Search results for: data driven diagnosis

Authors: Yu. I. Pigolkin, M. A. Garcia Corro

Abstract:

Age determination of unknown dead bodies in forensic personal identification is a complicated process which involves the application of numerous methods and techniques. Skeletal remains are less exposed to influences of environmental factors. In order to enhance the accuracy of forensic age estimation additional properties of bones correlating with age are required to be revealed. Material and Methods: Dimensional examination of the sella turcica was carried out on cadavers with the cranium opened by a circular vibrating saw. The sample consisted of a total of 90 Russian subjects, ranging in age from two months and 87 years. Results: The tendency of dimensional variations throughout life was detected. There were no observed gender differences in the morphometry of the sella turcica. The shared use of the sella turcica depth and length values revealed the possibility to categorize an examined sample in a certain age period. Conclusions: Based on the results of existing methods of age determination, the morphometry of the sella turcica can be an additional characteristic, amplifying the received values, and accordingly, increasing the accuracy of forensic biological age diagnosis.

Keywords: age–related changes in bone structures, forensic personal identification, sella turcica morphometry, body identification

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Authors: Irum Matloob, Shoab Ahmad Khan, Fahim Arif

Abstract:

As Indo-Pak has been the victim of heart diseases since many decades. Many surveys showed that percentage of cardiac patients is increasing in Pakistan day by day, and special attention is needed to pay on this issue. The framework is proposed for performing detailed analysis of ECG survey data which is conducted for measuring prevalence of heart diseases statistics in Pakistan. The ECG survey data is evaluated or filtered by using automated Minnesota codes and only those ECGs are used for further analysis which is fulfilling the standardized conditions mentioned in the Minnesota codes. Then feature selection is performed by applying proposed algorithm based on discernibility matrix, for selecting relevant features from the database. Clustering is performed for exposing natural clusters from the ECG survey data by applying spectral clustering algorithm using fuzzy c means algorithm. The hidden patterns and interesting relationships which have been exposed after this analysis are useful for further detailed analysis and for many other multiple purposes.

Keywords: arrhythmias, centroids, ECG, clustering, discernibility matrix

Procedia PDF Downloads 462

Authors: Pratistha Shrestha

Abstract:

Background: Acrochordons (Skin tags) are common benign skin tumors usually occurring on the neck and major flexors of older people. These range in size from 1 mm to 1cm in diameter and are skin-colored or brownish. A possible association with diabetes mellitus has been suggested in previous studies, but the result is not conclusive. Objective: The aim of this study was to find out the association of diabetes mellitus with acrochordons. Material and Methods: One hundred and two patients were selected for the study. Among them, 51 (males–23 and females–28) with acrochordons were taken as cases, and 51 with other dermatologic diseases after matching age and sex were taken as controls. The patients were selected from OPD of the Department of Dermatology and Venereology in Universal College of Medical Sciences–Teaching Hospital (UCMS-TH). Blood glucose levels, including both fasting plasma glucose and 2-hour post-glucose load, were determined for both case and control and compared. Results: Patients with acrochordons had a significantly higher frequency of diabetes than the control group (p < 0.001). A total of 48.5% and 40% of patients with acrochordons having diabetes were obese and overweight, respectively. Conclusion: There is an increased risk of diabetes mellitus in patients with acrochordons. With regard to the importance of early diagnosis of diabetes, it is recommended a high level of suspicion for diabetes mellitus in patients with acrochordons.

Keywords: acrochordons, diabetes mellitus, obesity, skin tags

Procedia PDF Downloads 146

Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

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Authors: Samir Roy

Abstract:

This paper investigates the relationship among knowledge, technology, and empowerment. In Physics power is defined as rate of doing work. In everyday use, the meaning of the word power is related to the capacity to bring change of value in the world. It appears that the popular aphorism “Knowledge is power” should be revisited in the context of contemporary states of affairs. For instance, classical mechanics is a system of knowledge, so also thermodynamics. But neither of them, per se, is sufficient to produce automobilin es. Boolean algebra, the logical foundation of digital electronic computers, was introduced by George Boole in 1847. But that knowledge was practically useless for almost one hundred years until digital electronics was developed in early twentieth century, which eventually led to invention of digital electronic computers. Empowerment of women is a burning issue in the arena of social justice. However, if we carefully analyze the functional elements of women’s empowerment, we find them to be highly technology driven as well as technology dependent in real life. On the other hand, technology has empowered modern states to maintain social order and promote democracy in an effective manner. This paper includes a few case studies to establish the close correspondence between knowledge, especially scientific knowledge, technology, and empowerment. It appears that in contemporary scenario, “Technology is power” is a more appropriate statement than the traditional aphorism “Knowledge is power”.

Keywords: knowledge, science, technology, empowerment, change, social justice

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Authors: T. Penkova, A. Korobko, V. Nicheporchuk, L. Nozhenkova, A. Metus

Abstract:

This paper presents an approach of on-line control of the state of technosphere and environment objects based on the integration of Data Warehouse, OLAP and Expert systems technologies. It looks at the structure and content of data warehouse that provides consolidation and storage of monitoring data. There is a description of OLAP-models that provide a multidimensional analysis of monitoring data and dynamic analysis of principal parameters of controlled objects. The authors suggest some criteria of emergency risk assessment using expert knowledge about danger levels. It is demonstrated now some of the proposed solutions could be adopted in territorial decision making support systems. Operational control allows authorities to detect threat, prevent natural and anthropogenic emergencies and ensure a comprehensive safety of territory.

Keywords: decision making support systems, emergency risk assessment, natural and anthropogenic safety, on-line control, territory

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Authors: Sanele Lionel Khanyile, Emmanuel Nahayo

Abstract:

Geomagnetic jerks are jumps observed in the second derivative of the main magnetic field that occurs on annual to decadal timescales. Understanding these jerks is crucial as they provide valuable insights into the complex dynamics of the Earth’s outer liquid core. In this study, we investigate the occurrence of geomagnetic jerks in geomagnetic observatory data collected at southern African magnetic observatories, Hermanus (HER), Tsumeb (TSU), Hartebeesthoek (HBK) and Keetmanshoop (KMH) between 2017 and 2023. The observatory data was processed and analyzed by retaining quiet night-time data recorded during quiet geomagnetic activities with the help of Kp, Dst, and ring current RC indices. Results confirm the occurrence of the 2019-2020 geomagnetic jerk in the region and identify the recent 2021 jerk detected with V-shaped secular variation changes in X and Z components at all four observatories. The highest estimated 2021 jerk secular acceleration amplitudes in X and Z components were found at HBK, 12.7 nT/year² and 19. 1 nT/year², respectively. Notably, the global CHAOS-7 model aptly identifies this 2021 jerk in the Z component at all magnetic observatories in the region.

Keywords: geomagnetic jerks, secular variation, magnetic observatory data, South Atlantic Anomaly

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Authors: Winnie Kung, Xinhua Liu, Debbie Huang, Patricia Kim, Keon Kim, Xiaoran Wang, Lawrence Yang

Abstract:

Considerable Asian Americans were exposed to the World Trade Center attack due to the proximity of the site to Chinatown and a sizeable number of South Asians working in the collapsed and damaged buildings nearby. Few studies focused on Asians in examining the disaster’s mental health impact, and even less longitudinal studies were reported beyond the first couple of years after the event. Based on the World Trade Center Health Registry, this study examined the trajectory of PTSD of individuals directly exposed to the attack from 2-3 to 5-6 years after the attack, comparing Asians against the non-Hispanic White group. Participants included 2,431 Asians and 31,455 Whites. Trajectories were delineated into the resilient, chronic, delayed-onset and remitted groups using PTSD checklist cut-off score at 44 at the 2 waves. Logistic regression analyses were conducted to compare the poorer trajectories against the resilient as a reference group, using predictors of baseline sociodemographic, exposure to the disaster, lower respiratory symptoms and previous depression/anxiety disorder diagnosis, and recruitment source as the control variable. Asians had significant lower socioeconomic status in terms of income, education and employment status compared to Whites. Over 3/4 of participants from both races were resilient, though slightly less for Asians than Whites (76.5% vs 79.8%). Asians had a higher proportion with chronic PTSD (8.6% vs 7.4%) and remission (5.9% vs 3.4%) than Whites. A considerable proportion of participants had delayed-onset in both races (9.1% Asians vs 9.4% Whites). The distribution of trajectories differed significantly by race (p<0.0001) with Asians faring poorer. For Asians, in the chronic vs resilient group, significant protective factors included age >65, annual household income >$50,000, and never married vs married/cohabiting; risk factors were direct disaster exposure, job loss due to 9/11, lost someone, and tangible loss; lower respiratory symptoms and previous mental disorder diagnoses. Similar protective and risk factors were noted for the delayed-onset group, except education being protective; and being an immigrant a risk. Between the 2 comparisons, the chronic group was more vulnerable than the delayed-onset as expected. It should also be noted that in both comparisons, Asians’ current employment status had no significant impact on their PTSD trajectory. Comparing between Asians against Whites, the direction of the relationships between the predictors and the PTSD trajectories were mostly the same, although more factors were significant for Whites than for Asians. A few factors showed significant racial difference: Higher risk for lower respiratory symptoms for Whites than Asians, higher risk for pre-9/11 mental disorder diagnosis for Asians than Whites, and immigrant a risk factor for the remitted vs resilient groups for Whites but not for Asians. Over 17% Asians still suffered from PTSD 5-6 years after the WTC attack signified its persistent impact which incurred substantial human, social and economic costs. The more disadvantaged socioeconomic status of Asians rendered them more vulnerable in their mental health trajectories relative to Whites. Together with their well-documented low tendency to seek mental health help, outreach effort to this population is needed to ensure follow-up treatment and prevention.

Keywords: PTSD, Asian Americans, World Trade Center Attack, racial differences

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Authors: Ines Zemni, Maher Slimane, Jamel Ben Hassouna, Khaled Rahal

Abstract:

Background: Neurofibromatosis type 1 (NF1) is a genetic disease, which is associated with an increased risk of developing different malignancies including breast cancer. The association between NF1 band breast sarcoma is a rare entity. Herein we present a 25-year-old woman with NF1 who had fibrosarcoma of the left breast. Case presentation: The patient has multiple thoraco-abdominal 'café au lait' spots. Clinical examination showed a lump of the left breast measuring 9 cm of diameter, which was noticed for 6 months. There was a left inguinal mass of 6 cm of diameter. The patient underwent first a left lumpectomy. Histopathological exam revealed a high-grade fibrosarcoma of the left breast measuring 7.5 cm. Three months later, the patient underwent a left mastectomy and excision of the inguinal mass, which was a neurofibroma. An adjuvant chemotherapy and radiation therapy were indicated, but not applied because of the timeout. The patient is now alive after a follow up of 6 years, with no loco-regional recurrence or metastasis. Conclusion: The relationship between NF1 and breast cancer need to be more clarified by further studies. Establishing a specific screening program of these patients may help to make an earlier diagnosis of breast cancer.

Keywords: neurofibromatosis, breast, sarcoma, cancer

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Authors: Angelis P. Barlampas

Abstract:

An 89 years old woman came to the emergency department complaining of long-lasting headaches and nausea. A CT examination was performed, and a homogeneous midline anterior cranial fossa lesion was revealed, which was situated near the base and measured 2,4 cm in diameter. The patient was allergic, and an i.v.c injection could not be done on the spot, and neither could an MRI exam because of metallic implants. How could someone narrow down the differential diagnosis? The interhemispheric meningioma is usually a silent midline lesion with no edema, and most often presents as a homogeneous, solid type, isodense, or slightly hyperdense mass ( usually the smallest lesions as this one ). Of them, 20-30% have some calcifications. Hyperostosis is typical for meningiomas that abut the base of the skull but is absent in the current case, presumably of a more cephalad location that is borderline away from the bone. Because further investigation could not be done, as the patient was allergic to the contrast media, some other differential options should be considered. Regarding the site of the lesion, the most common other entities to keep in mind are the following: Metastasis, tumor of skull base, abscess, primary brain tumors, meningioma, giant aneurysm of the anterior cerebral artery, olfactory neuroblastoma, interhemispheric meningioma, giant aneurysm of the anterior cerebral artery, midline lesion. Appearance will depend on whether the aneurysm is non-thrombosed, or partially, or completely thrombosed. Non-contrast: slightly hyperdense, well-defined round extra-axial mass, may demonstrate a peripheral calcified rim, olfactory neuroblastoma, midline lesion. The mass is of soft tissue attenuation and is relatively homogeneous. Focal calcifications are occasionally present. When an intracranial extension is present, peritumoral cysts between it and the overlying brain are often present. Final diagnosis interhemispheric meningioma (Known from the previous patient’s history). Meningiomas come from the meningocytes or the arachnoid cells of the meninges. They are usually found incidentally, have an indolent course, and their most common location is extra-axial, parasagittal, and supratentorial. Other locations include the sphenoid ridge, olfactory groove, juxtasellar, infratentorial, intraventricular, pineal gland area, and optic nerve meningioma. They are clinically silent entities, except for large ones, which can present with headaches, changes in personality status, paresis, or symptomatology according to their specific site and may cause edema of the surrounding brain tissue. Imaging findings include the presence of calcifications, the CSF cleft sign, hyperostosis of adjacent bone, dural tail, and white matter buckling sign. After i.v.c. injection, they enhance brightly and homogenously, except for large ones, which may exhibit necrotic areas or may be heavily calcified. Malignant or cystic variants demonstrate more heterogeneity and less intense enhancement. Sometimes, it is inevitable that the needed CT protocol cannot be performed, especially in the emergency department. In these cases, the radiologist must focus on the characteristic imaging features of the unenhanced lesion, as well as in previous examinations or a known lesion history, in order to come to the right report conclusion.

Keywords: computed tomography, emergency radiology, metastasis, tumor of skull base, abscess, primary brain tumors, meningioma, giant aneurysm of the anterior cerebral artery, olfactory neuroblastoma, interhemispheric meningioma

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Authors: Marek Dosbaba

Abstract:

Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Veronique Jubera, Ka-Young Kim, U-Chan Chung, Amelie Veillere, Jean-Marc Heintz

Abstract:

Emitting materials and all solid state lasers are widely used in the field of optical applications and materials science as a source of excitement, instrumental measurements, medical applications, metal shaping etc. Recently promising optical efficiencies were recorded on ceramics which result from a cheaper and faster ways to obtain crystallized materials. The choice and optimization of the sintering process is the key point to fabricate transparent ceramics. It includes a high control on the preparation of the powder with the choice of an adequate synthesis, a pre-heat-treatment, the reproducibility of the sintering cycle, the polishing and post-annealing of the ceramic. The densification is the main factor needed to reach a satisfying transparency, and many technologies are now available. The symmetry of the unit cell plays a crucial role in the diffusion rate of the material. Therefore, the cubic symmetry compounds having an isotropic refractive index is preferred. The cubic Y3NbO7 matrix is an interesting host which can accept a high concentration of rare earth doping element and it has been demonstrated that SPS is an efficient way to sinter this material. The optimization of diffusion losses requires a microstructure of fine ceramics, generally less than one hundred nanometers. In this case, grain growth is not an obstacle to transparency. The ceramics properties are then isotropic thereby to free-shaping step by orienting the ceramics as this is the case for the compounds of lower symmetry. After optimization of the synthesis route, several SPS parameters as heating rate, holding, dwell time and pressure were adjusted in order to increase the densification of the Eu3+ doped Y3NbO7 pellets. The luminescence data coupled with X-Ray diffraction analysis and electronic diffraction microscopy highlight the existence of several distorted environments of the doping element in the studied defective fluorite-type host lattice. Indeed, the fast and high crystallization rate obtained to put in evidence a lack of miscibility in the phase diagram, being the final composition of the pellet driven by the ratio between niobium and yttrium elements. By following the luminescence properties, we demonstrate a direct impact on the SPS process on this material.

