Search results for: document detection

Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

Procedia PDF Downloads 231

Authors: Bouami Hind, Millot Patrick

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The implementation of automated devices in life-critical systems such as hospitals can bring a new set of challenges related to automation malfunctions. While automation has been identified as great leverage for the medication dispensing system’s security and efficiency, it also increases the complexity of the organization. In particular, the installation and operation stage of automated devices can be complex when malfunctions related to automated systems occur. This paper aims to document operators’ situation awareness about the malfunctions of automated drug delivery systems (ADCs) during their implementation through Saint Brieuc hospital’s feedback. Our evaluation approach has been deployed in Saint Brieuc hospital center’s pharmacy, which has been equipped with automated nominative drug dispensing systems since January of 2021. The analysis of Saint Brieuc hospital center pharmacy’s automation revealed numerous malfunctions related to the implementation of Automated Delivery Cabinets. It appears that the targeted performance is not reached in the first year of implementation in this case study. Also, errors have been collected in patients' automated treatments’ production such as lack of drugs in pill boxes or nominative carnets, excess of drugs, wrong location of the drug, drug blister damaged, non-compliant sachet, or ticket errors. Saint Brieuc hospital center’s pharmacy is doing a tremendous job of setting up and monitoring performance indicators from the beginning of automation and throughout ADC’s operation to control ADC’s malfunctions and meet the performance targeted by the hospital. Health professionals, including pharmacists, biomedical engineers and directors of work, technical services and safety, are heavily involved in an automation project. This study highlights the importance of the evaluation of ADCs’ performance throughout the implementation process and the hospital’s team involvement in automation supervision and management.

Keywords: life-critical systems, situation awareness, automated delivery cabinets, implementation, risks and malfunctions

Procedia PDF Downloads 93

Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

Procedia PDF Downloads 147

Authors: Zahra Khoshnood, Mehdi Kazaie, Sajedeh Neisi

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In 2013, 24 fish samples were taken from two fishery regions in the north of Persian Gulf near the Iranian coastal lines. The two flatfishes were Yellofin seabream (Acanthopagrus latus) and Longtail tuna (Thannus tonggol). We analyzed total Hg concentration of liver and muscle tissues by Mercury Analyzer (model LECO AMA 254). The average concentration of total Hg in edible Muscle tissue of deep-Flounder was measured in Bandar-Abbas and was found to be 18.92 and it was 10.19 µg.g-1 in Bandar-Lengeh. The corresponding values for Oriental sole were 8.47 and 0.08 µg.g-1. The average concentration of Hg in liver tissue of deep-Flounder, in Bandar-Abbas was 25.49 and that in Bandar-Lengeh was 12.52 µg.g-1.the values for Oriental sole were 11.88 and 3.2 µg.g-1 in Bandar-Abbas and Bandar-Lengeh, respectively.

Keywords: mercury, Acanthopagrus latus, Thannus tonggol, Persian Gulf

Procedia PDF Downloads 596

Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

Procedia PDF Downloads 387

Authors: Akbar Gharbali, Ali Abbasian Ardekani, Afshin Mohammadi

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Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules.

Keywords: ultrasound imaging, thyroid nodules, computer aided diagnosis, texture analysis, PCA, LDA, NDA

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Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

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Authors: Krzysztof Pomorski

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In the given work we present universal mathematical framework for modeling of cattle farm system that can set and validate various hypothesis that can be tested against experimental data. The presented work is preliminary but it is expected to be valid tool for future deeper analysis that can result in new class of prediction methods allowing early detection of cow dieseaes as well as cow performance. Therefore the presented work shall have its meaning in agriculture models and in machine learning as well. It also opens the possibilities for incorporation of certain class of biological models necessary in modeling of cow behavior and farm performance that might include the impact of environment on the farm system. Particular attention is paid to the model of coupled oscillators that it the basic building hypothesis that can construct the model showing certain periodic or quasiperiodic behavior.

