Search results for: skin or non-skin classification

Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Gabriella Zarlenga, Martha L. Hall

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Introduction: Premature infants are at risk for neurodevelopmental deficits and hospital readmissions, which can increase the financial burden on the health care system and families. Kangaroo care (skin-to-skin contact) is a practice that can improve preterm infant health outcomes. Preterm infants can acquire adequate body temperature, heartbeat, and breathing regulation through lying directly on the mother’s abdomen and in between her breasts. Due to some infant’s condition, kangaroo care is not a feasible intervention. The purpose of this proof-of-concept research project is to create a device which simulates skin-to-skin contact for pre-term infants not eligible for kangaroo care, with the aim of promoting baby’s health outcomes, reducing the incidence of serious neonatal and early childhood illnesses, and/or improving cognitive, social and emotional aspects of development. Methods: The study design is a proof-of-concept based on a three-phase approach; (1) observational study and data analysis of the standard of care for 2 groups of pre-term infants, (2) design and concept development of a novel device for pre-term infants not currently eligible for standard kangaroo care, and (3) prototyping, laboratory testing, and evaluation of the novel device in comparison to current assessment parameters of kangaroo care. A single center study will be conducted in an area hospital offering Level III neonatal intensive care. Eligible participants include newborns born premature (28-30 weeks of age) admitted to the NICU. The study design includes 2 groups: a control group receiving standard kangaroo care and an experimental group not eligible for kangaroo care. Based on behavioral analysis of observational video data collected in the NICU, the device will be created to simulate mother’s body using electrical components in a thermoplastic polymer housing covered in silicone. It will be designed with a microprocessor that controls simulated respiration, heartbeat, and body temperature of the 'simulated caregiver' by using a pneumatic lung, vibration sensors (heartbeat), pressure sensors (weight/position), and resistive film to measure temperature. A slight contour of the simulator surface may be integrated to help position the infant correctly. Control and monitoring of the skin-to-skin contact simulator would be performed locally by an integrated touchscreen. The unit would have built-in Wi-Fi connectivity as well as an optional Bluetooth connection in which the respiration and heart rate could be synced with a parent or caregiver. A camera would be integrated, allowing a video stream of the infant in the simulator to be streamed to a monitoring location. Findings: Expected outcomes are stabilization of respiratory and cardiac rates, thermoregulation of those infants not eligible for skin to skin contact with their mothers, and real time mother Bluetooth to the device to mimic the experience in the womb. Results of this study will benefit clinical practice by creating a new standard of care for premature neonates in the NICU that are deprived of skin to skin contact due to various health restrictions.

Keywords: kangaroo care, wearable technology, pre-term infants, medical design

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Authors: Zahra Sokar, Sara Oufquir, Brahim Eddafali, Abderrahman Chait

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Fenugreek is one of the oldest plants used in traditional herbal medicine. Several studies have demonstrated the anticancer effects of seeds by inhibiting the proliferation, angiogenesis, invasion and metastasis of various cancers. While there is plenty of research demonstrating the antineoplastic effects of dormant seeds, little is known about the potential of sprouts in fighting cancer. Therefore, we propose to study the chemoprotective effect of germinating fenugreek seeds on keratoacanthoma skin cancer induced by cutaneous exposure to DMA/Croton oil in mice. The results obtained show that oral administration of 250 and 500 mg/kg aqueous sprout seed extract reduces the incidence, rate, volume, and tumor weight in a very significant manner. Histological examination revealed that mice treated with 250 mg/kg showed strong inhibition of squamous cell carcinoma formation with thickening of the epithelial layer and mild acanthosis and hyperkeratosis. A dose of 500 mg/kg prevented invasion and the occurrence of hyperkeratosis. Fenugreek sprouts appear to be a promising natural product for preventing keratoacanthoma skin cancer. Nevertheless, further studies in the same field need to be developed to evaluate the antineoplastic potential of germinated seeds.

