Search results for: LBP features

Authors: Nihad Karim Chowdhury, Mohammad Sanaullah Chowdhury, Muhammed Jamshed Alam Patwary, Rubel Biswas

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In content-based 3D shape retrieval system, achieving high search performance has become an important research problem. A challenging aspect of this problem is to find an effective shape descriptor which can discriminate similar shapes adequately. To address this problem, we propose a new shape descriptor for 3D shape models by combining multi-resolution with multi-block center-symmetric local binary pattern operator. Given an arbitrary 3D shape, we first apply pose normalization, and generate a set of multi-viewed 2D rendered images. Second, we apply Gaussian multi-resolution filter to generate several levels of images from each of 2D rendered image. Then, overlapped sub-images are computed for each image level of a multi-resolution image. Our unique multi-block CS-LBP comes next. It allows the center to be composed of m-by-n rectangular pixels, instead of a single pixel. This process is repeated for all the 2D rendered images, derived from both ‘depth-buffer’ and ‘silhouette’ rendering. Finally, we concatenate all the features vectors into one dimensional histogram as our proposed 3D shape descriptor. Through several experiments, we demonstrate that our proposed 3D shape descriptor outperform the previous methods by using a benchmark dataset.

Keywords: 3D shape retrieval, 3D shape descriptor, CS-LBP, overlapped sub-images

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Authors: Rylyne Mande Nchu, Robertson Tengeh, Chux Iwu

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Blended learning is a global phenomenon and is essential to many institutes of learning as an additional method of teaching that complements more traditional methods of learning. In this paper, the lack of practice of a blended learning approach to entrepreneurship education and how it impacts learners' perception of being entrepreneurial. E-learning is in its infancy within the secondary and high school sectors in South Africa. The conceptual framework of the study is based on theoretical aspects of systemic-constructivist learning implemented in an interactive online learning environment in an entrepreneurship education subject. The formative evaluation research was conducted implementing mixed methods of research (quantitative and qualitative) and it comprised a survey of high school learners and informant interviewing with entrepreneurs. Theoretical analysis of literature provides features necessary for creating interactive blended learning environments to be used in entrepreneurship education subject. Findings of the study show that learners do not always objectively evaluate their capacities. Special attention has to be paid to the development of learners’ computer literacy as well as to the activities that would bring online learning to practical training. Needs analysis shows that incorporating blended learning in entrepreneurship education may have a positive perception of entrepreneurship.

Keywords: blended learning, entrepreneurship education, entrepreneurship intention, entrepreneurial skills

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Authors: Aliya Grig, Konstantin Sokolov, Igor Shatalin

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The report describes the methodology for collecting data and building an ML model for determining the personality psychotype using profiling and personality traits methods based on several short messages of a user communicating on an arbitrary topic with a chitchat bot. In the course of the experiments, the minimum amount of text was revealed to confidently determine aspects of personality. Model accuracy - 85%. Users' language of communication is English. AI for a personalized communication with a user based on his mood, personality, and current emotional state. Features investigated during the research: personalized communication; providing empathy; adaptation to a user; predictive analytics. In the report, we describe the processes that captures both structured and unstructured data pertaining to a user in large quantities and diverse forms. This data is then effectively processed through ML tools to construct a knowledge graph and draw inferences regarding users of text messages in a comprehensive manner. Specifically, the system analyzes users' behavioral patterns and predicts future scenarios based on this analysis. As a result of the experiments, we provide for further research on training AI models to be empathetic, creating personalized communication for a user

Keywords: AI, empathetic, chatbot, AI models

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Authors: Jonas Volungevicius, Kristina Amaleviciute, Rimantas Vaisvalavicius, Alvyra Slepetiene, Darijus Veteikis