Keywords: coupled ordinary differential equations, cattle farm system, numerical methods, stochastic differential equations

Procedia PDF Downloads 141

Authors: Jia-Lien Hsu, Chiung-Wen Chang

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Research articles may follow a simple and succinct structure of organizational patterns, called move. For example, considering extended abstracts, we observe that an extended abstract usually consists of five moves, including Background, Aim, Method, Results, and Conclusion. As another example, when publishing articles in PubMed, authors are encouraged to provide a structured abstract, which is an abstract with distinct and labeled sections (e.g., Introduction, Methods, Results, Discussions) for rapid comprehension. This paper introduces a method for computational analysis of move structures (i.e., Background-Purpose-Method-Result-Conclusion) in abstracts and introductions of research documents, instead of manually time-consuming and labor-intensive analysis process. In our approach, sentences in a given abstract and introduction are automatically analyzed and labeled with a specific move (i.e., B-P-M-R-C in this paper) to reveal various rhetorical status. As a result, it is expected that the automatic analytical tool for move structures will facilitate non-native speakers or novice writers to be aware of appropriate move structures and internalize relevant knowledge to improve their writing. In this paper, we propose a Bayesian approach to determine move tags for research articles. The approach consists of two phases, training phase and testing phase. In the training phase, we build a Bayesian model based on a couple of given initial patterns and the corpus, a subset of CiteSeerX. In the beginning, the priori probability of Bayesian model solely relies on initial patterns. Subsequently, with respect to the corpus, we process each document one by one: extract features, determine tags, and update the Bayesian model iteratively. In the testing phase, we compare our results with tags which are manually assigned by the experts. In our experiments, the promising accuracy of the proposed approach reaches 56%.

Keywords: academic English writing, assisted writing, move tag analysis, Bayesian approach

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Authors: Sajidullah Khan, Najeeb Ullah, Wang Yin Chai, Chai Soo See

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In this paper, we propose an approach to remove impulse and Poisson noise from X-ray images. Many filters have been used for impulse noise removal from color and gray scale images with their own strengths and weaknesses but X-ray images contain Poisson noise and unfortunately there is no intelligent filter which can detect impulse and Poisson noise from X-ray images. Our proposed filter uses the upgraded layer discrimination approach to detect both Impulse and Poisson noise corrupted pixels in X-ray images and then restores only those detected pixels with a simple efficient and reliable one line equation. Our Proposed algorithms are very effective and much more efficient than all existing filters used only for Impulse noise removal. The proposed method uses a new powerful and efficient noise detection method to determine whether the pixel under observation is corrupted or noise free. Results from computer simulations are used to demonstrate pleasing performance of our proposed method.

Keywords: X-ray image de-noising, impulse noise, poisson noise, PRWF

Procedia PDF Downloads 378

Authors: Launcelot McGrath

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The Coronavirus disease 2019 (COVID-19) has spread rapidly around the world, resulting in high mortality rates and very large numbers of people requiring medical treatment in ICU. Management of patient hospitalisation is a critical aspect to control this disease and reduce chaos in the healthcare systems. Remote monitoring provides a solution to protect vulnerable and elderly high-risk patients. Continuous remote monitoring of oxygen saturation, respiratory rate, heart rate, and temperature, etc., provides medical systems with up-to-the-minute information about their patients' statuses. Remote monitoring also limits the spread of infection by reducing hospital overcrowding. This paper examines the potential of remote monitoring for Covid-19 to assist in the rapid identification of patients at risk, facilitate the detection of patient deterioration, and enable early interventions.

Keywords: remote monitoring, patient care, oxygen saturation, Covid-19, hospital management

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Authors: Aico Nogueira

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This paper examines the interinstitutional dialogues within the Brazilian federal structures, which comprises federal, state and local levels, around the themes of new approaches and interventions aimed to promote sustainable rural development, particularly rural development as part of a territorial approach. The work seeks to understand to what extent the various levels of the state interact with these strategies, particularly with the locally constituted powers, focusing on the importance of the transition of traditional agriculture methods to more sustainable agroecological systems and its effects on food security and sustainable rural development. The research analyses as case studies the Sustainable Rural Territories Development Program (PRONAT) of the Ministry of Agrarian Development at the federal level, as well as the State of São Paulo and the Vale do Ribeira Territory, an area characterized by environmental and social vulnerability, restrictive environmental laws and attempts to promote sustainable development. In order to examine how the interrelationships between different levels of governance and civil society, in addition to the neo-institutionalist polity centered literature, the research uses an adaptation of the concept of arena in Ostrom and Hannigan, produced at different scales of decision-making processes, as well as the multilevel governance literature. Document analysis, interviews, focus groups and direct observation techniques are also used. The main findings of this study are that how different levels of governance understand and organize themselves for this work and have a direct impact on the actions taken. Consequently, programs formulated for this purpose are not associated with the creation of institutions capable of breaking with a traditional sectoral view that has historically prevailed in policymaking. And the transition from traditional agriculture to agroecological production systems is hampered by a sectorial foundation, based on large-scale production and the strengthening of the traditional country's land concentration model.

