Search results for: signal classification

Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Rigas Dimakopoulos, Marianna Papadopoulou, Roser Pons

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Background: Participation in family, recreational activities and self-care is an integral part of health. It is also the main outcome of rehabilitation services for children and adolescents with motor disabilities. There are currently no tools in Greek to assess participation in young children. Purpose: To culturally adapt and validate the Greek version of the Child Engagement in Daily Living (CEDL). Method: The CEDL was cross-culturally translated into Greek using forward-backward translation, review by the expert committee, pretest application and final review. Internal consistency was evaluated using the Cronbach alpha and test-retest reliability using the intra-class correlation coefficient (ICC). Parents of children aged 18 months to 5 years and with motor disabilities were recruited. Participants completed the CEDL and the children’s gross motor function was classified using the Gross Motor Function Classification System (GMFCS). Results: Eighty-three children were included, GMFCS I-V. Mean ± standard deviation of the CEDL domains “frequency of participation” “enjoyment of participation” and “self-care” were 58.4±14.0, 3.8±1.0 and 49.9±24, respectively. Internal consistency of all domains was high; Cronbach alpha for “frequency of participation” was 0.83, for “enjoyment of participation” was 0.76 and for “self-care” was 0.92. Test-retest reliability (ICC) was excellent for the “self-care” (0.95) and good for “frequency of participation” and “enjoyment of participation” domains (0.90 and 0.88, respectively). Conclusion: The Greek CEDL has good reliability. It can be used to evaluate participation in Greek young children with motor disabilities GMFCS levels I-V.

Keywords: participation, child, disabilities, child engagement in daily living

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Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Saud Al Taj

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Employer branding is considered as a useful tool for addressing the global-local problem facing complex organisations that have operations scattered across the globe and face challenges of dealing with the local environment alongside. Despite being an established field of study within the Western developed world, there is little empirical evidence concerning the relevance of employer branding to global companies that operate in the under-developed economies. This paper fills this gap by gaining rich insight into the implementation of employer branding programs in a foreign multinational operating in Pakistan dealing with the global-local problem. The study is qualitative in nature and employs semi-structured and focus group interviews with senior/middle managers and local frontline employees to deeply examine the phenomenon in case organisation. Findings suggest that authenticity is required in employer brands to enable them to respond to the local needs thereby leading to the resolution of the global-local problem. However, the role of signaling theory is key to the development of authentic employer brands as it stresses on the need to establish an efficient and effective signaling environment wherein signals travel in both directions (from signal designers to receivers and backwards) and facilitate firms with the global-local problem. The paper also identifies future avenues of research for the employer branding field.

Keywords: authenticity, counter-signals, employer branding, global-local problem, signaling theory

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Authors: Cristina Costa-Lobo, Carla B. Vestena, Filomena E. Ponte

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Creativity and intelligence are concepts that have aroused very expressive interest in the field of educational sciences and the field of psychological science since the middle of the last century since they have a great impact on the potential and well-being of individuals. However, due to progress in cognitive and positive psychology, there has been a growing interest in the psychoeducational domain of intelligence and creativity in the last decade. In this theoretical work, are analyzed comparatively the theoretical models that relate the intelligence and the creativity, are analyzed several psychoeducational intervention programs that have been implemented with a view to the promotion of creativity and signal possibilities, realities and ironies around the psychological evaluation of intelligence and creativity. In order to reach a broad perspective on creativity, the evidence is presented that points the need to evaluate different psychological domains. The psychoeducational intervention programs addressed have, with a common characteristic, the full stimulation of the creative potential of the participants, assumed as a highly valued capacity at the present time. The results point to the systematize that all interventions in the ambit of creativity have two guiding principles: all individuals can be creative, and creativity is a capacity that can be stimulated. This work refers to the importance of stimulus creativity in educational contexts, to the usefulness and pertinence of the creation, the implementation, and monitoring of flexible curricula, adapted to the educational needs of students, promoting a collaborative work among teachers, parents, students, psychologists, managers and educational administrators.

