Search results for: accuracy and consistency

Authors: R. R. Dewi Rahmawaty Aktyani Putri, Earmporn Thongkrajai, Dedy Purwito

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There were many instruments have been developed to assess social support which has the different dimension in breast cancer patients. The Issue of measurement is a challenge to determining the component of dimensional concept, defining the unit of measurement, and establishing the validity and reliability of the measurement. However, the instruments where need to know how much support which obtained and perceived among women with breast cancer who undergone chemotherapy which it can help nurses to prevent of non-adherence in chemotherapy. This study aimed to measure the reliability of BC-SSAS instrument among 30 Indonesian women with breast cancer aged 18 years and above who undergone chemotherapy for six cycles in the oncological unit of Outpatient Department (OPD), Margono Soekardjo Hospital, Central Java, Indonesia. Data were collected during October to December 2015 by using modified the Breast Cancer Social Support Assessment (BC-SSAS). The Cronbach’s alpha analysis was carried out to measure internal consistency for reliability test of BC-SSAS instrument. This study used five experts for content validity index. The results showed that for content validity, I-CVI was 0.98 and S-CVI was 0.98; Cronbach’s alpha value was 0.971 and the Cronbach’s alpha coefficients for the subscales were high, with 0.903 for emotional support, 0.865 for informational support, 0.901 for tangible support, 0.897 for appraisal support and 0.884 for positive interaction support. The results confirmed that the BC-SSAS instrument has high reliability. BC-SSAS instruments were reliable and can be used in health care services to measure the social support received and perceived among women with breast cancer who undergone chemotherapy so that preventive interventions can be developed and the quality of health services can be improved.

Keywords: BC-SSAS, women with breast cancer, chemotherapy, Indonesia

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Authors: Rutej R. Mehta, Michael A. Chappell

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Introduction: Arterial spin labelling (ASL) is an increasingly popular perfusion MRI technique, in which arterial blood water is magnetically labelled in the neck before flowing into the brain, providing a non-invasive measure of cerebral blood flow (CBF). The accuracy of ASL CBF measurements, however, is hampered by dispersion effects; the distortion of the ASL labelled bolus during its transit through the vasculature. In spite of this, the current recommended implementation of ASL – the white paper (Alsop et al., MRM, 73.1 (2015): 102-116) – does not account for dispersion, which leads to the introduction of errors in CBF. Given that the transport time from the labelling region to the tissue – the arterial transit time (ATT) – depends on the region of the brain and the condition of the patient, it is likely that these errors will also vary with the ATT. In this study, various dispersion models are assessed in comparison with the white paper (WP) formula for CBF quantification, enabling the errors introduced by the WP to be quantified. Additionally, this study examines the relationship between the errors associated with the WP and the ATT – and how this is influenced by dispersion. Methods: Data were simulated using the standard model for pseudo-continuous ASL, along with various dispersion models, and then quantified using the formula in the WP. The ATT was varied from 0.5s-1.3s, and the errors associated with noise artefacts were computed in order to define the concept of significant error. The instantaneous slope of the error was also computed as an indicator of the sensitivity of the error with fluctuations in ATT. Finally, a regression analysis was performed to obtain the mean error against ATT. Results: An error of 20.9% was found to be comparable to that introduced by typical measurement noise. The WP formula was shown to introduce errors exceeding 20.9% for ATTs beyond 1.25s even when dispersion effects were ignored. Using a Gaussian dispersion model, a mean error of 16% was introduced by using the WP, and a dispersion threshold of σ=0.6 was determined, beyond which the error was found to increase considerably with ATT. The mean error ranged from 44.5% to 73.5% when other physiologically plausible dispersion models were implemented, and the instantaneous slope varied from 35 to 75 as dispersion levels were varied. Conclusion: It has been shown that the WP quantification formula holds only within an ATT window of 0.5 to 1.25s, and that this window gets narrower as dispersion occurs. Provided that the dispersion levels fall below the threshold evaluated in this study, however, the WP can measure CBF with reasonable accuracy if dispersion is correctly modelled by the Gaussian model. However, substantial errors were observed with other common models for dispersion with dispersion levels similar to those that have been observed in literature.

