Search results for: forecast accuracy unemployment rate

Authors: Monika Rawat, Rahul Kumar

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Input output Buffer Information Specification (IBIS) models are used for describing the analog behavior of the Input Output (I/O) buffers of a digital device. They are widely used to perform signal integrity analysis. Advantages of using IBIS models include simple structure, IP protection and fast simulation time with reasonable accuracy. As design complexity of driver and receiver increases, capturing exact behavior from transistor level model into IBIS model becomes an essential task to achieve better accuracy. In this paper, an improvement in existing methodology of generating IBIS model for complex I/O interfaces such as Inter-Integrated Circuit (I2C) and Low Voltage Differential Signaling (LVDS) is proposed. Furthermore, the accuracy and computational performance of standard method and proposed approach with respect to SPICE are presented. The investigations will be useful to further improve the accuracy of IBIS models and to enhance their wider acceptance.

Keywords: IBIS, signal integrity, open-drain buffer, low voltage differential signaling, behavior modelling, transient simulation

Procedia PDF Downloads 187

Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Huseyin Arasli, Maryam Abdullahi, Tugrul Gunay

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One of the prominence elements used by the destination marketing organization (DMO) as a marketing strategy is the application of Social media tools. During the current spread of coronavirus disease (COVID-19), travel restriction was placed in most countries of the world, leading to the closure of borders movement. It should be noted that most tourism travelers depend on social media to obtain and exchange different kinds of information about COVID-19 in an unprecedented scale. The situational information people received is valued, which calls for the response of the tourism industry on the epidemic. Therefore, it is highly important to recognize such situational information and to understand how people spread this propaganda on social media platforms so that suitable information that relates the COVID-19 epidemic is available in a manner that will not tarnish the marketing strategies, festival planners. Data for this research study was collected from the desk review, which is a secondary source data, online blogs, and interview through social media chat. The results of this research show that the widespread of COVID-19 pandemics led to rapid lockdown in states and cities all over Nigeria, causing declining demands in hotels, airlines, recreation, and tourism centers. Additionally, billions of dollars lost has been recorded in the high increase of hotels and travel bookings cancellations which caused hundreds and thousands of job loss in the country. The result of this research also revealed that COVID-19 is causing more havoc on the unemployment rate indices of the country. Similarly, the over-dependence of government on petroleum has further caused considerable revenue loss, thereby raising a high poverty rate among less privileged Nigerians. Based on this result, the study suggested that there is an urgent need for the government to diversify its economy by looking at other different sectors such as tourism and agricultural farm produce to harmonize other commercial trades sectors in the country.

Keywords: social media, destination marketing organizations, DMOs, cultural COVID-19, coronavirus, hospitality, travel tour, tourism

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Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

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Authors: S. Reaz Ahmed, M. S. Kaiser

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The tribological behavior of commercially used Perspex was evaluated under dry and wet sliding condition using a pin-on-disc wear tester with different applied loads ranging from 2.5 to 20 N. Experiments were conducted with varying sliding distance from 0.2 km to 4.6 km, wherein the sliding velocity was kept constant, 0.64 ms^-1. The results reveal that the weight loss increases with applied load and the sliding distance. The nature of the wear rate was very similar in both the sliding environments in which initially the wear rate increased very rapidly with increasing sliding distance and then progressed to a slower rate. Moreover, the wear rate in wet sliding environment was significantly lower than that under dry sliding condition. The worn surfaces were characterized by optical microscope and SEM. It is found that surface modification has significant effect on sliding wear performance of Perspex.