Keywords: creativity, intelligence, psychoeducational intervention programs, psychological evaluation, educational contexts

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Authors: Sizhu Wang, Cuisong Tang, Lin Zhang, Guangyu Tang

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Objective: We aimed to detect the composition of bone marrow fatty acids early after ovariectomized (OVX) surgery and explore the potential mechanism. Methods: Thirty-two female Sprague-Dawley (SD) rats (12 weeks) were randomly divided into OVX group and Sham group (N=16/group), and received ovariectomy or sham surgery respectively. After 3 and 28 days, eight rats in each group were sacrificed to detect the composition of bone marrow fatty acids by gas chromatography–mass spectrometry (GC–MS) and evaluate the trabecular bone microarchitecture by micro-CT. Significant different fatty acids in the early stage of post-menopausal osteoporosis were selected by OPLS-DA and t test. Then selected fatty acids were further studied in the process of osteogenic differentiation through RT-PCR and Alizarin Red S staining. Results: An apparent sample clustering and group separation were observed between OVX group and sham group three days after surgery, which suggested the role of bone marrow fatty acids in the early stage of postmenopausal osteoporosis. Specifically, myristate, palmitoleate and arachidonate were found to play an important role in classification between OVX group and sham group. We further investigated the effect of palmitoleate and arachidonate on osteogenic differentiation and found that palmitoleate promoted the osteogenic differentiation of MC3T3-E1 cells while arachidonate inhibited this process. Conclusion: Profound bone marrow fatty acids changes have taken place in the early stage of post-menopausal osteoporosis. Bone marrow fatty acids may begin to affect osteogenic differentiation shortly after deficiency of estrogen.

Keywords: bone marrow fatty acids, GC-MS, osteoblast, osteoporosis, post-menopausal

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Authors: Shao-Chi Chien, Ying-Ju Chen, Chiao-Yu Tseng, Wan-Ting Lee, Yi-Wen Cheng

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Dr. George Leslie Mackay arrived at Takoukang (now Port of Kaohsiung) in Taiwan on December 30, 1871. When Dr. Mackay dedicated at Taiwan for 30 years, he has been an important factor in such areas as preaching, medical and engaged in education. Many researchers have thoroughly studied Dr. Mackay's travels to understand his impact on the state of education, medicine and religion in Taiwan. In the 30-year period of hard work, Dr. Mackay's made outstanding influence on the church in Taiwan. Therefore, the present study will be the mission of the establishment of hospitals, schools, churches which preaching, education, and medicine whether there are related the number of comparisons to explore. According to The Diaries of George Leslie Mackay, our research uses the Geographic Information System (GIS) to map the location of Dr. Mackay's travel in Taiwan and compares it with today's local churches, hospitals, and schools whether there are related the number of comparisons to explore. Therefore, our research focuses on the whole of Taiwan, divided into missionary, medical and education as the main content of the three major parts. Additionally, use of point layer, the surface layer of the property table to establish, in-depth mission of Dr. Mackay's development in Taiwan and Today's comparison. The results will be based on the classification of three different colors pictures that the distance of Mackay's contribution of preaching, medicine, and education. Our research will be compared with the current churches, hospitals, schools and the past churches, hospitals, schools. The results of the present study will provide a reference for future research.

Keywords: George Leslie Mackay, geographic information system, spatial distribution, color categories analysis

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Authors: Fernanda Laureti T. Ferreira, Kazuo Nishimoto

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This paper presents a summary of the experience on videoconferencing tools that have been used to teach regular classes during this pandemic period in educational institutions in São Paulo, which tools and applications are most used and the challenges related to this mode of delivery. At this moment, the massive online education is not a choice of students or a structured development of education system, but a solution that emerged to attend urgent needs and it presents the opportunity to teach and learning available for the most students in this single time of social isolation that forced among others, this significant change for education, students, teachers, institutions and families. Distance education enables synchronous and asynchronous mode classes, and even though the current circumstances generate discomfort and uncertainty, on the other hand, there is a chance to promote a 'learning to learn'. The videoconference is a preferred choice of schools because synchronous mode to give more interaction between a group of students and teachers, but this mode requires specifics teacher competencies and skills, in addition to equipment and provision of adequate internet signal for all participants of the process. The approach is making use of known technical information about video conference tools and the results of search answered by a group of students, teachers, schools, and parents. The results presented refer to the perspectives of students and parents as respondents.