Keywords: emission, niobate of rare earth, Spark plasma sintering, lack of miscibility

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Authors: Jayati Das

Abstract:

Kolkata, one of the fastest-growing metropolises in India, has been suffering from air pollution for many decades. Mismanagement of government and an increase in automobiles have been fuelling this problem. The study aims to portray the quality of air along with the influence of traffic flow and vehicular growth and the effects on human health. It further shows the correlation between the emission of pollution during weekdays and weekends with the help of a scatter diagram and trend line. An assessment of Kolkata air quality is done where the listed pollutants’ (RPM, SPM, NO2, and SO2) annual average concentrations are classified into four different categories. Our observed association between childhood Acute Respiratory disorder and early life exposure to traffic-related air pollutants is biologically plausible. The period of in utero and the first year of life is critical in the development of the immune and respiratory systems and potentially harmful effects of toxic pollutants during this period might result in the long-lasting impaired capacity to fight infections and increased risk of allergic manifestations. Up-to-date knowledge about the seasonal and spatial variation of asthma and studying the air quality of the area is done through Geographical Information System (GIS). Steps are taken by the government to control air pollution by alternative public transport like the metro and compulsory certification of period-driven vehicles which test for Carbon mono oxide.

Keywords: air pollution, asthma, GIS, hotspots, governance

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Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

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Authors: Annabelle Onyefulu-Kingston

Abstract:

One of the major purposes of AI today is to evaluate and analyze millions of micro and macro data in order to determine what is relevant in a particular case and proffer it in an adequate manner. Microdata, as far as it relates to AI in international arbitration, is the millions of key issues specifically mentioned by either one or both parties or by their counsels, arbitrators, or arbitral tribunals in arbitral proceedings. This can be qualifications of expert witness and admissibility of evidence, amongst others. Macro data, on the other hand, refers to data derived from the resolution of the dispute and, consequently, the final and binding award. A notable example of this includes the rationale of the award and specific and general damages awarded, amongst others. This paper aims to critically evaluate and analyze the possibility of technological inclusion in international arbitration. This research will be imploring the qualitative method by evaluating existing literature on the consequence of applying AI to both micro and macro data in international arbitration, and how this can be of assistance to parties, counsels, and arbitrators.

Keywords: AI-based technologies, algorithms, arbitrators, international arbitration

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Authors: Siddhartha Chauhan, Nitin Kumar Kotania

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Traditional Wireless Sensor Networks (WSNs) generally use static sinks to collect data from the sensor nodes via multiple forwarding. Therefore, network suffers with some problems like long message relay time, bottle neck problem which reduces the performance of the network. Many approaches have been proposed to prevent this problem with the help of mobile sink to collect the data from the sensor nodes, but these approaches still suffer from the buffer overflow problem due to limited memory size of sensor nodes. This paper proposes an energy efficient scheme for data gathering which overcomes the buffer overflow problem. The proposed scheme creates virtual grid structure of heterogeneous nodes. Scheme has been designed for sensor nodes having variable sensing rate. Every node finds out its buffer overflow time and on the basis of this cluster heads are elected. A controlled traversing approach is used by the proposed scheme in order to transmit data to sink. The effectiveness of the proposed scheme is verified by simulation.