Keywords: arterial spin labelling, dispersion, MRI, perfusion

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Authors: Jian Qu, Akira Shimazu

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OOV (Out Of Vocabulary) terms are terms that cannot be found in many dictionaries. Although it is possible to translate such OOV terms, the translations do not provide any real information for a user. We present an OOV term definition extraction method by using information available from the Internet. We use features such as occurrence of the synonyms and location distances. We apply machine learning method to find the correct definitions for OOV terms. We tested our method on both biomedical type and name type OOV terms, our work outperforms existing work with an accuracy of 86.5%.

Keywords: information retrieval, definition retrieval, OOV (out of vocabulary), biomedical information retrieval

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Authors: Alan C. Lin, Tsong Der Lin

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This study proposes a deviation control method to add interpolation points to numerical control (NC) codes of five-axis machining in order to achieve the required machining accuracy. Specific research issues include: (1) converting machining data between the CL (cutter location) domain and the NC domain, (2) calculating the deviation between the deviated path and the linear path, (3) finding interpolation points, and (4) determining tool orientations for the interpolation points. System implementation with practical examples will also be included to highlight the applicability of the proposed methodology.

Keywords: CAD/CAM, cutter path, five-axis machining, numerical control

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Authors: Khaled M. Nabil

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This paper introduces the Nested Vortexes conception of the universe structure and interprets all the physical phenomena according this conception. The paper first reviews recent physics theories, either in microscopic scale or macroscopic scale, to collect evidence that the space is not empty. But, these theories describe the property of the space medium without determining its structure. Determining the structure of space medium is essential to understand the mechanism that leads to its properties. Without determining the space medium structure, many phenomena; such as electric and magnetic fields, gravity, or wave-particle duality remain uninterpreted. Thus, this paper introduces a conception about the structure of the universe. It assumes that the universe is a medium of ultra-tiny homogeneous particles which are still undiscovered. Like any medium with certain movements, possibly because of a great asymmetric explosion, vortexes have occurred. A vortex condenses the ultra-tiny particles in its center forming a bigger particle, the bigger particles, in turn, could be trapped in a bigger vortex and condense in its center forming a much bigger particle and so on. This conception describes galaxies, stars, protons as particles at different levels. Existing of the particle’s vortexes make the consistency of the speed of light postulate is not true. This conception shows that the vortex motion dynamic agrees with the motion of all the universe particles at any level. An experiment has been carried out to detect the orbiting effect of aggregated vortexes of aligned atoms of a permanent magnet. Based on the described particle’s structure, the gravity force of a particle and attraction between particles as well as charge, electric and magnetic fields and quantum mechanics characteristics are interpreted. All augmented physics phenomena are solved.

Keywords: astrophysics, cosmology, particles’ structure model, particles’ forces

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Authors: Saroj Bijarnia, Preety Singh

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We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system.

Keywords: biometrics, age invariant, verification, support vector machine

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Hasmadi Mamat, Noorfarahzilah Masri

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The search for lesser known and underutilized crops, many of which are potentially valuable as human and animal foods has been the focus of research in recent years. Tarap (Artocarpus odoratisimmus) is one of the most delicious tropical fruit and can be found extensively in Borneo, particularly in Sabah and Sarawak. This study was conducted in order to determine the proximate composition, mineral contents as well as to study the effect of the seed flour on the quality of bread produced. Tarap seed powder (TSP) was incorporated (up to 20%) with wheat flour and used to produce bread. The moisture content, ash, protein, fat, ash, carbohydrates, and dietary fiber were measured using AOAC methods while the mineral content was determined using AAS. The effect of substitution of wheat flour with Tarap seed flour on the quality of dough and bread was investigated using various techniques. Farinograph tests were applied to determine the effect of seaweed powder on the rheological properties of wheat flour dough, while texture profile analysis (TPA) was used to measure the textural properties of the final product. Besides that sensory evaluations were also conducted. On a dry weight basis, the TSP was composed of 12.50% moisture, 8.78% protein, 15.60% fat, 1.17% ash, 49.65% carbohydrate and 12.30% of crude fiber. The highest mineral found were Mg, followed by K, Ca, Fe and Na respectively. Farinograh results found that as TSP percentage increased, dough consistency, water absorption capacity and development time of dough decreased. Sensory analysis results showed that bread with 10% of TSP was the most accepted by panelists where the highest acceptability score were found for aroma, taste, colour, crumb texture as well as overall acceptance. The breads with more than 10% of TSP obtained lower acceptability score in most of attributes tested.