Keywords: distance education, interaction on education, online classes, synchronous e-learning, videoconference

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Authors: Khadra Ahmed, Khaled Kheireldin

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: planktons, diversity indices, water quality index, water ways

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Authors: Eric Mensah

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The effects of rapid urbanisation of tropical sub-Saharan developing cities on local and global climate are of great concern due to the negative impacts of Urban Heat Island (UHI) effects. The importance of urban parks, vegetative cover and forest reserves in these tropical cities have been undervalued with a rapid degradation and loss of these vegetative covers to urban developments which continue to cause an increase in daily mean temperatures and changes to local climatic conditions. Using Landsat data of the same months and period intervals, the spatial variations of land cover changes, temperature, and vegetation were examined to determine how vegetation improves local temperature and the effects of urbanisation on daily mean temperatures over the past 12 years. The remote sensing techniques of maximum likelihood supervised classification, land surface temperature retrieval technique, and normalised differential vegetation index techniques were used to analyse and create the land use land cover (LULC), land surface temperature (LST), and vegetation and non-vegetation cover maps respectively. Results from the study showed an increase in daily mean temperature by 0.80 °C as a result of rapid increase in urban area by 46.13 sq. km and loss of vegetative cover by 46.24 sq. km between 2005 and 2017. The LST map also shows the existence of UHI within the urban areas of Accra, the potential mitigating effects offered by the existence of forest and vegetative cover as demonstrated by the existence of cool islands around the Achimota ecological forest and University of Ghana botanical gardens areas.

Keywords: land surface temperature, climate, remote sensing, urbanisation

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Authors: João Madureira, Ricardo Lagido, Inês Sousa, Fraunhofer Portugal

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Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.

Keywords: inertial measurement unit (IMU), global positioning system (GPS), smartphone, surfing performance

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Yun Sun, Meng Xun, Jingtao Zhou, Ming Li, Qiang Kan, Zhi Jin, Xinyu Liu, Dexin Wu

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Higher speed short-wavelength vertical-cavity surface-emitting lasers (VCSELs) working at high temperature are required for future optical interconnects. In this work, the high-speed 850 nm VCSELs are designed, fabricated and characterized. The temperature dependent static and dynamic performance of devices are investigated by using current-power-voltage and small signal modulation measurements. Temperature-stable high-speed properties are obtained by employing highly strained multiple quantum wells and short cavity length of half wavelength. The temperature dependent photon lifetimes and carrier radiative times are determined from damping factor and resonance frequency obtained by fitting the intrinsic optical bandwidth with the two-pole transfer function. In addition, an analytical theoretical model including the strain effect is development based on model-solid theory. The calculation results indicate that the better high temperature performance of VCSELs can be attributed to the strong confinement of holes in the quantum wells leading to enhancement of the carrier transit time.

Keywords: vertical cavity surface emitting lasers, high speed modulation, optical interconnects, semiconductor lasers

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Authors: Usha Kiran, M. Z. Abdin

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Osmotin is an abundant cationic multifunctional protein discovered in cells of tobacco (Nicotiana tabacum L. var Wisconsin 38) adapted to an environment of low osmotic potential. It provides plants protection from pathogens, hence placed in the PRP family of proteins. The osmotin induced proline accumulation has been reported in plants including transgenic tomato and strawberry conferring tolerance against both biotic and abiotic stresses. The exact mechanism of induction of proline by osmotin is however, not known till date. These observations have led us to hypothesize that osmotin induced proline accumulation could be due to its involvement as transcription factor and/or cell signal pathway modulator in proline biosynthesis. The present investigation was therefore, undertaken to analyze the osmotin protein as transcription factor /cell signalling modulator using bioinformatics tools. The results of available online DNA binding motif search programs revealed that osmotin does not contain DNA-binding motifs. The alignment results of osmotin protein with the protein sequence from DATF showed the homology in the range of 0-20%, suggesting that it might not contain a DNA binding motif. Further to find unique DNA-binding domain, the superimposition of osmotin 3D structure on modeled Arabidopsis transcription factors using Chimera also suggested absence of the same. We, however, found evidence implicating osmotin in cell signaling. With these results, we concluded that osmotin is not a transcription factor but regulating proline biosynthesis and accumulation through cell signaling during abiotic stresses.