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Lithuanian territory is characterized by landscape with prevailing morain hills and clayey lowlands. The largest part of it has endured agrogenization of various degrees which was the cause of changes both in the structure of landscape and soil cover, transformations of soil profile and degradation of natural background soils. These changes influence negatively geoecological potential of landscape and soil and contribute to the weakening of the sustainability of agroecosystems. Research objective: to reveal the landscape agrogenization induced alterations of catenae and their appendant soil profiles in Lithuanian morain hills and clayey lowlands. Methods: Soil cover analysis and catenae charting was conducted using landscape profiling; soil morphology detected and soil type identified following WRB 2014. Granulometric composition of soil profiles was obtained by laser diffraction method (lazer diffractometer Mastersizer 2000). pH was measured in H2O extraction using potentiometric titration; SOC was determined by the Tyurin method modified by Nikitin, measuring with spectrometer Cary 50 (VARIAN) in 590 nm wavelength using glucose standards. Results: analysis showed that the decrease of forest vegetation and the other natural landscape components following the agrogenization of the research area influenced differently but significantly the structural alterations in soil cover and vertical soil profile. The research detected that due to landscape agrogenization, the suppression of zone-specific processes and the intensification of inter-zone processes determined by agrogenic factors take place in Lithuanian agroecosystems. In forested hills historically prevailing Retisols and Histosols territorial complex is transforming into the territorial complex of Regosols, Deluvial soils and drained Histosols. Processes taking place are simplification of vertical profile structure, intensive rejuvenation of profile, disappearance of the features of zone-specific soil-forming processes (podzolization, lessivage, gley formation). Erosion and deluvial processes manifest more intensively and weakly accumulating organic material more intensively spread in a vertical soil profile. The territorial soil complex of Gleyic Luvisols and Gleysols dominating in forested clayey lowlands subjected to agrogenization is transformed into the catena of drained Luvisols and pseudo Cambisols. The best expressed are their changes in moisture regime (morphological features of gley and stagnic properties are on decline) together with alterations of pH and distribution and intensity of accumulation of organic matter in profile. A specific horizon, antraquic, uncharacteristic to natural soil formation is appearing. Important to note that due to deep ploughing and other agrotechnical measures, the natural vertical differentiation of clay particles in a soil profile is destroyed which leads not only to alterations of physical qualities of soil, but also encumbers the identification of Luvisols by creating presumptions to misidentify them as Cambisols. The latter have never developed in these ecosystems under the present climatic conditions. Acknowledgements: This work was supported by the National Science Program: The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: agroecosystems, landscape agrogenization, luvisols, retisols, transformation of soil profile

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Authors: Michael Saad Thabet Azrek

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The advanced facts era is becoming a vital element within the improvement of financial offerings enterprise, in particular, the banking enterprise. It has introduced new approaches to delivering banking to the patron, including Internet Banking. Banks started to observe digital banking (e-banking) as a means to update a number of their conventional branch features using the net as a brand-new distribution channel. A few purchasers have, as a minimum, a couple of accounts across banks and get the right of entry to these accounts using e-banking offerings. To study the contemporary net really worth role, clients ought to log in to each of their debts and get the info and paintings on consolidation. This not simplest takes enough time, but it's also a repetitive hobby at a specific frequency. To cope with this point, an account aggregation idea is introduced as an answer. E-banking account aggregation, as one of the e-banking types, appeared to construct a more potent dating with clients. Account Aggregation provider usually refers to a provider that permits clients to manage their financial institution debts maintained in distinct establishments through a not unusual net banking operating a platform, with an excessive situation to protection and privateness. This paper gives an outline of an e-banking account aggregation method as a new provider in the e-banking field.

Keywords: compatibility, complexity, mobile banking, observation, risk banking technology, internet banks, modernization of banks, banks, account aggregation, security, enterprise development

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Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

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All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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Authors: Rihab Hamdi, Amel Hadri Hamida, Ouafae Bennis, Fatima Babaa, Sakina Zerouali

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This paper features a sliding mode controller (SMC) for closed-loop voltage control of DC-DC three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM). To maintain the switching frequency, the approach is to incorporate a pulse-width modulation that utilizes an equivalent control, inferred by applying the SM control method, to produce a control sign to be contrasted and the fixed-frequency within the modulator. Detailed stability and transient performance analysis have been conducted using Lyapunov stability criteria to restrict the switching frequency variation facing wide variations in output load, input changes, and set-point changes. The results obtained confirm the effectiveness of the proposed control scheme in achieving an enhanced output transient performance while faithfully realizing its control objective in the event of abrupt and uncertain parameter variations. Simulations studies in MATLAB/Simulink environment are performed to confirm the idea.