Keywords: tarap seed, proximate analysis, bread, sensory evaluation

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Authors: Ashwaq Alsulami, Jianhua Shao

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Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: attributed community, attribute detection, community, social network

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Authors: Gafar Bamigbade, Adebunkola Omemu

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“Ogi” is a fermented cereal gruel prepared from maize (Zea mays), millet (Pennisetum typhoideum) or guinea corn (Sorghum bicolour). It could be boiled to give a thicker consistency wrapped in leaf allowed to cool and set to a gel known as “eko”. The objective of this study is to determine the amylase activities of mould associated with the spoilage of Ogi and eko. Moulds were isolated from spoilt Ogi and eko samples using standard microbiological procedures. The isolate was then screened for amylase production using starch agar medium. Positive isolates were used for amylase production by solid state fermentation (SFF) using rice bran as the medium. An alpha-amylase and glucoamylase activity of the crude enzyme was determined using the DNS method. The mean mold Population ranged from 1.15 X 105cfu/g for raw Ogi to 6.25 X 105cfu/g for Eko (wrapped in Leaves). Twenty-seven (27) moulds isolated from the sample include A. niger, A. flavus, A. fumigatus, Rhizopus species and Penicillium species. Aspergillus flavus had the highest percentage (51.9%) of incidence while Penicillium species had the least (3.7%). Out of the 27 isolates screened, 19 were found to be amylase positive by showing a clear zone around their colony after flooding with iodine solution. Diameter of clear zone ranged from 3.00mm (Aspergillus niger, C4) to 22.00mm (Aspergillus flavus, A1). Aspergillus niger isolated from spoilt Eko wrapped in leaf has the highest percentage alpha-amylase activity (30.8%) and Aspergillus flavus isolated from spoilt raw ogi has the lowest activity (11.4%). Aspergillus niger isolated from spoilt Eko wrapped in nylon produces the highest glucoamylase activity (240U/ml) while penicillium specie isolated from spoilt cooked ogi has the lowest activity (100U/ml). This study shows that moulds associated with spoilage of ogi and eko can produce amylase.

Keywords: glucoamylase, alpha amylase, ogi, eko

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Bushra

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This paper explores the historical significance, ecological benefits, and contemporary applications of natural dyes in textile dyeing, aiming to provide a comprehensive overview of their potential to contribute to a sustainable fashion industry while minimizing ecological footprints. This research explores the potential of natural dyes as a sustainable alternative to synthetic dyes in the textile industry, examining their historical context, sources, and environmental benefits. Natural dyes come from plants, animals, and minerals, including roots, leaves, bark, fruits, flowers, insects, and metal salts, used as mordants to fix dyes to fabrics. Natural dyeing involves extraction, mordanting, and dyeing techniques. Optimizing these processes can enhance the performance of natural dyes, making them viable for contemporary textile applications based on experimental research. Natural dyes offer eco-friendly benefits like biodegradability, non-toxicity, and resource renewables, reducing pollution, promoting biodiversity, and reducing reliance on petrochemicals. Natural dyes offer benefits but face challenges in color consistency, scalability, and performance, requiring industrial production to meet modern consumer standards for durability and colorfastness. Contemporary initiatives in the textile industry include fashion brands like Eileen Fisher and Patagonia incorporating natural dyes, artisans like India Flint's Botanical Alchemy promoting traditional dyeing techniques, and research projects like the European Union's Horizon 2020 program. Natural dyes offer a sustainable textile industry solution, reducing environmental impact and promoting harmony with nature. Research and innovation are paving the way for widespread adoption, transforming textile dyeing.