Keywords: osmotin, cell signaling modulator, bioinformatic tools, protein

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Authors: Seongho Han, Dongkwan Kim

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The focus of regional innovation policies is shifting from a central government to local governments. The central government demands that regions enforce autonomous and responsible regional innovation policies and that regional governments seek for innovation policies fit for regional characteristics. However, the central government and local governments have not arrived yet at a conclusion on what innovation policies are appropriate for regional circumstances. In particular, even if each local government is trying to find regional innovation strategies that are based on the needs of a region, its innovation strategies turn out to be similar with those of other regions. This leads to a consequence that is inefficient not only at a national level, but also at a regional level. Existing researches on regional innovation types point out that there are remarkable differences in the types or characteristics of innovation among the regions of a nation. In addition they imply that there would be no expected innovation output in cases in which policies are enforced with ignoring such differences. This means that it is undesirable to enforce regional innovation policies under a single standard. This research, given this problem, aims to find out the characteristics and differences in innovation types among the regions in Korea and suggests appropriate policy implications by classifying such characteristics and differences. This research, given these objectives, classified regions in consideration of the various indicators that comprise the innovation suggested by existing related researches and illustrated policies based on such characteristics and differences. This research used recent data, mainly from 2012, and as a methodology, clustering analysis based on multiple factor analysis was applied. Supplementary researches on dynamically analyzing stability in regional innovation types, establishing systematic indicators based on the regional innovation theory, and developing additional indicators are necessary in the future.

Keywords: regional innovation policy, regional innovation type, region-based innovation, multiple factor analysis, clustering analysis

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Authors: Almalki M., Alajmi M., Qadrouh Y., Alzahrani E., Sulaiman A., Aleid M., Albaiji A., Alfaifi H., Alhadadi A., Almotairy H., Alrasheed R., Alhafedh Y.

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High-resolution marine seismic survey is a well-established technique that commonly used to characterize near-surface sediments and geological structures at shallow water. We conduct single channel seismic survey to provide high quality seismic images for near-surface sediments upto 100m depth at Jubal costal area, Arabian Gulf. Eight hydrophones streamer has been used to collect stacked seismic traces alone 5km seismic line. To reach the required depth, we have used spark system that discharges energies above 5000 J with expected frequency output span the range from 200 to 2000 Hz. A suitable processing flow implemented to enhance the signal-to-noise ratio of the seismic profile. We have found that shallow sedimentary layers at the study site have complex pattern of reflectivity, which decay significantly due to amount of source energy used as well as the multiples associated to seafloor. In fact, the results reveal that single channel marine seismic at shallow water is a cost-effective technique that can be easily repeated to observe any possibly changes in the wave physical properties at the near surface layers

Keywords: shallow marine single-channel data, high resolution, frequency filtering, shallow water