Keywords: DC-DC converter, pulse width modulation, power electronics, sliding mode control

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Authors: Ilaisa Kacivakanadina, Samson Viulu, Brad Carte, Katy Soapi

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Two bioactive compounds namely Vulgamycin (also known as enterocin A) and 5-deoxyenterocin were purified from a marine bacterial strain 1903. Strain 1903 was isolated from marine sediments collected from the Solomon Islands. Morphological features of strain 1903 showed that it belongs to the genus Streptomyces. The two secondary metabolites were extracted using EtOAc and purified by chromatographic methods using EtOAc and hexane solvents. Mass spectrum and NMR data of pure compounds were used to elucidate the chemical structures. In this study, results showed that both compounds were strongly active against Wild Type Staphylococcus aureus (WTSA) (MIC < 1 µg/mL) and in Brine shrimp assays (BSA) (MIC < 1 µg/mL). 5-deoxyenterocin was also active against Rifamycin resistant Staphylococcus aureus (RRSA) (MIC, 250 µg/mL) while vulgamycin showed bioactivity against Methicillin resistant Staphylococcus aureus (MRSA) (MIC 250 µg/mL). To the best of our knowledge, this is the first study that showed the bio-activity of 5-deoxyenterocin. This is also the first time that Vulgamycin has been reported to be active in a BSA. There has not been any mechanism of action studies for these two compounds against pathogens. This warrants further studies on their mechanism of action against microbial pathogens.

Keywords: 5-deoxyenterocin, bioactivity, brine shrimp assay (BSA), vulgamycin

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Authors: Budoor Al Abid

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Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only.

Keywords: machine learning, adaptive, fuzzy logic, data mining

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Authors: Merve Ozer, Tolga Gokkurt, Yasemen Gokkurt, Ezgi Bozbey

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In this study, we investigated the recovery of Polyethylene terephthalate/Polypropylene (PET/PP)-containing automotive textile waste from post-product and post-consumer phases in the automotive sector according to the upcycling technique and the methods of formulation and production that would allow these wastes to be substituted as PP/PET alloys instead of original PP raw materials used in plastic edge band production. The laminated structure of the stated wastes makes it impossible to separate the incompatible PP and PET phases in content and thus produce a quality raw material or product as a result of recycling. Within the scope of a two-stage production process, a comprehensive process was examined using block copolymers and maleic grafted copolymers with different features to ensure that these two incompatible phases are compatible. The mechanical, thermal, and morphological properties of the plastic raw materials, which will be referred to as PP/PET blends obtained as a result of the process, were examined in detail and discussed their substitutability instead of the original raw materials.

Keywords: mechanical recycling, melt blending, plastic blends, polyethylene, polypropylene, recycling of plastics, terephthalate, twin screw extruders

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Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao

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Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.

Keywords: OSML, BERT, text classification, one shot

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Authors: Godwin A. Otogo, Sansanee Wangvoralak, Graham J. Pierce, Lee C. Hastie, Beth Scott

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The squid Loligo forbesii is suspected to be an important species in marine food webs, as it can strongly impact its prey and be impacted upon by predation, competition, fishing and/or climate variability. To quantify these impacts in the food web, the measurement of its trophic position and ecological role within well-studied ecosystems is essential. An Ecopath model was balanced and run for the Moray Firth ecosystem and was used to investigate the significance of this squid’s trophic roles. The network analysis routine included in Ecopath with Ecosim (EwE) was used to estimate trophic interaction, system indicators (health condition and developmental stage) and food web features. Results indicated that within the Moray Firth squid occupy a top trophic position in the food web and also a major prey item for many other species. Results from Omnivory Index (OI) showed that squid is a generalized feeder transferring energy across wide trophic levels and is more important as a predator than that as a prey in the Moray Firth ecosystem. The results highlight the importance of taking squid into account in the management of Europe’s living marine resources.

Keywords: Squid, Loligo forbesii, Ecopath, Moray Firth, Trophic level

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Authors: Veselin Konstantinov Hristov

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

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

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Authors: Sebastião Milton Pinheiro da Silva, Michele Barbosa da Rocha, Ana Lúcia Fernandes Campos, Miquéias Rildo de Souza Silva