Keywords: historical significance, textile industry, natural dyes, sustainability

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Authors: Marwa Ahmed Abd El Hamied

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In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

Keywords: component, artificial immunity technique, solar energy, perturbation and observation, power based methods

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Juyeon Lee, Sungeun You

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Experiential avoidance is one’s tendency to avoid painful internal experience, unwanted adverse thoughts, emotions, and physical sensations. The Multidimensional Experiential Avoidance Questionnaire (MEAQ) is a measure of experiential avoidance, and the original scale consisted of 62 items with six subfactors including behavioral avoidance, distress aversion, procrastination, distraction/suppression, repression/denial, and distress endurance. The purpose of this study was to examine the factor structure of the MEAQ in a Korean sample. Three hundred community adults and university students aged 18 to 35 participated in an online survey assessing experiential avoidance (MEAQ and Acceptance and Action Questionnaire-II; AAQ-II), depression (Patient Health Questionnaire-9; PHQ-9), anxiety (Generalized Anxiety Disoder-7; GAD-7), negative affect (Positive and Negative Affect Scale; PANAS), neuroticism (Big Five Inventory; BFI), and quality of life (Satisfaction with Life Scale; SWLS). Factor analysis with principal axis with direct oblimin rotation was conducted to examine subfactors of the MEAQ. Results indicated that the six-factor structure of the original scale was adequate. Eight items out of 62 items were removed due to insufficient factor loading. These items included 3 items of behavior avoidance (e.g., “When I am hurting, I would do anything to feel better”), 2 items of repression/denial (e.g., “I work hard to keep out upsetting feelings”), and 3 items of distress aversion (e.g., “I prefer to stick to what I am comfortable with, rather than try new activities”). The MEAQ was positively associated with the AAQ-II (r = .47, p < .001), PHQ-9 (r = .37, p < .001), GAD-7 (r = .34, p < .001), PANAS (r = .35, p < .001), and neuroticism (r = .24, p < .001), and negatively correlated with the SWLS (r = -.38, p < .001). Internal consistency was good for the MEAQ total (Cronbach’s α = .90) as well as all six subfactors (Cronbach’s α = .83 to .87). The findings of the study support the multidimensional feature of experiential avoidance and validity of the MEAQ in a sample of Korean adults.

Keywords: avoidance, experiential avoidance, factor structure, MEAQ

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Authors: Yang-Keun Ahn, Kwang-Soon Choi, Young-Choong Park, Kwang-Mo Jung

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Vision based user interfaces to control TVs and PCs have the advantage of being able to perform natural control without being limited to a specific device. Accordingly, various studies on hand gesture recognition using RGB cameras or depth cameras have been conducted. However, such cameras have the disadvantage of lacking in accuracy or the construction cost being large. The proposed method uses a low cost IR camera to accurately differentiate between the hand and the background. Also, complicated learning and template matching methodologies are not used, and the correlation between the fingertips extracted through curvatures is utilized to recognize Click and Move gestures.

Keywords: recognition, hand gestures, infrared camera, RGB cameras

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Authors: Rafika Bibi, Khaled Khaladi, Hind Mokran, Mohamed Salah Boukhechem

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A high performance liquid chromatographic method with ultraviolet detection at 264nm was developed and validate for quantitative determination and separation of cefazolin and cefotaxime in urine, the mobile phase consisted of acetonitrile and phosphate buffer pH4,2(15 :85) (v/v) pumped through ODB 250× 4,6 mm, 5um column at a flow rate of 1ml/min, loop of 20ul. In this condition, the validation of this technique showed that it is linear in a range of 0,01 to 10ug/ml with a good correlation coefficient ( R>0,9997), retention time of cefotaxime, cefazolin was 9.0, 10.1 respectively, the statistical evaluation of the method was examined by means of within day (n=6) and day to day (n=5) and was found to be satisfactory with high accuracy and precision.