Keywords: Perspex, wear, friction, SEM

Procedia PDF Downloads 267

Authors: Uzoma Emmanuel Igboji

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This paper examines the influence of public debts (external and internal) on economic growth and development in Nigeria from (1980-2015). The study uses aggregate GDP as a proxy for economic growth, per capital income as a proxy for standard of living and Government expenditure on health as a proxy for human capital development, while Foreign Direct Investment, Unemployment rate, and Oil revenue were used as control variables. The study made use of ex-post facto research design with the data extracted from the Central Bank of Nigeria (CBN) Statistical Bulletin and the World Bank database. It adopted a multiple regression analysis of the ordinary least square (OLS) method with the help of E-View version 3.0. The results revealed that external debt has a negative and insignificant effect on GDP, per capital income and human capital development. The study concluded that external debts were being channeled to meet the recurrent expenditures of the nation’s economy at the expense of productive investment that could stimulate growth and poverty alleviation. It, however, recommended that government should ensure that the bulk of the total borrowings are mostly sourced from within the domestic economy so that the repayment of the principal and interest will serve as a crowd in-effect rather that crowd out-effect which in turn further accelerates the country’s economic growth and development.

Keywords: economic growth, external debt, internal debt, Nigeria

Procedia PDF Downloads 247

Authors: Changyong Liu, Junda Tong, Feng Xu, Ninggui Huang

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In the injection molding process, the performance of cooling channels is crucial to the part quality. Through the application of conformal cooling channels fabricated via metal additive manufacturing, part distortion, warpage can be greatly reduced and cycle time can be greatly shortened. However, the properties of additively manufactured conformal cooling channels are quite different from conventional drilling processes such as the poorer dimensional accuracy and larger surface roughness. These features have significant influences on its cooling performance. In this study, test molds with the cooling channel diameters of φ2 mm, φ3 mm and φ4 mm were fabricated via selective laser melting and conventional drilling process respectively. A test system was designed and manufactured to measure the pressure difference between the channel inlet and outlet, the coolant flow rate and the temperature variation during the heating process. It was found that the cooling performance of SLM-fabricated channels was poorer than drilled cooling channels due to the smaller sectional area of cooling channels resulted from the low dimensional accuracy and the unmolten particles adhered to the channel surface. Theoretical models were established to determine the friction factor and heat transfer coefficient of SLM-fabricated cooling channels. These findings may provide guidance to the design of conformal cooling channels.

Keywords: conformal cooling channels, selective laser melting, cooling performance, injection molding

Procedia PDF Downloads 143

Authors: Nofiu A. Musa, Olubola Babalola

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The study explored the effect of ICT on the accuracy of Preliminary Cost Estimates (PCEs) prepared by quantity surveying consulting firms in Nigeria for building projects, with a view to determining the desirability of the adoption and use of the technological innovation for preliminary estimating. Thus, data pertinent to the study were obtained through questionnaire survey conducted on a sample of one hundred and eight (108) quantity surveying firms selected from the list of registered firms compiled by the Nigerian Institute of Quantity Surveyors (NIQS), Lagos State Chapter through systematic random sampling. The data obtained were analyzed with SPSS version 17 using student’s t-tests at 5% significance level. The results obtained revealed that the mean bias and co-efficient of variation of the PCEs of the firms are significantly less at post ICT adoption period than the pre ICT adoption period, F < 0.05 in each case. The paper concluded that the adoption and use of the Technological Innovation (ICT) has significantly improved the accuracy of the Preliminary Cost Estimates (PCEs) of building projects, hence, it is desirable.

Keywords: accepted tender price, accuracy, bias, building projects, consistency, information and communications technology, preliminary cost estimates

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Authors: Jarek Krajewski, David Daxberger

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We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Abubakar Salisu Garba