Keywords: buffer overflow problem, mobile sink, virtual grid, wireless sensor networks

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Authors: Leticia Lengler, Jefferson Oliveira, Vania Estivalete, Jordana Marques Kneipp

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The context of social, economic and environmental transformations has driven innumerable changes in the organizational environment, influencing the social interactions that occur in this scenario. In this sense, social entrepreneurship emerges as a unique opportunity to challenge, question, rethink certain concepts and traditional theories widely discussed in relation to entrepreneurship. Therefore, the interest in studying personal values has been based on the idea that they might be predictors of the behavior of individuals. As an attempt to relate personal values with the characteristics of social entrepreneurs, this study aims to investigate the salient values and the social entrepreneurship perceptions that occur in two social projects responsible for developing sports skills among the students. For purposes of analysis, it is intended to consider: (i) a description of both Social Projects and their respective institutions, considering their history and relevance in the context; (ii) analysis of the personal values of the idealizers and teachers responsible for the projects, (iii) identification of the characteristics of social entrepreneurship manifested in the two projects, and (iv) discussion of similarities and disparities of the categories identified among the participants of the projects. Therefore, this study will carry a qualitative analysis from the interviews with 10 participants of each social project (named Projeto Remar/ASENA and Projeto Mãos Dadas/JUDÔ SANTA MARIA): 2 projects coordinators, 2 students, 2 parents of students, 2 physical education internships and 2 businessmen who stablished a partnership with each project. The data collection will be done through semi-structured interviews that are going to last around 30 minutes each, being recorded, transcribed and later analyzed, through the categorical analysis. The option for categorical analysis is supported by the fact that it is the best alternative when one wants to study values, opinions, attitudes and beliefs, through qualitative ones. In the present research, the pre-analysis phase consisted of an organization of the material collected during the research with Remar and Mãos Dadas Project, and a dynamic reading of this material, seeking to identify the characteristics of social entrepreneurship and values addressed in the study. In the analytical description phase, a more in-depth analysis of the material collected in the research will be carried out. The third phase, referred to as referential interpretation or treatment of results obtained will allow to verify the homogeneity and the heterogeneity among the participants' perceptions of the projects. Some preliminary results coming from the first interviews revealed the projects are guided by values such as cooperation, respect, well-being and nature preservation. These values are linked to the social entrepreneurship perception of the projects managers, who established their activities in behalf of the local community.

Keywords: personal values, social entrepreneurship, social projects, sports participants

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Authors: Ghulam Haider Haidaree, Nsenda Lukumwena

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Today the world of research enjoys abundant data, available in virtually any field, technology, science, and business, politics, etc. This is commonly referred to as big data. This offers a great deal of precision and accuracy, supportive of an in-depth look at any decision-making process. When and if well used, Big Data affords its users with the opportunity to produce substantially well supported and good results. This paper leans extensively on big data to investigate possible smart solutions to urban mobility and related issues, namely road traffic accidents, its casualties, and fatalities based on multiple factors, including age, gender, location occurrences of accidents, etc. Multiple technologies were used in combination to produce an Information Communication Technology (ICT) based solution with embedded technology. Those technologies include principally Geographic Information System (GIS), Orange Data Mining Software, Bayesian Statistics, to name a few. The study uses the Leeds accident 2016 to illustrate the thinking process and extracts thereof a model that can be tested, evaluated, and replicated. The authors optimistically believe that the proposed model will significantly and smartly help to flatten the curve of road traffic accidents in the fast-growing population densities, which increases considerably motor-based mobility.

Keywords: accident factors, geographic information system, information communication technology, mobility

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Authors: Irum Matloob, Shoab Ahmad Khan, Fahim Arif

Abstract:

As Indo-pak has been the victim of heart diseases since many decades. Many surveys showed that percentage of cardiac patients is increasing in Pakistan day by day, and special attention is needed to pay on this issue. The framework is proposed for performing detailed analysis of ECG survey data which is conducted for measuring the prevalence of heart diseases statistics in Pakistan. The ECG survey data is evaluated or filtered by using automated Minnesota codes and only those ECGs are used for further analysis which is fulfilling the standardized conditions mentioned in the Minnesota codes. Then feature selection is performed by applying proposed algorithm based on discernibility matrix, for selecting relevant features from the database. Clustering is performed for exposing natural clusters from the ECG survey data by applying spectral clustering algorithm using fuzzy c means algorithm. The hidden patterns and interesting relationships which have been exposed after this analysis are useful for further detailed analysis and for many other multiple purposes.