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Water is an essential natural resource for life on Earth. Rivers, lakes, lagoons and dams are the main sources of water storage for human consumption. The costs of extracting and using these water sources are lower than those of exploiting groundwater on transition zones to semi-arid terrains. However, the volume of surface water has decreased over time, with the depletion of first-order drainage and the disappearance of springs, phenomena which are easily observed in the field. Climate change worsens water scarcity, compromising supply and hydric security for rural populations. To minimize the expected impacts, producing and storing water through watershed management planning requires detailed cartographic information on the relief and topography, and updated data on the stage and intensity of catchment basin environmental degradation problems. The cartography available of the Brazilian northeastern territory dates to the 70s, with topographic maps, printed, at a scale of 1:100,000 which does not meet the requirements to execute this project. Exceptionally, there are topographic maps at scales of 1:50,000 and 1:25,000 of some coastal regions in northeastern Brazil. Still, due to scale limitations and outdatedness, they are products of little utility for mapping low-order watersheds drainage and springs. Remote sensing data and geographic information systems can contribute to guiding the process of mapping and environmental recovery by integrating detailed relief and topographic data besides social and other environmental information in the Guajiru River Basin, located on the east coast of Rio Grande do Norte, on the Northeast region of Brazil. This study aimed to recognize and map catchment basin, springs and low-order drainage features along estimating morphometric parameters. Alos PALSAR and Copernicus DEM digital elevation models were evaluated and provided regional drainage features and the watersheds limits extracted with Terraview/Terrahidro 5.0 software. CBERS 4A satellite images with 2 m spatial resolution, processed with ESA SNAP Toolbox, allowed generating land use land cover map of Guajiru River. A Mappir Survey 3 multiespectral camera onboard of a DJI Phantom 4, a Mavic 2 Pro PPK Drone and an X91 GNSS receiver to collect the precised position of selected points were employed to detail mapping. Satellite images enabled a first knowledge approach of watershed areas on a more regional scale, yet very current, and drone images were essential in mapping details of catchment basins. The drone multispectral image mosaics, the digital elevation model, the contour lines and geomorphometric parameters were generated using OpenDroneMap/ODM and QGis softwares. The drone images generated facilitated the location, understanding and mapping of watersheds, recharge areas and first-order ephemeral watercourses on an adequate scale and will be used in the following project’s phases: watershed management planning, recovery and environmental protection of Rio's springs Guajiru. Environmental degradation is being analyzed from the perspective of the availability and quality of surface water supply.

Keywords: imaging, relief, UAV, water

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Authors: Maryam Zahedifard, Fadhil Lafta Faraj, Nazia Abdul Majid, Hapipah Mohd Ali, Mahmood Ameen Abdulla

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The new quinazoline Schiff bases have been synthesized through condensation reaction of 2-aminobenzhydrazide with 5-bromosalicylaldehyde and 3-methoxy-5-bromosalicylaldehyde. The compound was investigated for anticancer activity against MCF-7 human breast cancer cell line. It demonstrated a remarkable antiproliferative effect, with an IC50 value of 3.41±0.34, after 72 hours of treatment. Most apoptosis morphological features in treated MCF-7 cells were observed by AO/PI staining. The results of cell cycle analysis indicate that compounds did not induce S and M phase arrest in cell after 24 hours of treatment. Furthermore, MCF-7 cells treated with compound subjected to apoptosis death, as exhibited by perturbation of mitochondrial membrane potential and cytochrome C release as well as increase in ROS generation. We also found activation of caspases 3/7 and -9. Moreover, acute toxicity results demonstrated the nontoxic nature of the compounds in mice. Our results showed the selected compound significantly induce apoptosis in MCF-7 cells via intrinsic pathway, which might be considered as a potential candidate for further in vivo and clinical breast cancer studies.

Keywords: quinazoline Schiff base, apoptosis, MCF-7, caspase, cell cycle, acute toxicity

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Authors: Kamalendu Pal

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This paper describes the main features of a knowledge-based system evaluation method. System evaluation is placed in the context of a hybrid legal decision-support system, Advisory Support for Home Settlement in Divorce (ASHSD). Legal knowledge for ASHSD is represented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the paper outlines the actual use of these forms in a computational framework that is designed to generate a plausible solution for a given case, by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of suitability assessment of a solution has been considered as a multiple criteria decision making process in ASHAD evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups. The answers to questionnaires used in this evaluations method have been measured as a combination of linguistic variables, fuzzy numbers, and by using defuzzification process. The results show that the designed evaluation method creates suitable mechanism in order to improve the performance of the knowledge-based system.

Keywords: case-based reasoning, fuzzy number, legal decision-support system, linguistic variable, rule-based reasoning, system evaluation

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Upal Barua

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Crash risk at intersections is a critical safety issue. This paper examines the effectiveness of removing an existing off-set at an intersection by realignment, in reducing crashes. Empirical Bayes method was applied to conduct a before-and-after study to assess the effect of this safety improvement. The Transportation Safety Improvement Program in Austin Transportation Department completed several safety improvement projects at high crash intersections with a view to reducing crashes. One of the common safety improvement techniques applied was the realignment of intersection approaches removing an existing off-set. This paper illustrates how this safety improvement technique is applied at a high crash intersection from inception to completion. This paper also highlights the significant crash reductions achieved from this safety improvement technique applying Empirical Bayes method in a before-and-after study. The result showed that realignment of intersection approaches removing an existing off-set can reduce crashes by 53%. This paper also features the state of the art techniques applied in planning, engineering, designing and construction of this safety improvement, key factors driving the success, and lessons learned in the process.