Keywords: agroecology, food security, inter-institutional dialogue, rural poverty, sustainable rural development, territorial development

Procedia PDF Downloads 197

Authors: Galih Imaduddin

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Knowledge has been viewed as one of the most important resources in organizations, including those that operate in the healthcare sector. On that basis, Knowledge Management (KM) is crucial for healthcare organizations to improve their productivity and ensure effective utilization of their resources. Despite the growing interests to understand how KM might work for healthcare organizations, there is only a modest amount of empirical inquiries which have specifically focused on the tools and initiatives to share knowledge. Hence, the main purpose of this paper is to investigate the way healthcare organizations, particularly public sector ones, utilize knowledge sharing tools and initiatives for the benefit of patient-care. Employing a qualitative method, 13 (thirteen) Community Health Centers (CHCs) from a high-performing district health setting in Indonesia were observed. Data collection and analysis involved a repetition of document retrievals and interviews (n=41) with multidisciplinary health professionals who work in these CHCs. A single case study was cultivated reflecting on the means that were used to share knowledge, along with the factors that inhibited the exchange of knowledge among those health professionals. The study discovers that all of the thirteen CHCs exhibited and applied knowledge sharing means which included knowledge documents, virtual communication channels (i.e. emails and chatting applications), and social learning forums such as staff meetings, morning briefings, and communities of practices. However, the intensity of utilization was different among these CHCs, in which organizational culture, leadership, professional boundaries, and employees’ technological aptitude were presumed to be the factors that inhibit knowledge sharing processes. Making a distance with the KM literature of other sectors, this study denounces the primacy of technology-based tools, suggesting that socially-based initiatives could be more reliable for sharing knowledge. This suggestion is largely due to the nature of healthcare work which is still predominantly based on the tacit form of knowledge.

Keywords: knowledge management, knowledge sharing, knowledge sharing tools and initiatives, knowledge sharing inhibitors, primary health care organizations

Procedia PDF Downloads 241

Authors: J. Izan, E.I. Tengku Azmina, F. Pey Thing

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Sustainability has been introduced globally since the emerging of the advancement of technology and product development in various aspects. This concept is regarded highly, listed among the seventeenth elements in Sustainable Development Goals (SDG), especially by developed countries in any of their development plans and being considered in the development of the developing countries. It is such as the concept of sustainability can undeniably provide a medium where the cost used by energy consumption and pollution problems can be greatly reduced. In Malaysia, many rules and policies had been advocated to achieve sustainability; however, the practice and implementation as well the enforcement to ensure its implementation are still not great. University, as an educational institution, shall practice and implement sustainability concepts in as much aspect as possible as a positive example for a wider community. University Malaysia Terengganu (UMT) has already published a blueprint guide in 2015, aims to introduce and implement sustainable practice in the university, and solid waste is one of the key elements highlighted. This study was conducted to determine the status of solid waste management practice among the university community associated with several factors that facilitate the room for sustainable management, as drawn in the established blueprint document. The quantitative analysis was carried out via survey conducted online, acquired the questions on green campus concept and implementation in general and solid waste in particular. The results showed that community UMT showed a high level of awareness and knowledge on sustainable solid waste; however, low percentage in managing solid waste in a sustainable manner. Respondents suggested that stringent guidelines and the establishment of policy greatly help in the realization and enforcement of sustainable solid waste handling and management. Facilities such as waste collection centre with separation and segregation containers will motivate the community to practice 3Rs on a daily basis. This will eventually reduce the generation of waste need to be sent to landfill hence reduce the disposal cost. Prolong, and continuous campaign on sustainable solid waste management need to be carried out more frequently.