Keywords: arrhythmias, centroids, ECG, clustering, discernibility matrix

Procedia PDF Downloads 345

Authors: Atabong Awung Lekeazem

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The purpose of this paper is to identify the impact or role of incentives on employee’s performance with a particular emphasis on Korean workers. The process involves defining and explaining the different types of motivation. In defining them, we also bring out the difference between the two major types of motivations. The second phase of the paper shall involve gathering data/information from a sample population and then analyzing the data. In the analysis, we shall get to see the almost similar mentality or value which Koreans attach to motivation, which a slide different view coming only from top management personnel. The last phase shall have us presenting the data and coming to a conclusion from which possible knowledge on how managers and potential managers can ignite the best out of their employees.

Keywords: motivation, employee’s performance, Korean workers, business information systems

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Manon Ghislain, Timothée Bonnet, Olivier Gimenez, Olivier Dehorter, Pierre-Yves Henry

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Climatic fluctuations affect the demography of animal populations, generating changes in population size, phenology, distribution and community assemblages. However, very few studies have identified the underlying demographic processes. For short-lived species, like common passerine birds, are these changes generated by changes in adult survival or in fecundity and recruitment? This study tests for an effect of annual climatic conditions (spring and winter) on annual, local adult survival at very large spatial (a country, 252 sites), temporal (25 years) and biological (25 species) scales. The Constant Effort Site ringing has allowed the collection of capture - mark - recapture data for 100 000 adult individuals since 1989, over metropolitan France, thus documenting annual, local survival rates of the most common passerine birds. We specifically developed a set of multi-year, multi-species, multi-site Bayesian models describing variations in local survival and recapture probabilities. This method allows for a statistically powerful hierarchical assessment (global versus species-specific) of the effects of climate variables on survival. A major part of between-year variations in survival rate was common to all species (74% of between-year variance), whereas only 26% of temporal variation was species-specific. Although changing spring climate is commonly invoked as a cause of population size fluctuations, spring climatic anomalies (mean precipitation or temperature for March-August) do not impact adult survival: only 1% of between-year variation of species survival is explained by spring climatic anomalies. However, for sedentary birds, winter climatic anomalies (North Atlantic Oscillation) had a significant, quadratic effect on adult survival, birds surviving less during intermediate years than during more extreme years. For migratory birds, we do not detect an effect of winter climatic anomalies (Sahel Rainfall). We will analyze the life history traits (migration, habitat, thermal range) that could explain a different sensitivity of species to winter climate anomalies. Overall, we conclude that changes in population sizes for passerine birds are unlikely to be the consequences of climate-driven mortality (or emigration) in spring but could be induced by other demographic parameters, like fecundity.

Keywords: Bayesian approach, capture-recapture, climate anomaly, constant effort sites scheme, passerine, seasons, survival

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Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

Abstract:

Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Ashti Morovati, Hushyar Azari, Bahram Pourghassem Gargari

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Objectives and goals: The prevalence of metabolic syndrome (MetS), as a major health burden for societies, is increasing. This clinical trial was conducted to evaluate the effects of CuEO supplementation on anthropometric indices, systolic and diastolic blood pressure, blood glucose level, insulin resistance and serum lipid level in patients suffering from MetS. Methods: This was a randomized, triple‐blind, placebo‐controlled clinical trial in which 56 patients with MetS aged 18–60 years who fulfilled the eligibility criteria were randomly allocated to an intervention or a control group. Inclusion criteria for the study were comprised of diagnosis of MetS according to the new International Federation of Diabetes. The exclusion criteria were defined as: taking herbal supplements, use of drugs having evident interaction with cumin such as anti‐depressant drugs, vitamin D, omega 3, selenium, zinc, smoking, pregnancy, or breastfeeding, suffering from cancer, having any history of gastrointestinal and hepatic, cardiovascular, thyroid and kidney disorders, and menopause. 75 mg CuEO or placebo soft gels were administered three times daily to the participants for eight weeks. The soft gel consumption was checked by asking the participants to bring the medication containers in the follow‐up visits at the 4th and the 8th weeks of the study. Data pertaining to blood pressure, height, weight, waist circumference, hip circumference and BMI, as well as food consumption were collected at the beginning and end of the study. Fasting blood samples ( glucose, triglyceride, total cholesterol, HDL-cholesterol and LDL-cholesterol) were obtained and biochemical measurements were assessed at the beginning and end of the study. Results: At eight weeks, a total of 44 patients completed this study. Except for diastolic blood pressure (DBP), the other assessed variables were not significantly different between the two groups. In intra group analysis, placebo and CuEO groups both had insignificant decrements in DBP (mean difference [MD] with 95% CI: −3.31 [−7.11, 0.47] and −1.77 [−5.95, 2.40] mmHg, respectively). However, DBP was significantly lower in CuEO compared with the placebo group at the end of study (81.41 ± 5.88 vs. 84.09 ± 5.54 mmHg, MD with 95% CI: −3.98 [−7.60, −0.35] mmHg, p < .05). Conclusions: The results of this study indicated that CuEO does not have any effect on MetS components, except for DBP in patients with MetS.