Keywords: crash risk, intersection, off-set, safety improvement technique, before-and-after study, empirical Bayes method

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Authors: Michael Alsop, Hannah Heitz, Prathiba Natesan Batley, Marion Hambrick, Jason Immekus

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The impacts of cultural engagement on individuals’ health and well-being have been well documented. The purposes of this study were to create an instrument to measure wellbeing constructs, including cultural wellbeing, and explore the relationships between cultural wellbeing and other wellbeing constructs (e.g., emotional, social, physical, spiritual). A sample of 358 participants attending concerts performed by a civic orchestra in the southeastern United States completed a questionnaire designed to measure eight wellbeing constructs. Split-half exploratory, confirmatory factor analyses resulted in the retention of four wellbeing constructs: general, emotional, financial, and cultural. Structural equation modeling showed statistically significant relationships between cultural wellbeing and other wellbeing constructs. In addition to the indirect effect of financial wellbeing on emotional and general wellbeing through cultural wellbeing, there were also direct statistically significant relationships (i.e., moderator). This highlights the importance of removing financial barriers to cultural engagement and the relationship between cultural wellbeing on emotional and general wellbeing. Additionally, the retained cultural wellbeing items focused primarily on community features, indicating the value of community-based cultural engagement opportunities.

Keywords: cultural wellbeing, cultural engagement, factor analysis, structural equation modeling

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Authors: Chothmal

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In this paper, we present a route discovery process which uses the signal strength on a link as a parameter of its inclusion in the route discovery method. The proposed signal-to-interference and noise ratio (SINR) based multipath reactive routing protocol is named as SINR-MP protocol. The proposed SINR-MP routing protocols has two following two features: a) SINR-MP protocol selects routes based on the SINR of the links during the route discovery process therefore it select the routes which has long lifetime and low frame error rate for data transmission, and b) SINR-MP protocols route discovery process is multipath which discovers more than one SINR based route between a given source destination pair. The multiple routes selected by our SINR-MP protocol are node-disjoint in nature which increases their robustness against link failures, as failure of one route will not affect the other route. The secondary route is very useful in situations where the primary route is broken because we can now use the secondary route without causing a new route discovery process. Due to this, the network overhead caused by a route discovery process is avoided. This increases the network performance greatly. The proposed SINR-MP routing protocol is implemented in the trail version of network simulator called Qualnet.

Keywords: ad hoc networks, quality of service, video streaming, H.264/SVC, multiple routes, video traces

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Authors: Chayanika Hazarika Bordoloi

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This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

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Authors: Spoorthi Sripad

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Remote sensing is a powerful tool for acquiring information about the Earth's surface without direct contact, relying on the interaction of electromagnetic radiation with various materials and features. This paper explores the fundamental principle of "Interaction with Earth's Surface" in remote sensing, shedding light on the intricate processes that occur when electromagnetic waves encounter different surfaces. The absorption, reflection, and transmission of radiation generate distinct spectral signatures, allowing for the identification and classification of surface materials. The paper delves into the significance of the visible, infrared, and thermal infrared regions of the electromagnetic spectrum, highlighting how their unique interactions contribute to a wealth of applications, from land cover classification to environmental monitoring. The discussion encompasses the types of sensors and platforms used to capture these interactions, including multispectral and hyperspectral imaging systems. By examining real-world applications, such as land cover classification and environmental monitoring, the paper underscores the critical role of understanding the interaction with the Earth's surface for accurate and meaningful interpretation of remote sensing data.

Keywords: remote sensing, earth's surface interaction, electromagnetic radiation, spectral signatures, land cover classification, archeology and cultural heritage preservation

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Authors: Flaviu Mihai Frigura-Lliasa, Andreea Anamaria Anghel, Attila Simo

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Due to the beauty, usefulness, science, constantly changing, constantly evolving features, and most of the time, mystery it involves, nature-based art is seen as a both modern and timeless direction that has been extensively used in design. The goal of the team's activities was to experiment with ways of fusing the two most common contemporary ways of referring to green installations, that is, either in a pure artistic or in an ecological manner, and creating a living, dynamic, interactive installation capable of both receiving and interpreting external factors, such as natural and human stimuli, that would not only determine some of the mechanism's presets. By consequent, a complex experiment made up of various research and project stages was elaborated in order to transform an idea into an actual interactive green installation within months thanks to the interaction, teamwork, and design processes undertaken throughout the academic years by both university lecturers and some of our students. The outcomes would lead to the development of a dynamic artwork called "Modgrew" as well as the introduction of experiment-based learning at the Timisoara Faculty of Architecture and Urban Planning, as well as at the Faculty of Electrical and Power Engineering, for the green wall automation issues.