Keywords: anticancer, fenugreek, keratoacanthoma, sprouts

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Authors: M. Thuanthong, W. Youravong, N. Sirinupong

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Nile tilapia (Oreochromis niloticus) is one of fish species cultured in Thailand with a high production volume. A lot of skin is generated during fish processing. In addition, there are many research reported that fish skin contains abundant of collagen. Thus, the use of Nile tilapia skin as collagen source can increase the benefit of industrial waste. In this study, Acid soluble collagen (ASC) was extracted at 5, 15 or 25 ˚C with 0.5 M acetic acid then the acid was removed out and collagen was concentrated by ultrafiltration-diafiltration (UFDF). The triple helix collagen from UFDF process was used as substrate to produce collagen peptides by alcalase hydrolysis in an enzymatic membrane reactor (EMR) coupling with 1 kDa molecular weight cut off (MWCO) polysulfone hollow fiber membrane. The results showed that ASC extracted at high temperature (25 ˚C) with 0.5 M acetic acid for 5 h still preserved triple helix structure. In the UFDF process, the acid removal was higher than 90 % without any effect on ASC properties, particularly triple helix structure as indicated by circular dichroism spectrum. Moreover, Collagen from UFDF was used to produce collagen peptides by EMR. In EMR, collagen was pre-hydrolyzed by alcalase for 60 min before introduced to membrane separation. The EMR operation was operated for 10 h and provided a good of protein conversion stability. The results suggested that there is a successfulness of UF in application for acid removal to produce ASC with desirable preservation of its quality. In addition, the EMR was proven to be an effective process to produce low molecular weight peptides with ACE-inhibitory activity properties.

Keywords: acid soluble collagen, ultrafiltration-diafiltration, enzymatic membrane reactor, ace-inhibitory activity

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: Kwounghee Choi

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Technological convergence between the various sectors is expected to have a very large impact on future industrial and economy. This study attempts to do empirical approach between specific technologies’ classification. For technological convergence classification, it is necessary to set the target technology to be analyzed. This study selected target technology from national research and development plan. At first we found a source technology for analysis. Depending on the weight of source technology, NT-based, BT-based, IT-based, ET-based, CS-based convergence types were classified. This study aims to empirically show the concept of convergence technology and convergence types. If we use the source technology to classify convergence type, it will be useful to make practical strategies of convergence technology.

Keywords: technology convergence, source technology, convergence type, R&D strategy, technology classification

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: intelligent transportation system, object detection, vehicle couting, vehicle classification, video processing

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Authors: Johannes Matiasek

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Introduction and Aims: Octenidine is a common antiseptic agent in the area of surgical interventions because of its antimicrobial efficacy and outstanding biocompatibility index. We investigate the direct postoperative application of octenilin® on typical procedures in the field of plastic surgery in a prospective, randomized controlled intervention study. The aim of this study is to determine the influence of a direct postoperative application of an octenidine-containing wound gel on wound healing and scarring after abdominoplasty. Material and Methods: In this study, we enrolled 33 patients who underwent abdominoplasty because of medical indications (e.g. Cutis laxa abdominis). To ensure an intraindividual comparison, each patient received both dressings (study-group: octenilin® wound gel; control-group: Omnistrip® dry plaster) immediately after surgery. We evaluate wound-healing tendency, pain during dressing changes and scar formation after two weeks, three, six and twelve months. Regarding scar-evaluation skin-elasticity, sebum on the skin, transepidermal waterloss, skin hydration, melanin content and erythema level were determined with special probes. Furthermore the Vancouver Scar Scale (VSS) and pain level during dressing change are determined. Results: At the time of surgery the mean patient’s age was 44.1 years. On average 5.6 dressing changes were necessary. Wound healing disorders occurred more often in the control-group. In the control-group (dry plaster Omnistrip®) patients reported significantly more pain and superficial skin injuries during dressing changes occurred. Objective scar-evaluation after 3, 6 and 12 months resulted in a significant higher skin-elasticity and significant lower transepidermal water loss in the octenilin® group which is confirmed in the VSS. Conclusion: The immediate postoperative application of the octenidine-containing hydrogel octenilin® after abdominoplasty results in favoured scar formation compared to our actual standard therapy. Less hypertrophic scar formation was observed in the study-group.