Keywords: management, solid waste, sustainability, university

Procedia PDF Downloads 98

Authors: Mosab Alrashed, Wei Xu, Stephen Abineri, Yifan Zhao, Jörn Mehnen

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Thermographic inspection is a relatively new technique for Non-Destructive Testing (NDT) which has been gathering increasing interest due to its relatively low cost hardware and extremely fast data acquisition properties. This technique is especially promising in the area of rapid automated damage detection and quantification. In collaboration with a major industry partner from the aerospace sector advanced thermography-based NDT software for impact damaged composites is introduced. The software is based on correlation analysis of time-temperature profiles in combination with an image enhancement process. The prototype software is aiming to a) better visualise the damages in a relatively easy-to-use way and b) automatically and quantitatively measure the properties of the degradation. Knowing that degradation properties play an important role in the identification of degradation types, tests and results on specimens which were artificially damaged have been performed and analyzed.

Keywords: NDT, correlation analysis, image processing, damage, inspection

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Authors: Eveline Muika Kabongo, Peter Delobelle, Ferdinand Mukumbang, Edward Nicol

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Introduction: Studies have been conducted to establish the effect of the MomConnect program in South Africa, but these studies did not focus on the stakeholders' and implementers' perspectives and the underlying program theory of the MomConnect initiative program. We strived to obtain stakeholders’ perspectives and assumptions on the MomConnect program and develop an initial program theory (IPT) of how the MomConnect initiative was expected to work. Methods: A realist-informed explanatory design used. The interviewer was performed with 10 key informants selected purposively among MomConnect key informants at the a national level of NDoH South Africa. The interview was done via zoom and lasted for 30 to 60 minutes. Introduction and abduction inferencing approaches were applied. The deductive and inductive approaches were performed during the analysis. ICAMO hereustic framework was used to analysed the data in order to get key informants expectations on how the MomConnect will work or not. Results: We developed three folk’s theories illustrating how the key informants’ expected the MomConnect to work. These theories showed that the MomConnect intended to provide users with health information and education that will empower and motivate them with knowledge which will allow the improvement of health services delivery among HCPs and improvement of the uptake of MCH services among pregnant women and mothers and decrease the rate of maternal and child mortality in the country. The lack of an updated mechanism to link women to the outcome was an issue. Another problem enlightened was the introduction of the WhatsApp program instead of SMS messaging, which was free of charge to women. Conclusion: The Folk’s theory developed from this study provided an insight into how the MomConnect was expected to work and what did not work. The folk’s theory will be merged with information from candidate theories on synthesis review and document review to develop our initial program theory of the MomConnect initiative.

Keywords: mHealth, MomConnect program, realist evaluation, maternal and child health, maternal and child health services, introduction, theory-driven

Procedia PDF Downloads 186

Authors: Nada Basset

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Problem Statement: The study examines the important role of leading change inside start-ups and highlights the challenges faced by an entrepreneur during the startup phase of the business. Research Methods/Procedures/Approaches: A qualitative research approach is taken, using the case study analysis method. A comparative study was made between two day care nurseries in Greater Cairo. Non-probability purposive sampling was used and a triangulation of semi-structured interviews, document analysis and participant-observation were applied simultaneously. The in-depth case study analysis took place over a longitudinal study of four calendar months. Results/Findings: Findings demonstrated that leading change in an entrepreneurial setup must be initiated by the entrepreneur, who must also be the owner of the change process. Another important finding showed that the culture of change, although created by the entrepreneur, needs the support and engagement of followers, who should be sharing the same value system and vision of the entrepreneur. Conclusions and Implications: An important implication suggests that during the first year of a start-up lifecycle, special emphasis must be made to the recruitment and selection of personnel, who should play a role into setting the new start-up culture and help it grow or shrink. Another drawn conclusion is that the success of the change must be measured in both quantitative and qualitative terms. Increasing revenues and customer attrition rates -as quantitative KPIs- must be aligned with other qualitative KPIs like customer satisfaction, employee satisfaction, and organizational commitment and business reputation. Originality of Paper: The paper addresses change management in an entrepreneurial concept, with an empirical application on an Egyptian start-up model providing a service to both adults and children. This privileges the research as the constructs measured merged together the level of satisfaction of employees, decision-makers (parents of children), and the users (children).