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The objective of the paper is to highlight the nature of entrepreneurial activities in the Sub Sahara Africa. Five countries in the Sub Sahara African that are participating in Global Entrepreneurship Monitor (GEM) research have been studied to understand the types of entrepreneurial activities and their socio-economic implications in the region. The importance of entrepreneurial activities in boosting socio-economic development has been recognized not only in developing countries, but across the entire global economies. Some people believe that the wealth and poverty of developing countries is associated with nature and type of entrepreneurial activity. Policy makers are not only concern about the rate of business start up, but the growth and development of those starts up is of paramount importance to the development of the country’s economy. Although, the supply of entrepreneurs is essential, sometimes it does not really matters in boosting economic performance. What is more important is having high impact entrepreneurs who could make meaningful contribution to the economy. High growth oriented entrepreneurs are more stable and contribute greatly in enhancing the economic performance. When entrepreneurs are facing difficulties in sustaining and growing their businesses, it may be unlikely for entrepreneurship to reduce unemployment and poverty. Inadequate financial supports, insufficient infrastructure, lack of enforcing laws protecting the right of entrepreneurs are some of the problems making business environment difficult in Sub-Saharan Africa.

Keywords: entrepreneurship, entrepreneurial activity, job creation, poverty reduction, Sub-Saharan Africa

Procedia PDF Downloads 413

Authors: Sanjida Ahmed Sinthia

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Bangladesh is one of the poorest countries in the world. There are several reasons for this insufficiency and uncontrolled population growth is one of the prime reasons. Others include low economic progress, imbalanced resource management, unemployment and underemployment, urban migration and natural catastrophes etc. As a result, the rate of urban poor is increasing inevitably in every sphere of urban cities in Bangladesh and Dhaka is the most affected one. Besides there is scarcity of urban land, housing, urban infrastructure and amenities which create pressure on urban cities and mostly encroach the open space, wetlands that causes environmental degradation. Government has no or limited control over these due to poor government policy and management, political pressure and lack of resource management. Unfortunately, over centralization and bureaucracy creates unnecessary delay and interruptions in any government initiations. There is also no coordination between government and private sector developer to solve the problem of urban Poor. To understand the problem of these huge populations this paper analyzes one of the single largest slum areas in Dhaka, Korail Slum. The study focuses on socio demographic analysis, morphological pattern and role of different actors responsible for the improvements of the area and recommended some possible steps for determining the potential outcomes.

Keywords: demographic analysis, environmental degradation, government policy, housing and land management policy

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

Procedia PDF Downloads 139

Authors: Misbahuddin Mahammad, P. Chandra Sekhar, Metuku Shyamsunder

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The oversampled output of a sigma-delta modulator is decimated to Nyquist sampling rate by decimation filters. The decimation filters work twofold; they decimate the sampling rate by a factor of OSR (oversampling rate) and they remove the out band quantization noise resulting in an increase in resolution. The speed, area and power consumption of oversampled converter are governed largely by decimation filters in sigma-delta A/D converters. The scope of the work is to design a decimation filter for sigma-delta ADC and simulation using MATLAB. The decimation filter structure is based on cascaded-integrated comb (CIC) filter. A second decimation filter is using CIC for large rate change and cascaded FIR filters, for small rate changes, to improve the frequency response. The proposed structure is even more hardware efficient.

Keywords: sigma delta modulator, CIC filter, decimation filter, compensation filter, noise shaping

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

Procedia PDF Downloads 143

Authors: Syed Toqueer, Omer Younus

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Terrorism, today, has emerged as a global menace with Pakistan being the most adversely affected state. Therefore, the motive behind this study is to empirically establish the linkage of terrorism with socio-economic (uneven income distribution, poverty and unemployment) and political nexuses so that a policy recommendation can be put forth to better approach this issue in Pakistan. For this purpose, the study employs two competing models, namely, the distributed lag model and OLS, so that findings of the model may be consolidated comprehensively, over the reference period of 1984-2012. The findings of both models are indicative of the fact that uneven income distribution of Pakistan is rather a contributing factor towards terrorism when measured through GDP per capita. This supports the hypothesis that immiserizing modernization theory is applicable for the state of Pakistan where the underprivileged are marginalized. Results also suggest that other socio-economic variables (poverty, unemployment and consumer confidence) can condense the brutality of terrorism once these conditions are catered to and improved. The rational of opportunity cost is at the base of this argument. Poor conditions of employment and poverty reduces the opportunity cost for individuals to be recruited by terrorist organizations as economic returns are considerably low and thus increasing the supply of volunteers and subsequently increasing the intensity of terrorism. The argument of political freedom as a means of lowering terrorism stands true. The more the people are politically repressed the more alternative and illegal means they will find to make their voice heard. Also, the argument that politically transitioning economy faces more terrorism is found applicable for Pakistan. Finally, the study contributes to an ongoing debate on which of the two set of factors are more significant with relation to terrorism by suggesting that socio-economic factors are found to be the primary causes of terrorism for Pakistan.