Keywords: abdominoplasty, octenidine, scarring, wound healing

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Laura Gleason, Sahithi Talasila, Lauren Banner, Ladan Afifi, Neda Nikbakht

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Primary cutaneous anaplastic large cell lymphoma (pcALCL) accounts for 9% of all cutaneous T-cell lymphomas. pcALCL is classically characterized as a solitary papulonodule that often enlarges, ulcerates, and can be locally destructive, but overall exhibits an indolent course with overall 5-year survival estimated to be 90%. Distinguishing pcALCL from systemic ALCL (sALCL) is essential as sALCL confers a poorer prognosis with average 5-year survival being 40-50%. Although extremely rare, there have been several cases of ALK-positive ALCL diagnosed on skin biopsy without evidence of systemic involvement, which poses several challenges in the classification, prognostication, treatment, and follow-up of these patients. Objectives: We present a case of cutaneous ALK-positive ALCL without evidence of systemic involvement, and a narrative review of the literature to further characterize that ALK-positive ALCL limited to the skin is a distinct variant with a unique presentation, history, and prognosis. A 30-year-old woman presented for evaluation of an erythematous-violaceous papule present on her right chest for two months. With the development of multifocal disease and persistent lymphadenopathy, a bone marrow biopsy and lymph node excisional biopsy were performed to assess for systemic disease. Both biopsies were unrevealing. The patient was counseled on pursuing systemic therapy consisting of Brentuximab, Cyclophosphamide, Doxorubicin, and Prednisone given the concern for sALCL. Apropos of the patient we searched for clinically evident, cutaneous ALK-positive ALCL cases, with and without systemic involvement, in the English literature. Risk factors, such as tumor location, number, size, ALK localization, ALK translocations, and recurrence, were evaluated in cases of cutaneous ALK-positive ALCL. The majority of patients with cutaneous ALK-positive ALCL did not progress to systemic disease. The majority of cases that progressed to systemic disease in adults had recurring skin lesions and cytoplasmic localization of ALK. ALK translocations did not influence disease progression. Mean time to disease progression was 16.7 months, and significant mortality (50%) was observed in those cases that progressed to systemic disease. Pediatric cases did not exhibit a trend similar to adult cases. In both the adult and pediatric cases, a subset of cutaneous-limited ALK-positive ALCL were treated with chemotherapy. All cases treated with chemotherapy did not progress to systemic disease. Apropos of an ALK-positive ALCL patient with clinical cutaneous limited disease in the histologic presence of systemic markers, we discussed the literature data, highlighting the crucial issues related to developing a clinical strategy to approach this rare subtype of ALCL. Physicians need to be aware of the overall spectrum of ALCL, including cutaneous limited disease, systemic disease, disease with NPM-ALK translocation, disease with ALK and EMA positivity, and disease with skin recurrence.

Keywords: anaplastic large cell lymphoma, systemic, cutaneous, anaplastic lymphoma kinase, ALK, ALCL, sALCL, pcALCL, cALCL

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Authors: Yemima Dani Riani, Anggraini Barlian

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Green turtle (Chelonia mydas) is one of well known long-lived turtle. Its age can reach 100 years old. Senescence in green turtle is an interesting process to study because until now no clear explanation has been established about senescence at cellular or molecular level in this species. Since 1999, green turtle announced as an endangered species. Hence, establishment of fibroblast skin cell culture of green turtle may be material for future study of senescence. One common marker used for detecting senescence is telomere shortening. Reduced telomerase activity, the reverse transcriptase enzyme which adds TTAGGG DNA sequence to telomere end, may also cause senescence. The purpose of this research are establish and identify green turtle fibroblast skin cell culture and also compare telomere length and telomerase activity from passage 5 and 14. Primary cell culture made with primary explant method then cultured in Leibovitz-15 (Sigma) supplemented by 10% Fetal Bovine Serum (Sigma) and 100 U/mL Penicillin/Streptomycin (Sigma) at 30 ± 1oC. Cells identified with Rabbit Anti-Vimentin Polyclonal Antibody (Abcam) and Goat Polyclonal Antibody (Abcam) using confocal microscope (Zeiss LSM 170). Telomere length obtained using TeloTAGGG Telomere Length Assay (Roche) while telomerase activity obtained using TeloTAGGG Telomerase PCR ElisaPlus (Roche). Primary cell culture from green turtle skin had fibroblastic morphology and immunocytochemistry test with vimentin antibody proved the culture was fibroblast cell. Measurement of telomere length and telomerase activity showed that telomere length and telomerase activity of passage 14 was greater than passage 5. However, based on morphology, green turtle fibroblast skin cell culture showed senescent morphology. Based on the analysis of telomere length and telomerase activity, suspected fibroblast skin cell culture of green turtles is not undergo aging through telomere shortening.