Keywords: leadership, change management, entrepreneurship, startup business

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Authors: Seyed Mahdi Jameii

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Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

Procedia PDF Downloads 413

Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

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The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

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Authors: Martin Dupuy, Solange Grunenwald, Pierre-Louis Colombo, Laurence Mahieu, Pomone Richard, Philippe Bartoli

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Endonasal endoscopic trans-sphenoidal surgery for pituitary tumours has become a mainstay of treatment over the last two decades. Although it is generally accepted that there is no significant difference between endoscopic versus microscopic approach for surgical outcomes (endocrine and ophthalmologic status), nasal morbidity seems to the benefit of endoscopic procedures. Minimally invasive endoscopic surgery needs an operative learning curve to achieve surgeon’s efficiency. This learning curve is now well known for surgical outcomes and complications rate, however, few data are available for nasal morbidity. The aim of our series is to document operative experience and nasal quality of life after (NQOL) endoscopic trans-sphenoidal surgery. The prospective pituitary surgical cohort consisted of 525 consecutives patients referred to our Skull Base Diseases Department. Endoscopic procedures were performed by a single neurosurgeon using an uninostril approach. NQOL was evaluated using the Sino-Nasal Test (SNOT-22), the Anterior Base Nasal Inventory (ASBNI) and the Skull Base Inventory Score (SBIS). Data were collected before surgery during hospital stay and 3 months after the surgery. The seventy first patients were compared to the latest 70 patients. There was no significant difference between comparison score before versus after surgery for SNOT-22, ASBNI and SBIS during the single surgeon’s learning curve. Our series demonstrates that in our institution there is no statistically significant learning curve for NQOL after uninostril endoscopic pituitary surgery. A careful progression through sinonasal structures with very limited mucosal incision is associated with minimal morbidity and preserves nasal function. Conservative and minimal invasive approach could be achieved early during learning curve.

Keywords: pituitary surgery, quality of life, minimal invasive surgery, learning curve, pituitary tumours, skull base surgery, endoscopic surgery

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Sheng-Shih Wang, Wei-Ting Wang

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This paper designs an intelligent guidance system, based on wireless sensor networks, for efficient parking in parking lots. The proposed system consists of a parking space allocation subsystem, a parking space monitoring subsystem, a driving guidance subsystem, and a vehicle detection subsystem. In the system, we propose a novel and effective virtual coordinate system for sensing and displaying devices to determine the proper vacant parking space and provide the precise guidance to the driver. This study constructs a ZigBee-based wireless sensor network on Arduino platform and implements the prototype of the proposed system using Arduino-based complements. Experimental results confirm that the proposed prototype can not only work well, but also provide drivers the correct parking information.

Keywords: Arduino, parking guidance, wireless sensor network, ZigBee

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Authors: Nouredine Benouzza, Ahmed Hamida Boudinar, Azeddine Bendiabdellah

Abstract:

An algorithm for Eccentricity Fault Detection (EFD) applied to a squirrel cage induction machine is proposed in this paper. This algorithm employs the behavior of the stator current spectral analysis and the localization of the Rotor Slot Harmonic (RSH) frequency to detect eccentricity faults in three phase induction machine. The RHS frequency once obtained is used as a key parameter into a simple developed expression to directly compute the eccentricity fault frequencies in the induction machine. Experimental tests performed for both a healthy motor and a faulty motor with different eccentricity fault severities illustrate the effectiveness and merits of the proposed EFD algorithm.

Keywords: squirrel cage motor, diagnosis, eccentricity faults, current spectral analysis, rotor slot harmonic

Procedia PDF Downloads 477

Authors: Belinda Crissman

Abstract:

Serious mental disorder is greatly overrepresented in prisoners compared to the general community, with consequences for prison management, recidivism and the prisoners themselves. Incarcerated individuals with psychotic disorders experience insufficient detection and treatment and higher rates of suicide in custody. However direct evidence to explain the overrepresentation of individuals with psychosis in prisons is sparse. The current study aimed to use a life course criminology perspective to answer two key questions: 1) What is the temporal relationship between psychosis and offending (does first mental health contact precede first recorded offence, or does the offending precede the mental health diagnosis)? 2) Are there key temporal points or system contacts prior to incarceration that could be identified as opportunities for early intervention? Data from the innovative Queensland Linkage project was used to link individuals with their corrections, health and relevant social service systems to answer these questions.