Keywords: cefazolin, cefotaxime, HPLC, bioscience, biochemistry, pharmaceutical

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Jyoti Bharti, M. K. Gupta, Astha Jain

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This paper proposes a new improved approach for false positives filtering of detected face images on OpenCV’s Viola Jones Algorithm In this approach, for Filtering of False Positives, Skin Detection in two colour spaces i.e. HSV (Hue, Saturation and Value) and YCrCb (Y is luma component and Cr- red difference, Cb- Blue difference) is used. As a result, it is found that false detection has been reduced. Our proposed method reaches the accuracy of about 98.7%. Thus, a better recognition rate is achieved.

Keywords: face detection, Viola Jones, false positives, OpenCV

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Authors: Gramza-Michalowska Anna, Skrety Joanna, Kobus-Cisowska Joanna, Kmiecik Dominik, Korczak Jozef, Anna Zywica

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Background: Recent research presented many evidences confirming that food besides its basic nutritional function, possess significant therapeutic and prophylactic potential. Conscious consumer is aware of diet habits and well being lifestyle influencing a proper functioning that is why there is a need of new pro-health products. Objective: Proposition of the technology of unsweetened almond drinks enriched with plant extracts for pro-health oriented individuals. Research investigated the influence of selected plant extracts addition on antioxidative activity and consumer’s acceptance of drinks as all day diet product representatives. Methods: The analysis of the basic composition and antioxidant properties of the almond drink was conducted. Research included analysis of basic composition (protein, lipids and fiber content) and antioxidant capacity of drink (DPPH, ABTS, ORAC value, and FRAP). Proposed drink was also characterized with sensory analysis, including color, aroma, taste, consistency, and overall acceptance. Results: Results showed that addition of plant extracts into an almond drink allowed to improve its antioxidant capacity and sensory value of the drinks. Profitable composition and pro-health properties of designed drink permits offering healthy product for all day consumption. Conclusion: Designed almond drink would be a significant supplement for pro-healthy life style of the consumers. Results showed that plant extracts enriched almond drink would be a good source of antioxidants and accepted by the consumers.

Keywords: phytonutrients, pro-health, almond, wellbeing, antioxidant potential, sensory value

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Authors: Faraz Tahir, M. Usman Akram, Albab Ahmad Khan, Mujahid Abbass, Ahmad Tariq, Nuzhat Qaiser

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The objective of this study was to evaluate the Degree of anemia by taking the picture of the palpebral conjunctiva using Smartphone Camera. We have first localized the region of interest from the image and then extracted certain features from that Region of interest and trained SVM classifier on those features and then, as a result, our system classifies the image in real-time on their level of hemoglobin. The proposed system has given an accuracy of 70%. We have trained our classifier on a locally gathered dataset of 30 patients.

Keywords: anemia, palpebral conjunctiva, SVM, smartphone

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Authors: Nathan Reeves

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Aim: The aim of this study was to evaluate if there was an impact on student learning when peer role play was substituted for a professional actor in the role of simulated patient in a simulation-based scenario. Method: Third-year exercise science students enrolled in a field project course in 2015 (n=24), and 2016 (n=20) participated in a simulation-based case scenario designed to develop their client-centred exercise prescription skills. During the simulation, students were provided with feedback from the simulated patients. In 2015, three professional actors played the part of the simulated patient, and in 2016 one of the simulated patients was a student from another exercise science cohort (peer role play). The student learning experience, consistency in case fidelity and feedback provided by the simulated patients was evaluated using a 5-point Likert scale survey and collecting phenomenological data. Results: Improvements to student pre and post confidence remained constant between the 2015 and 2016 cohorts (1.04 and 0.85). The perceived usefulness and enjoyability also remained high across the two cohorts (4.96 and 4.71). The feedback provided by all three simulated patients in 2016 was seen to strongly support student learning experience (4.82), and was of a consistent level (4.47). Significance of the findings to allied health: Simulation-based education is rapidly expanding in the curricula across the allied health professions. The simulated patient methodology continues to receive support as a pedagogy to develop a range of clinical skills including communication, engagement and client-centeredness. Upskilling students to peer role play can be a reasonable alternative to engaging paid actors.