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Authors: Anatoly Krivinsh

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The results of dissertation research "Preventing and combating corruption in public procurement" are presented in this publication. The study was conducted 2011 till 2013 in a Member State of the European Union, in the Republic of Latvia. Goal of the thesis is to explore corruption prevention and combating issues in public procurement sphere, to identify the prevalence rates, determinants and contributing factors and prevention opportunities in Latvia. In the first chapter the author analyses theoretical aspects of understanding corruption in public procurement, with particular emphasis on corruption definition problem, its nature, causes and consequences. A separate section is dedicated to the public procurement concept, mechanism and legal framework. In the first part of this work the author presents cognitive methodology of corruption in public procurement field, based on which the author has carried out an analysis of corruption situation in public procurement in Republic of Latvia. In the second chapter of the thesis, the author analyzes the problem of corruption in public procurement, including its historical aspects, typology and classification of corruption subjects involved, corruption risk elements in public procurement and their identification. During the development of the second chapter author's practical experience in public procurements was widely used. The third and fourth chapter deals with issues related to the prevention and combating corruption in public procurement, namely the operation of the concept, principles, methods and techniques, subjects in Republic of Latvia, as well as an analysis of foreign experience in preventing and combating corruption. The fifth chapter is devoted to the corruption prevention and combating perspectives and their assessment. In this chapter the author has made the evaluation of corruption prevention and combating measures efficiency in Republic of Latvia, assessment of anti-corruption legislation development stage in public procurement field in Latvia.

Keywords: prevention of corruption, public purchases, good governance, human rights

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

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Authors: Siripen Yiamjanya, Kevin Wongleedee

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This research paper aims to identify travel motivation by push and pull factors that affected decision making of international tourists in selecting Thailand as their destination choice. A total of 200 international tourists who traveled to Thailand during January and February, 2014 were used as the sample in this study. A questionnaire was employed as a tool in collecting the data, conducted in Bangkok. The list consisted of 30 attributes representing both psychological factors as “push- based factors” and destination factors as “pull-based factors”. Mean and standard deviation were used in order to find the top ten travel motives that were important determinants in the respondents’ decision making process to select Thailand as their destination choice. The finding revealed the top ten travel motivations influencing international tourists to select Thailand as their destination choice included [i] getting experience in foreign land; [ii] Thai food; [iii] learning new culture; [iv] relaxing in foreign land; [v] wanting to learn new things; [vi] being interested in Thai culture, and traditional markets; [vii] escaping from same daily life; [viii] enjoying activities; [ix] adventure; and [x] good weather. Classification of push- based and pull- based motives suggested that getting experience in foreign land was the most important push motive for international tourists to travel, while Thai food portrayed its highest significance as pull motive. Discussion and suggestions were also made for tourism industry of Thailand.

Keywords: decision making, destination choice, international tourist, pull factor, push factor, Thailand, travel motivation

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

Abstract:

Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

Procedia PDF Downloads 169

Authors: Iftikhar A. Tayubi, Tabrej Khan, Mansoor M. Alsubei, Fahad A. Alsaferi

Abstract:

LIZTOXD provides a single source of high-quality information about proteinaceous lizard toxins that will be an invaluable resource for pharmacologists, neuroscientists, toxicologists, medicinal chemists, ion channel scientists, clinicians, and structural biologists. We will provide an intuitive, well-organized and user-friendly web interface that allows users to explore the detail information of Lizard and toxin proteins. It includes common name, scientific name, entry id, entry name, protein name and length of the protein sequence. The utility of this database is that it can provide a user-friendly interface for users to retrieve the information about Lizard, toxin and toxin protein of different Lizard species. These interfaces created in this database will satisfy the demands of the scientific community by providing in-depth knowledge about Lizard and its toxin. In the next phase of our project we will adopt methodology and by using A MySQL and Hypertext Preprocessor (PHP) which and for designing Smart Draw. A database is a wonderful piece of equipment for storing large quantities of data efficiently. The users can thus navigate from one section to another, depending on the field of interest of the user. This database contains a wealth of information on species, toxins, toxins, clinical data etc. LIZTOXD resource that provides comprehensive information about protein toxins from lizard toxins. The combination of specific classification schemes and a rich user interface allows researchers to easily locate and view information on the sequence, structure, and biological activity of these toxins. This manually curated database will be a valuable resource for both basic researchers as well as those interested in potential pharmaceutical and agricultural applications of lizard toxins.