Keywords: cell culture, chelonia mydas, telomerase, telomere, senescence

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Abdulsalam Abiodun Salman, Abdulmumin Isah

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The study investigated the motivational factors that could enhance the teaching and understanding of ICT-driven cataloguing and classification (Cat and Class) practical classes among students of library and information science (LIS) in Kwara State Library Schools, Nigeria. It deployed a positivist research paradigm using a quantitative method by deploying the use of questionnaires for data collection. The population of the study is one thousand, one hundred and twenty-five (1,125) which was obtained from the department of each respective library school (the University of Ilorin, Ilorin (Unilorin); Federal Polytechnic Offa, (Fedpoffa); and Kwara State University (KWASU). The sample size was determined using the research advisor table. Hence, the study sample of one hundred and ten (110) was used. The findings revealed that LIS students were averagely motivated toward ICT-driven Cataloguing and Classification practical classes. The study recommended that modern cataloguing and classification tools for practical classes should be made available in the laboratories as motivational incentives for students. It was also recommended that library schools should motivate the students beyond the provision of these ICT-driven tools but also extend the practical class periods. Availability and access to medical treatment in case of injuries during the practical classes should be made available. Technologists/Tutors of Cat and Class practical classes should also be exposed to further training in modern trends, especially emerging digital knowledge and skills in cataloguing and classification. This will keep both the tutors and students abreast of the new development in the technological arena.

Keywords: cataloguing and classification, motivational factors, ICT-driven practical classes, LIS students, Nigeria

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Maher Aburrous, Adel Khelifi, Manar Abu Talib

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Transactions performed by financial institutions on daily basis require XML encryption on large scale. Encrypting large volume of message fully will result both performance and resource issues. In this paper a novel approach is presented for securing financial XML transactions using classification data mining (DM) algorithms. Our strategy defines the complete process of classifying XML transactions by using set of classification algorithms, classified XML documents processed at later stage using element-wise encryption. Classification algorithms were used to identify the XML transaction rules and factors in order to classify the message content fetching important elements within. We have implemented four classification algorithms to fetch the importance level value within each XML document. Classified content is processed using element-wise encryption for selected parts with "High", "Medium" or “Low” importance level values. Element-wise encryption is performed using AES symmetric encryption algorithm and proposed modified algorithm for AES to overcome the problem of computational overhead, in which substitute byte, shift row will remain as in the original AES while mix column operation is replaced by 128 permutation operation followed by add round key operation. An implementation has been conducted using data set fetched from e-banking service to present system functionality and efficiency. Results from our implementation showed a clear improvement in processing time encrypting XML documents.

Keywords: XML transaction, encryption, Advanced Encryption Standard (AES), XML classification, e-banking security, fuzzy classification, cryptography, intelligent encryption

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Maizatulnisa Othman, Mohamad Bukhari, Zahurin Halim, Souad A. Muhammad, Khalisani Khalid

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Sandwich structure composites produced by epoxy core and aluminium skin were developed as potential building materials. Interface bonding between core and skin was controlled by varying kenaf content. Five different weight percentage of kenaf loading ranging from 10 wt% to 50 wt% were employed in the core manufacturing in order to study the mechanical properties of the sandwich composite. Properties of skin aluminium with epoxy were found to be affected by drying time of the adhesive. Mechanical behavior of manufactured sandwich composites in relation with properties of constituent materials was studied. It was found that 30 wt% of kenaf loading contributed to increase the flexural strength and flexural modulus up to 102 MPa and 32 Gpa, respectively. Analysis were done on the flatwise and edgewise compression test. For flatwise test, it was found that 30 wt% of fiber loading could withstand maximum force until 250 kN, with compressive strength results at 96.94 MPa. However, at edgewise compression test, the sandwich composite with same fiber loading only can withstand 31 kN of the maximum load with 62 MPa of compressive strength results.