Keywords: terrorism, socioeconomic conditions, political freedom, distributed lag model, ordinary least square

Procedia PDF Downloads 318

Authors: Md Abdullah Al Mashud, M. Tariquzzaman, M. Jahangir Alam, Tapan Kumar Godder, M. Mahbubur Rahman

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This paper presents a Monte Carlo (MC) method-based dose distributions on lung tumor for 6 MV photon beam to improve the dosimetric accuracy for cancer treatment. The polystyrene which is tissue equivalent material to the lung tumor density is used in this research. In the empirical calculations, TRS-398 formalism of IAEA has been used, and the setup was made according to the ICRU recommendations. The research outcomes were compared with the state-of-the-art experimental results. From the experimental results, it is observed that the proposed based approach provides more accurate results and improves the accuracy than the existing approaches. The average %variation between measured and TPS simulated values was obtained 1.337±0.531, which shows a substantial improvement comparing with the state-of-the-art technology.

Keywords: lung tumour, Monte Carlo, polystyrene, Elekta synergy, Monaco planning system

Procedia PDF Downloads 434

Authors: Madeleine Nicole G. Bernardo, Katrina T. Feliciano, Marcelo Nonato A. Nacionales III, Diane Frances M. Peralta, Denise Nicole V. Profeta

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This study aims to find out if heart rate variability and valence of emotion have an effect on perceived intensity of emotion. Psychology undergraduates (N = 60) from the University of the Philippines Diliman were shown 10 photographs from the Japanese Female Facial Expression (JAFFE) Database, along with a corresponding questionnaire with a Likert scale on perceived intensity of emotion. In this 3 x 2 mixed subjects factorial design, each group was either made to do a simple exercise prior to answering the questionnaire in order to increase the heart rate, listen to a heart rate of 120 bpm, or colour a drawing to keep the heart rate stable. After doing the activity, the participants then answered the questionnaire, providing a rating of the faces according to the participants’ perceived emotional intensity on the photographs. The photographs presented were either of positive or negative emotional valence. The results of the experiment showed that neither an induced fast heart rate or perceived fast heart rate had any significant effect on the participants’ perceived intensity of emotion. There was also no interaction effect of heart rate variability and valence of emotion. The insignificance of results was explained by the Philippines’ high context culture, accompanied by the prevalence of both intensely valenced positive and negative emotions in Philippine society. Insignificance in the effects were also attributed to the Cannon-Bard theory, Schachter-Singer theory and various methodological limitations.

Keywords: heart rate variability, perceived intensity of emotion, Philippines , valence of emotion

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

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Authors: Manjit Singh

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Multiscale entropy (MSE) is an extensively used index to provide a general understanding of multiple complexity of physiologic mechanism of heart rate variability (HRV) that operates on a wide range of time scales. Accurate selection of electrocardiogram (ECG) sampling frequency is an essential concern for clinically significant HRV quantification; high ECG sampling rate increase memory requirements and processing time, whereas low sampling rate degrade signal quality and results in clinically misinterpreted HRV. In this work, the impact of ECG sampling frequency on MSE based HRV have been quantified. MSE measures are found to be sensitive to ECG sampling frequency and effect of sampling frequency will be a function of time scale.

