method to obtain from the latter the single input capacity and ultimately achieve the related performance indicators. Put simply; the main objective is to calculate the average delay of each single roundabout entrance to apply the most common Highway Capacity Manual, or HCM, criteria. The paper is organized as follows: firstly, the rotary and roundabout capacity models are sketched, and it has made a brief introduction to the model combination technique with some practical instances. The successive section is deserved to summarize the TRRL old rotary capacity model and the most recent HCM-7th modern roundabout capacity model. Then, the two models are combined through an iteration-based algorithm, especially set-up and linked to the concept of roundabout total capacity, i.e., the value reached due to a traffic flow pattern leading to the simultaneous congestion of all roundabout entrances. The solution is the average delay for each entrance of the rotary, by which is estimated its respective level of service. In view of further experimental applications, at this research stage, a collection of existing rotary intersections operating with the priority-to-circle rule has already started, both in the US and in Italy. The rotaries have been selected by direct inspection of aerial photos through a map viewer, namely Google Earth. Each instance has been recorded by location, general urban or rural, and its main geometrical patterns. Finally, conclusion remarks are drawn, and a discussion on some further research developments has opened.

Keywords: mixed methods, old rotary and modern roundabout capacity models, total capacity algorithm, level of service estimation

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: G. Guramishvili, M. Moistsrapishvili, L. Andghuladze

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In the article are considered the problems arising at increasing of transferring from rolling stock axles on rail loading from 210 KN up to 270 KN and is offered for rail strength analysis definition of rail force loading complex integral characteristic with taking into account all affecting force factors that is characterizing specific operation condition of rail structure and defines the working capability of structure. As result of analysis due mentioned method is obtained that in the conditions of 270 KN loading the rail meets the working assessment criteria of rail and rail structures: Strength, rail track stability, rail links stability and its transverse stability, traffic safety condition that is rather important for post-Soviet countries railways.

Keywords: axial loading, rail force loading, rail structure, rail strength analysis, rail track stability

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Authors: Martin Kendra, Jaroslav Mašek, Juraj Čamaj, Matej Babin

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The railway transport is considered as a one of the most environmentally friendly mode of transport. With future prediction of increasing of freight transport there are lines facing problems with demanded capacity. Increase of the track capacity could be achieved by infrastructure constructive adjustments. The contribution shows how the travel time can be minimized and the track capacity increased by changing some of the basic infrastructure and operation parameters, for example, the minimal curve radius of the track, the number of tracks, or the usable track length at stations. Calculation of the necessary parameter changes is based on the fundamental physical laws applied to the train movement, and calculation of the occupation time is dependent on the changes of controlling the traffic between the stations.

Keywords: curve radius, maximum curve speed, track mass capacity, reconstruction

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Authors: Ines Antunes, Andrea Massari, Concetta Bartucca

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Greenhouse gasses (GHG) role in the atmosphere has been well known since the 19th century; however, researchers have begun to relate them to climate changes only in the second half of the following century. From this moment, scientists started to correlate the presence of GHG such as CO₂ with the global warming phenomena. This has raised the awareness not only of those who were experts in this field but also of public opinion, which is becoming more and more sensitive to environmental pollution and sustainability issues. Nowadays the reduction of GHG emissions is one of the principal objectives of EU nations. The target is an 80% reduction of emissions in 2050 and to reach the important goal of carbon neutrality. Road sector is responsible for an important amount of those emissions (about 20%). The most part is due to traffic, but a good contribution is also given directly or indirectly from road construction and maintenance. Raw material choice and reuse of post-consumer plastic rather than a cleverer design of roads have an important contribution to reducing carbon footprint. Bituminous membranes can be successfully used as reinforcement systems in asphalt layers to improve road pavement performance against cracking. Composite materials coupling membranes with grids and/or fabrics should be able to combine improved tensile properties of the reinforcement with stress absorbing and waterproofing effects of membranes. Polyglass, with its brand dedicated to road construction and maintenance called Polystrada, has done more than this. The company's target was not only to focus sustainability on the final application but also to implement a greener mentality from the cradle to the grave. Starting from production, Polyglass has made important improvements finalized to increase efficiency and minimize waste. The installation of a trigeneration plant and the usage of selected production scraps inside the products as well as the reduction of emissions into the environment, are one of the main efforts of the company to reduce impact during final product build-up. Moreover, the benefit given by installing Polystrada products brings a significant improvement in road lifetime. This has an impact not only on the number of maintenance or renewal that needs to be done (build less) but also on traffic density due to works and road deviation in case of operations. During the end of the life of a road, Polystrada products can be 100% recycled and milled with classical systems used without changing the normal maintenance procedures. In this work, all these contributions were quantified in terms of CO₂ emission thanks to an LCA analysis. The data obtained were compared with a classical system or a standard production of a membrane. What it is possible to see is that the usage of Polyglass products for street maintenance and building gives a significant reduction of emissions in case of membrane installation under the road wearing course.

Keywords: CO₂ emission, LCA, maintenance, sustainability

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