Keywords: LIZTOXD, MySQL, PHP, smart draw

Procedia PDF Downloads 149

Authors: Faiza Babur Khan, Sohail Asghar

Abstract:

With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

Procedia PDF Downloads 314

Authors: M. A. Grigoryev, A. N. Shishkov, D. A. Sychev

Abstract:

The article defines the necessity of choosing the optimal power circuits scheme of the electric drive with field regulated reluctance machine. The specific weighting factors are calculation, the linear regression dependence of specific losses in semiconductor frequency converters are presented depending on the values of the rated current. It is revealed that with increase of the carrier frequency PWM improves the output current waveform, but increases the loss, so you will need depending on the task in a certain way to choose from the carrier frequency. For task of optimization by criterion of the minimum electrical losses regression dependence of the electrical losses in the frequency converter circuit at a frequency of a PWM signal of 0 Hz. The surface optimization criterion is presented depending on the rated output torque of the motor and number of phases. In electric drives with field regulated reluctance machine with at low output power optimization criterion appears to be the worst for multiphase circuits. With increasing output power this trend hold true, but becomes insignificantly different optimal solutions for three-phase and multiphase circuits. This is explained to the linearity of the dependence of the electrical losses from the current.

Keywords: field regulated reluctance machine, the electrical losses, multiphase power circuit, the surface optimization criterion

Procedia PDF Downloads 280

Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

Abstract:

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

Procedia PDF Downloads 85

Authors: Mohammed Dabboor

Abstract:

Spaceborne Synthetic Aperture Radar (SAR) systems are active remote sensing systems independent of weather and sun illumination, two factors which usually inhibit the use of optical satellite imagery. A SAR system could acquire single, dual, compact or fully polarized SAR imagery. Each SAR imagery type has its advantages and disadvantages. The sensitivity of SAR images is a function of the: 1) band, polarization, and incidence angle of the transmitted electromagnetic signal, and 2) geometric and dielectric properties of the radar target. The RADARSAT-1 (launched on November 4, 1995), RADARSAT-2 ((launched on December 14, 2007) and RADARSAT Constellation Mission (to be launched in July 2018) are three past, current, and future Canadian SAR space missions. Canada is developing the RADARSAT Constellation Mission (RCM) using small satellites to further maximize the capability to carry out round-the-clock surveillance from space. The Canadian Space Agency, in collaboration with other government-of-Canada departments, is leading the design, development and operation of the RADARSAT Constellation Mission to help addressing key priorities. The purpose of our presentation is to give an overview of the future Canadian RCM SAR mission with its satellites. Also, the RCM SAR imaging modes along with the expected SAR products will be described. An emphasis will be given to the mission unique capabilities and characteristics, such as the new compact polarimetry SAR configuration. In this presentation, we will summarize the RCM advancement from previous RADARSAT satellite missions. Furthermore, the potential of the RCM mission for different Earth observation applications will be outlined.

Keywords: compact polarimetry, RADARSAT, SAR mission, SAR applications

Procedia PDF Downloads 176

Authors: H. Fraga, J. A. Santos

Abstract:

Vitiviniculture in Portugal is a key socio-economic sector, with a strong connection to local traditions and culture. Despite being a relatively small country, with prevailing Mediterranean environments, Portugal comprises an exceptionally large diversity of growth conditions (Terroirs). The vineyard area in Portugal is over 190 thousand hectares, being the eleventh wine producer and ninth wine exporter worldwide. Owing to the strong impact of weather and climate conditions on grapevine physiological development, grape berry quantity and quality show important inter-annual variability. Grapevines are also susceptible to climate change, as their responses will be unavoidably different under future climates. These impacts may change wine typicity of a given region or even its viticultural suitability. The current study reveals that the projected warming and drying trends for Portugal under the Representative Concentration Pathway (RCP) 4.5 and 8.5, are projected to 1) significantly shift current grapevine growing thermal conditions (e.g., heat and chill accumulation), 2) enhance water stress, 3) anticipate phenological timings and 4) modify yields. Moreover, the present study provides some hints regarding the effectiveness of mulching and irrigation as climate change adaptation measures. Our results show that the effectiveness of these adaptation measures will strongly rest on the strength of the climate change signal at a local scale, thus emphasizing the need for local-to-regional climate change assessments.

Keywords: viticulture, climate change, adaptation measures, Portugal

Procedia PDF Downloads 136