Keywords: sandwich structure composite, epoxy, aluminium, kenaf fiber

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Authors: Yogeeta O. Agrawal, Hitendra S. Mahajan, Sanjay J. Surana

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Methotrexate is effective in controlling recalcitrant psoriasis when administered by the oral or parenteral route long-term. However, the systematic use of this drug may provoke any of a number of side effects, notably hepatotoxic effects. To reduce these effects, clinical studies have been done with topical MTx. It is useful in treating a number of cutaneous conditions, including psoriasis. A major problem in topical administration of MTx currently available in market is that the drug is hydrosoluble and is mostly in the dissociated form at physiological pH. Its capacity for passive diffusion is thus limited. Localization of MTx in effected layers of skin is likely to improve the role of topical dosage form of the drug as a supplementary to oral therapy for treatment of psoriasis. One of the possibilities for increasing the penetration of drugs through the skin is the use of Nanostructured lipid Carriers. The objective of the present study was to formulate and characterize Methotrexate loaded Nanostructured Lipid Carriers (MtxNLCs), to understand in vitro drug release and evaluate the role of the developed gel in the topical treatment of psoriasis. MtxNLCs were prepared by solvent diffusion technique using 3(2) full factorial design.The mean diameter and surface morphology of MtxNLC was evaluated. MtxNLCs were lyophilized and crystallinity of NLC was characterized by Differential Scanning Calorimtery (DSC) and powder X-Ray Diffraction (XRD). The NLCs were incorporated in 1% w/w Carbopol 934 P gel base and in vitro skin deposition studies in Human Cadaver Skin were conducted. The optimized MtxNLCs were spherical in shape, with average particle size of 253(±9.92)nm, zeta potential of -30.4 (±0.86) mV and EE of 53.12(±1.54)%. DSC and XRD data confirmed the formation of NLCs. Significantly higher deposition of Methotrexate was found in human cadaver skin from MtxNLC gel (71.52 ±1.23%) as compared to Mtx plain gel (54.28±1.02%). Findings of the studies suggest that there is significant improvement in therapeutic index in treatment of psoriasis by MTx-NLCs incorporated gel base developed in this investigation over plain drug gel currently available in the market.

Keywords: methotrexate, psoriasis, NLCs, hepatotoxic effects

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Authors: Ali Farzan

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Classification of data objects or patterns is a major part in most of Decision making systems. One of the popular and commonly used classification methods is Decision Tree (DT). It is a hierarchical decision making system by which a binary tree is constructed and starting from root, at each node some of the classes is rejected until reaching the leaf nods. Each leaf node is a representative of one specific class. Finding the splitting criteria in each node for constructing or training the tree is a major problem. Particle Swarm Optimization (PSO) has been adopted as a metaheuristic searching method for finding the best splitting criteria. Result of evaluating the proposed method over benchmark datasets indicates the higher accuracy of the new PSO based decision tree.

Keywords: decision tree, particle swarm optimization, splitting criteria, metaheuristic

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Authors: Fardis Teifoori, Mehdi Dehghani, Idoia Postigo, Jorge Martinez