Keywords: ECG (electrocardiogram), heart rate variability (HRV), multiscale entropy, sampling frequency

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Badache Messaoud

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Geothermal thermosyphons (GTs) are increasingly used in many heating and cooling geothermal applications owing to their high heat transfer performance. This paper proposes a response surface methodology (RSM) to investigate and optimize the performance of a CO2 geothermal thermosyphon. The filling ratio (FR), temperature, and flow rate of the heat transfer fluid are selected as the designing parameters, and heat transfer rate and effectiveness are adopted as response parameters (objective functions). First, a dedicated experimental GT test bench filled with CO2 was built and subjected to different test conditions. An RSM was used to establish corresponding models between the input parameters and responses. Various diagnostic tests were used to assess evaluate the quality and validity of the best-fit models, which explain respectively 98.9% and 99.2% of the output result’s variability. Overall, it is concluded from the RSM analysis that the heat transfer fluid inlet temperatures and the flow rate are the factors that have the greatest impact on heat transfer (Q) rate and effectiveness (εff), while the FR has only a slight effect on Q and no effect on εff. The maximal heat transfer rate and effectiveness achieved are 1.86 kW and 47.81%, respectively. Moreover, these optimal values are associated with different flow rate levels (mc level = 1 for Q and -1 for εff), indicating distinct operating regions for maximizing Q and εff within the GT system. Therefore, a multilevel optimization approach is necessary to optimize both the heat transfer rate and effectiveness simultaneously.

Keywords: geothermal thermosiphon, co2, Response surface methodology, heat transfer performance

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Authors: Baso Hamdani, Arniati Massinai, Jamaluddin Jompa

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Coral diseases are one of the factors affect reef degradation. This research had analysed the progression rate, incidence, and prevalence of Black Band Disease (BBD) on stony coral (Pachyseris sp.) in relation to the environmental parameters (pH, nitrate, phospate, Dissolved Organic Matter (DOM), and turbidity). The incidence of coral disease was measured weekly for 6 weeks using Belt Transect Method. The progression rate of BBD was measured manually. Furthermore, the prevalence and incidence of BBD were calculated each colonies infected. The relationship between environmental parameters and the progression rate, prevalence and incidence of BBD was analysed by Principal Component Analysis (PCA). The results showed the average of progression rate is 0,07 ± 0,02 cm/ hari. The prevalence of BBD increased from 0,92% - 19,73% in 7 weeks observation with the average incidence of new infected colonies coral 0,2 - 0,65 colony/day The environment factors which important were pH, Nitrate, Phospate, DOM, and Turbidity.

Keywords: progression rate, incidence, prevalence, Black Band Disease, Barranglompo

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Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

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In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

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Authors: Gbenga Martins, Christopher J. Olowookere, Lateef Bamidele, Kehinde O. Olatunji

Abstract:

The aims of this research are to measure the scattered dose rate during an X-ray examination in an X-ray room, compare the scattered dose rate with exposure parameters based on the body region examined, and examine the X-ray examination done with an over couch tube. The research was carried out using Gamma Scout software installation on the computer system (Laptop) to record the radiation counts, pulse rate, and dose rate. The measurement was employed by placing the detector at 900 to the incident X-ray. Proforma was used for the collection of patients’ data such as age, sex, examination type, and initial diagnosis. Data such as focus skin distance (FSD), body mass index (BMI), body thickness of the patients, the beam output (kVp) were collected at Obafemi Awolowo University, Ile-Ife, Western Nigeria. Total number of 136 patients was considered during this research. Dose rate range between 14.21 and 86.78 µSv/h for the plain abdominal region, 85.70 and 2.86 µSv/h for the lumbosacral region,1.3 µSv/yr and 3.6 µSv/yr in the pelvis region, 2.71 µSv/yr and 28.88 µSv/yr for leg region, 3.06 µSv/yr and 29.98 µSv/yr in hand region. The results of this study were compared with those of other studies carried out in other countries. The findings of this study indicated that the number of exposure parameters selected for each diagnostic examination contributed to the dose rate recorded. Therefore, these results call for a quality assurance program (QAP) in diagnostic X-ray units in Nigerian hospitals.