Keywords: exercise science, simulation-based learning, simulated patient, peer role play

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Authors: Sam Teek Ling, Norhashidah Hj. Mohd. Ali

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We investigate the formulation and implementation of new explicit group iterative methods in solving the two-dimensional Poisson equation with Dirichlet boundary conditions. The methods are derived from a fourth order compact nine point finite difference discretization. The methods are compared with the existing second order standard five point formula to show the dramatic improvement in computed accuracy. Numerical experiments are presented to illustrate the effectiveness of the proposed methods.

Keywords: explicit group iterative method, finite difference, fourth order compact, Poisson equation

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Authors: G. K. Sruthi, Sila Bhattacharya

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Sorghum is an important food crop in the dry tropical areas of the world, and possesses significant levels of phytochemicals and dietary fiber to offer health benefits. However, the absence of gluten is a limitation for converting the sorghum dough into sheeted/flattened/rolled products. Chapathi/roti (flat unleavened bread prepared conventionally from whole wheat flour dough) was attempted from sorghum as wheat gluten causes allergic reactions leading to celiac disease. Dynamic oscillatory rheology of sorghum flour dough (control sample) and enzyme treated sorghum doughs were studied and linked to the attributes of the finished ready-to-eat product. Enzymes like amylase, xylanase, and a mix of amylase and xylanase treated dough affected drastically the rheological behaviour causing a lowering of dough consistency. In the case of amylase treated dough, marked decrease of the storage modulus (G') values from 85513 Pa to 23041 Pa and loss modulus (G") values from 8304 Pa to 7370 Pa was noticed while the phase angle (δ) increased from 5.6 to 10.1o for treated doughs. There was a 2 and 3 fold increase in the total sugar content after α-amylase and xylanase treatment, respectively, with simultaneous changes in the structure of the dough and finished product. Scanning electron microscopy exhibited enhanced extent of changes in starch granules. Amylase and mixed enzyme treatment produced a sticky dough which was difficult to roll/flatten. The dough handling properties were improved by the use of xylanase and quality attributes of the chapath/roti. It is concluded that enzyme treatment can offer improved rheological status of gluten free doughs and products.

Keywords: sorghum dough, amylase, xylanase, dynamic oscillatory rheology, sensory assessment

Procedia PDF Downloads 393

Authors: M. Abouelatta, A. Shaker, M. El-Banna, G. T. Sayah, C. Gontrand, A. Zekry

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In this paper, a methodology for physically modeling the intrinsic MOS part and the drift region of the n-channel Laterally Double-diffused MOSFET (LDMOS) is presented. The basic physical effects like velocity saturation, mobility reduction, and nonuniform impurity concentration in the channel are taken into consideration. The analytical model is implemented using MATLAB. A comparison of the simulations from technology computer aided design (TCAD) and that from the proposed analytical model, at room temperature, shows a satisfactory accuracy which is less than 5% for the whole voltage domain.

Keywords: LDMOS, MATLAB, RESURF, modeling, TCAD

Procedia PDF Downloads 189

Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Mohammad Ali Oghabian, Reza Reiazi, Esmaeel Parsai, Mehdi Aghili, Ramin Jaberi

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In this project a new procedure has been introduced for utilizing digitally reconstructed radiograph from MRI images in Brachytherapy treatment planning. This procedure enables us to localize the tumor volume and delineate the extent of critical structures in vicinity of tumor volume. The aim of this project was to improve the accuracy of dose delivered to targets of interest in 2D treatment planning system.

Keywords: brachytherapy, cervix, digitally reconstructed radiographs, lymph node

Procedia PDF Downloads 524

Authors: Arslan Ellahi, Syed Amjad Hussain, Fawaz Saleem Bokhari

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Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them.

Keywords: complexity, rapid evolution, swarm intelligence, dimensions

Procedia PDF Downloads 255

Authors: Hoda A. Abdel Hafez

Abstract:

Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: mining big data, big data, machine learning, telecommunication

Procedia PDF Downloads 399