Keywords: green design, living walls, modular structure, interactive proof of concept

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Authors: Jim Shi, Jasmine Chang, Nesreen El-Rayes

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The world has been experiencing a steadily increasing trend in both the production and consumption of plastic. The global consumer revolution should not have been possible without plastic, thanks to its salient feature of inexpensiveness and durability. But, as a two-edged sword, its durable quality has returned to haunt and even jeopardized us. That exacerbating the plastic crisis has attracted various global initiatives and actions. Simultaneously, firms are eager to adopt new technology as they witness and perceive more potential and merit of Industry 4.0 technologies. For example, Blockchain technology (BCT) is drawing the attention of numerous stakeholders because of its wide range of outstanding features that promise to enhance supply chain operations. However, from a research perspective, most of the literature addresses the plastic crisis from either environmental or social perspectives. In contrast, analysis from the data science perspective and technology is relatively scarce. To this end, this study aims to fill this gap and cover the plastic crisis from a holistic view of environmental, social, technological, and business perspectives. In particular, we propose a mathematical model to examine the inclusion of BCT to enhance and improve the efficiency on the upstream and the downstream sides of the plastic value, where the whole value chain is coordinated systematically, and its interoperability can be optimized. Consequently, the Environmental, Social, and Governance (ESG) goal and Circular Economics (CE) sustainability can be maximized.

Keywords: blockchain technology, plastic, circular economy, sustainability

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Authors: Suprabha Islam, Sifat Ullah Tanzil

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During past few decades, being amazed by the excellent silent flight of owl, scientists have been trying to demystify the unique features of its wing feathers. Our present study is dedicated to taking our understanding further on this phenomenon. In this present study, a numerical investigation was performed to analyze how the shape of the leading-edge serration at owl wing feathers effects the flow-induced noise generation. For the analysis, an owl inspired single feather wing model was prepared for both with and without serrations at the leading edge. The serration profiles were taken at different positions of the vane length for a single feather. The broadband noise was studied to quantify the local contribution to the total acoustic power generated by the flow, where the results clearly showed the effect of serrations in reducing the noise generation. It was also clearly visible that the shape of the serration has a very strong influence on noise generation. The frequency spectrum of noise was also analyzed and a strong relation was found between the shape of the serration and the noise generation. It showed that the noise suppression is strongly influenced by the height to length ratio of the serration. With the increase in height to length ratio, the noise suppression is enhanced further.

Keywords: aeroacoustics, aerodynamic, biomimetics, serrations

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Authors: G. Kloudova, M. Stehlik

Abstract:

Optimal human performance is a key goal in the professional setting of military pilots, which is a highly challenging atmosphere. The aviation environment requires substantial cognitive effort and is rich in potential stressors. Therefore, it is important to analyze variables such as mental workload to ensure safe conditions. Pilot mental workload could be measured using several tools, but most of them are very subjective. This paper details research conducted with military pilots using psychophysiological methods such as electroencephalography (EEG) and heart rate (HR) monitoring. The data were measured in a simulator as well as under real flight conditions. All of the pilots were exposed to highly demanding flight tasks and showed big individual response differences. On that basis, the individual pattern for each pilot was created counting different EEG features and heart rate variations. Later on, it was possible to distinguish the most difficult flight tasks for each pilot that should be more extensively trained. For training purposes, an application was developed for the instructors to decide which of the specific tasks to focus on during follow-up training. This complex system can help instructors detect the mentally demanding parts of the flight and enhance the training of military pilots to achieve optimal performance.

Keywords: cognitive effort, human performance, military pilots, psychophysiological methods

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Authors: Salahaeddine Sbai

Abstract:

Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Endri Rama, Genci Capi, Shigenori Kawahara

Abstract:

The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: brain-machine interface, decision-making, mobile robot, neural network

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Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

Abstract:

Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: attention-based fully convolutional network, optic disc detection and segmentation, retinal fundus image, screening of ocular diseases

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Authors: Anes Enakoa, Yawei Liang

Abstract:

Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

Procedia PDF Downloads 143