Keywords: X-radiation, exposure parameters, dose rate, pulse rate, number of counts, tube current, tube potential, diagnostic examination, scattered radiation

Procedia PDF Downloads 106

Authors: Indrajeet Singh, Abhishek Gandhi, Smita Mohanty, S. K. Nayak

Abstract:

In this article, a focused study has been performed to comprehend the influence of change in depressurization rate on microcellular polycarbonate foamed morphological attributes. The depressurization rate considered in this study were 0.5, 0.05, 0.01 and 0.005 MPa/sec and the physical blowing agent utilized was carbon dioxide owing to its high solubility in polycarbonate at room temperature. The study was performed on two distinct saturation pressures, i.e., 3 MPa and 6 MPa to understand if saturation pressure has any effects on it. It is reported that with increase in depressurization rate, a higher amount of thermodynamic instability was induced which resulted in generation of larger number of smaller sized cells. This article puts forward an understanding of how depressurization rate control could be well exploited during the batch foaming process to develop high quality microcellular foamed products with exceedingly well controlled cell size.

Keywords: depressurization, porous polymer, foaming, microcellular

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Authors: Roop Kiran, Muhammad Shoaib Zafar, Nazish Idrees Chaudhary

Abstract:

Depression is known to be the second most frequently occurring comorbid mental illness among patients suffering from chronic physical conditions. Around the world, depression is associated with chronic liver diseases as one of the dominant symptoms. This evidence brings attention to the research about various predictors for short life expectancy and poor quality of life in patients suffering from comorbid depression and CLD. Following are the objectives of this study i) measure the occurrence rate of comorbid depression among patients with CLD and ii) find the frequency of risk factors between patients with and without depression comorbid with CLD. This is a quantitative study with a cross-sectional design. The research data was collected through a measure called Hamilton Depression Rating Scale (HDRS) with a demographic Performa from 100 patients who visited the Department of Psychiatry for consultation at Mayo Hospital Lahore with a diagnosed CLD from the last four years. There were (42%) patients with CLD who had comorbid depression. Among depressed and non-depressed patients, significant differences were found (p<0.05) for unemployment in 25 (59.5%) males and 20 (34.5%) female patients, for co-morbidity in 25 (59.5%) males and 18 (31.0%) female patients, for illiteracy in 18 (42.9%) males and 13 (22.4%) female patients, for the history of CLD for more than the last 2years in 41 (97.6%) males and 47 (81.0%) female patients, for severity of CLD in 26 (61.9%) males and 20 (34.5%) female patients. This concludes that depression frequently occurs among patients with CLD. This study recommends considerable attention to plan preventative measures in the future and develop such intervention protocols that consider the management of risk factors that significantly influence comorbid depression with CLD.

Keywords: psychiatry, comorbid, health, quality of life

Procedia PDF Downloads 194

Authors: Hasan Basri Jumin

Abstract:

Application of crude palm oil waste combined to suitable concentration of benzyl-adenine give the significant effect to mean relative growth rate of vegetable plants and the same pattern in net assimilation rate crude palm oil waste has also significantly increased during 28 days old plants. Combination of treatment of suitable concentration of crude palm oil and benzyl adenine increased the growth and production of vegetable plants. The relative growth rate of vegetable plants was rapid 3 weeks after planting and gradually decreased at the end of the harvest time period. Combination of 400 mg.l-1 CPO with 1.0 mgl-1 till 10mgl-1 BA increased the Mean Relative Growth Rate (MRGR), Net assimilation rate (NAR), Leaf area and dry weight of Brassica juncea, Brassica oleraceae and Lactuca sativa.

Keywords: benzyladenine, crude-palm-oil, nutrient, vegetable, waste

Procedia PDF Downloads 185