Keywords: mental disorder, crime, life course criminology, prevention

Procedia PDF Downloads 127

Authors: M. S. Sukumar

Abstract:

Our objective is to develop a full-fledged system for exploring and studying nature of fossils and to extend this to underwater archaeology and mineral mapping. This includes aerial surveying, imaging techniques, artefact extraction and spectrum analysing techniques. These techniques help in regular monitoring of fossils and also the sensing system. The ROV was designed to complete several tasks which simulate collecting data and samples. Given the time constraints, the ROV was engineered for efficiency and speed in performing tasks. Its other major design consideration was modularity, allowing the team to distribute the building process, to easily test systems as they were completed and troubleshoot and replace systems as necessary. Our design itself had several challenges of on-board waterproofed sensor mounting, waterproofing of motors, ROV stability criteria, camera mounting and hydrophone sound acquisition.

Keywords: remotely operated vehicle (ROV) dragonair, underwater archaeology, full-fledged system, aerial imaging and detection

Procedia PDF Downloads 234

Authors: Sadiah Baharom, Che Nidzam Che Ahmad, Saipol Barin Ramli, Asmayati Yahaya, Sopia Md Yassin

Abstract:

The early childhood care and education (ECCE) in Malaysia aspire to develop children who are intellectually, emotionally, physically and spiritually balanced. This aspiration can only materialise if the early childhood program developed comprehensive and is of high quality comparable to international standards. As such, there is a pressing need to assess the quality of the program in an all-encompassing manner. The overall research project aims at developing a comprehensive and integrated model of high-quality Malaysian ECCE. One of the major objectives of this project is to assess and evaluate the scope and quality of the existing ECCE programs in Malaysia. To this end, a specific aspect of this objective is to develop and validate an instrument to assess and evaluate the ECCE curriculum of the country. Thus this paper describes the development and validation of an instrument to explore the quality of early childhood care and education curriculum currently implemented in the country’s ECCE centres. The generation of the constructs and items were based on a set of criteria mapped against existing ECCE practice, document analyses, expert interviews and panel discussions. The items went through expert validation and were field tested on 597 ECCE teachers. The data obtained went through an exploratory factor analysis to validate the constructs of the instrument followed by reliability studies on internal consistency based on the Cronbach Alpha values. The final set of items for the ECCE curriculum instrument, earmarked for the main study, consists of four constructs namely philosophy and core values, curriculum content, curriculum review and unique features. Each construct consists of between 21 to 3 items with a total of 36 items in all. The reliability coefficients for each construct range from 0.65 to 0.961. These values are within the acceptable limits for a reliable instrument to be used in the main study.

Keywords: early childhood and care education, instrument development, reliability studies, validity studies

Procedia PDF Downloads 197

Authors: Wisam H. Muragaa, Kamaruzzaman Seman, Mohd Fadzli Marhusin

Abstract:

Software Defined Networking (SDN) is a paradigm designed to facilitate the way of controlling the network dynamically and with more agility. Network traffic is a set of flows, each of which contains a set of packets. In SDN, a matching process is performed on every packet coming to the network in the SDN switch. Only the headers of the new packets will be forwarded to the SDN controller. In terminology, the flow header fields are called tuples. Basically, these tuples are 5-tuple: the source and destination IP addresses, source and destination ports, and protocol number. This flow information is used to provide an overview of the network traffic. Our module is meant to extract this 5-tuple with the packets and flows numbers and show them as a list. Therefore, this list can be used as a first step in the way of detecting the DDoS attack. Thus, this module can be considered as the beginning stage of any flow-based DDoS detection method.

Keywords: matching, OpenFlow tables, POX controller, SDN, table-miss

Procedia PDF Downloads 194

Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

Procedia PDF Downloads 184