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Introduction: Many skin disorders, such as atopic dermatitis (AD), is characterized by inflammation, infection, and hyperplasia. In this work, keratinocytes from AD patients are used to study the pomegranate seed oil properties for skin care. Material and methods: Isolated keratinocytes from patients with AD were cultured and stimulated by IL-9 (20 ng/ml) and TNF-α (50ng/ml) for 48h to induce vascular endothelial growth factor (VEGF) and Regulated upon activation, normal T cell expressed and secreted (RANTES) production, respectively, in the presence of different concentrations of pomegranate seed oil (20, 50, 100, and 200 µM). Finally, the concentrations of RANTES and VEGF in the cell culture supernatant were quantified according to the standard protocol of commercial ELISA kits. Results: The results indicated that pomegranate seed oil concentrations of 50, 100, and 200 µM could significantly inhibit the production of VEGF and RANTES by stimulating keratinocytes with IL-9 (20 ng/ml) and TNF-α (50ng/ml), respectively. The decrease in VEGF and RANTES concentration in the presence of the pomegranate seed oil concentrations of 20 and 50 uM was not significant. Conclusion: It was concluded that pomegranate seed oil (PSO) counteracts atopic dermatitis conditions dose-dependently: with the highest effect at the concentration of 200 µM. We suggest that the inexpensive and easily available pomegranate seed oil is a good candidate for cosmetics and clinical utilization for skin care.

Keywords: atopic dermatitis, pomegranate, Punica granatum, RANTES, VEGF

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Authors: Khitem Amiri, Mohamed Farah

Abstract:

Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Yasir Ali Arfat

Abstract:

Composite films based on fish protein isolate (FPI) and fish skin gelatin (FSG) blend incorporated with 50 and 100% (w/w, protein) basil leaf essential oil (BEO) in the absence and presence of 3% (w/w, protein) ZnO nanoparticles (ZnONP) were prepared and characterised. Tensile strength (TS) decreased, whilst elongation at break (EAB) increased as BEO level increased (p < 0.05). However, ZnONP addition resulted in higher TS but lower EAB (p < 0.05). The lowest water vapour permeability (WVP) was observed for the film incorporated with 100% BEO and 3% ZnONP (p < 0.05). BEO and ZnONP incorporation decreased transparency of FPI/FSG films (p < 0.05). FTIR spectra indicated that films added with BEO exhibited higher hydrophobicity. Both BEO and ZnONP had a marked impact on thermal stability of the films. Microstructural study revealed that presence of ZnONP prevented bilayer formation of film containing 100% BEO. FPI/FSG films incorporated with 100% BEO, especially in combination with ZnONP, exhibited strong antibacterial activity against food pathogenic and spoilage bacteria and thus could be used as an active food packaging material to ensure safety and to extend the shelf-life of packaged foods.

Keywords: bionanocomposite, fish protein isolate, fish skin gelatin, basil essential oil, ZnO nanoparticles, antimicrobial packaging

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Authors: I. A. Farhat, M. Bin Hasan

Abstract:

A developed approach for the protection of power transmission lines using Support Vector Machines (SVM) technique is presented. In this paper, the SVM technique is utilized for the classification and isolation of faults in power transmission lines. Accurate fault classification and location results are obtained for all possible types of short circuit faults. As in distance protection, the approach utilizes the voltage and current post-fault samples as inputs. The main advantage of the method introduced here is that the method could easily be extended to any power transmission line.

Keywords: fault detection, classification, diagnosis, power transmission line protection, support vector machines (SVM)

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Authors: Ashraf Mohammed M. Eid, Hossam El Badry

Abstract:

The bored pile friction resistance may be affected by many factors such as the method of construction, pile length and diameter, the soil properties, as well as the depth below ground level. These factors can be represented analytically to study the influence of diameter on the unit skin friction. In this research, the Egyptian Code of soil mechanics is used to assess the skin friction capacity for either the ordinary pile diameter as well as for the large pile diameter. The later is presented in the code and through the work of some researchers based on the results of investigations adopted for a sufficient number of field tests. The comparative results of these researchers with respect to the Egyptian Code are used to check the adequacy of both methods. Based on the results of this study, the traditional static formula adopted for piles of diameter less than 60 cm may be continually used for larger piles by correlating the analyzed formulae. Accordingly, the corresponding modified angle of internal friction is concluded demonstrating a reduction of shear strength due to soil disturbance along the pile shaft. Based on this research the difference between driven piles and bored piles constructed in same soil can be assessed and a better understanding can be evaluated for the effect of different factors on pile skin friction capacity.

Keywords: large piles, static formula, friction piles, sandy soils

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

Abstract:

Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

Procedia PDF Downloads 63