Keywords: blood pressure, fasting blood glucose, lipid profile, waist circumference

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Authors: Sirilak Areerachakul

Abstract:

Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Jun-ya Nagase, Toshiyuki Satoh, Norihiko Saga, Koichi Suzumori

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In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand.

Keywords: disturbance observer, pneumatic balloon, predictive functional control, rubber artificial muscle

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Authors: Ping Bo, Meng Yunshan

Abstract:

Satellite-derived sea surface temperature (SST) is a key parameter for many operational and scientific applications. However, the disadvantage of SST data is a high percentage of missing data which is mainly caused by cloud coverage. Data Interpolating Empirical Orthogonal Function (DINEOF) algorithm is an EOF-based technique for reconstructing the missing data and has been widely used in oceanographic field. The reconstruction of SST images within a long time series using DINEOF can cause large discontinuities and one solution for this problem is to filter the temporal covariance matrix to reduce the spurious variability. Based on the previous researches, an algorithm is presented in this paper to improve the temporal correlations in EOF expansion. Similar with the previous researches, a filter, such as Laplacian filter, is implemented on the temporal covariance matrix, but the temporal relationship between two consecutive images which is used in the filter is considered in the presented algorithm, for example, two images in the same season are more likely correlated than those in the different seasons, hence the latter one is less weighted in the filter. The presented approach is tested for the monthly nighttime 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST for the long-term period spanning from 1989 to 2006. The results obtained from the presented algorithm are compared to those from the original DINEOF algorithm without filtering and from the DINEOF algorithm with filtering but without taking temporal relationship into account.

Keywords: data interpolating empirical orthogonal function, image reconstruction, sea surface temperature, temporal filter

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Authors: Li Mengmeng, Kang Jian

Abstract:

With the rapid development of the city, the noise pollution becomes more and more serious. Noise has seriously affected people's normal life, study and work. In addition, noise has seriously affected the city's ecological environment and the migration of birds. Therefore, it is urgent to control the noise. As one of natural noise-reducing materials, plants have been paid more and more attention. In urban landscape design, it is very important to choose plant species with good noise reduction effect to the sustainable development of urban ecology. The aim of this paper is to find out the characteristics of the plant with good noise reduction effect and apply it in urban landscape design. This study investigated the vibration of leaves of six plant species in a sound field using a Keyence (IG-1000/CCD) Laser Micrometer. The results of the experiments showed that the vibration speed of plant leaves increased obviously after being stimulated by sound source, about 5-10 times. In addition, when driven by the same sound, the speed of all leaves varied with the difference of leaf thickness, leaf size and leaf mass. The speed of all leaves would increase with the increase of leaf size and leaf mass, while those would decrease with the increase of leaf thickness.

Keywords: landscape design, leaf vibration , noise attenuation, plants configuration

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Authors: Fanqiang Kong, Chending Bian

Abstract:

In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined l_2,p-norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy.

Keywords: hyperspectral unmixing, joint-sparse, low-rank representation, abundance estimation

Procedia PDF Downloads 246