Готові списки джерел за темами / Skinks Classification

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Добірка наукової літератури з теми "Skinks Classification"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 31 січня 2023

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Статті в журналах з теми "Skinks Classification"

1

Freitas, Elyse S., Aniruddha Datta-Roy, Praveen Karanth, L. Lee Grismer, and Cameron D. Siler. "Multilocus phylogeny and a new classification for African, Asian and Indian supple and writhing skinks (Scincidae: Lygosominae)." Zoological Journal of the Linnean Society 186, no. 4 (April 5, 2019): 1067–96. http://dx.doi.org/10.1093/zoolinnean/zlz001.

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Анотація:
AbstractThe genera Lepidothyris, Lygosoma and Mochlus comprise the writhing or supple skinks, a group of semi-fossorial, elongate-bodied skinks distributed across the Old World Tropics. Due to their generalized morphology and lack of diagnostic characters, species- and clade-level relationships have long been debated. Recent molecular phylogenetic studies of the group have provided some clarification of species-level relationships, but a number of issues regarding higher level relationships among genera still remain. Here we present a phylogenetic estimate of relationships among species in Lygosoma, Mochlus and Lepidothyris generated by concatenated and species tree analyses of multilocus data using the most extensive taxonomic sampling of the group to date. We also use multivariate statistics to examine species and clade distributions in morpho space. Our results reject the monophyly of Lygosoma s.l., Lygosoma s.s. and Mochlus, which highlights the instability of the current taxonomic classification of the group. We, therefore, revise the taxonomy of the writhing skinks to better reflect the evolutionary history of Lygosoma s.l. by restricting Lygosoma for Southeast Asia, resurrecting the genus Riopa for a clade of Indian and Southeast Asian species, expanding the genus Mochlus to include all African species of writhing skinks and describing a new genus in Southeast Asia.
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2

HEDGES, S. BLAIR. "The high-level classification of skinks (Reptilia, Squamata, Scincomorpha)." Zootaxa 3765, no. 4 (February 19, 2014): 317. http://dx.doi.org/10.11646/zootaxa.3765.4.2.

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SHEA, GLENN M. "Nomenclature of supra-generic units within the Family Scincidae (Squamata)." Zootaxa 5067, no. 3 (November 11, 2021): 301–51. http://dx.doi.org/10.11646/zootaxa.5067.3.1.

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Анотація:
The modern classification of skinks is based on a nomenclature that dates to the 1970s. However, there are a number of earlier names in the family group that have been overlooked by recent workers. These names are identified and their validity with respect to the International Code of Zoological Nomenclature investigated, along with their type genera. In most cases, use of these names to supplant junior synonyms in modern day use is avoidable by use of the Reversal of Precedence articles of the Code, but the names remain available in case of future divisions at the tribe and subtribe level. Other names are unavailable due to homonymy, either of their type genera or the stems from similar but non-homonymous type genera. However, the name Egerniini is replaced by Tiliquini, due to a limited timespan of use of Egerniini. A new classification of the Family Scincidae is proposed, providing a more extensive use of Code-regulated levels of classification, including tribes and subtribes, and a detailed synonymy provided for each taxonomic unit.
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4

Prokof’eva, Tatiana V. "Experience of the teaching of soil classification systems to students at different stages of education (Faculty of Soil Science, LMSU, Russia)." Bulletin of Geography. Physical Geography Series 14, no. 1 (June 1, 2018): 85–90. http://dx.doi.org/10.2478/bgeo-2018-0008.

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Анотація:
Abstract Soil classification systems provide a common language for scientific communication, represent the diversity of soils and create a scientific basis for soil management, monitoring and conservation. There are several soil classifications currently in use in Russia. Teaching soil systematics to students at the Faculty of Soil Science of the LMSU has developed over the years to meet specific requirements at different stages of education. Students learn to use and correlate different classification systems. Bachelor’s students study classifications to enable professional communication and describing soil diversity. Master’s students further learn the key principles of soil formation, historical and current trends in the development of soil science and the international terminology of soil science. Studying different aspects of the theory and practice of soil classification at different stages of education gives our students a solid base for systematising their knowledge and acquiring skills in scientific research.
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Nuriddinova, Madinabonu. "Classification Of Genres In Multimedia Journalism." American Journal of Social Science and Education Innovations 02, no. 11 (November 23, 2020): 112–18. http://dx.doi.org/10.37547/tajssei/volume02issue11-19.

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Анотація:
Тhe article focuses on multimedia issues that are gaining popularity in journalism. It also includes analysis of increasingly popular multimedia articles online, classification of multimedia genres, and transformation issues. Online format of data journalism, journalistic skills, classification online data materials are also covered in it. The virtual network genres are covered with a basis of extensive examples.
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Rohman, Abdul. "Implementasi Teori Pembelajaran Blended Learning dalam Menyeimbangkan Kapabilitas Belajar pada Era Digital (Studi Kasus di Prodi PAI Universitas Alma Ata Yogyakarta)." An-Nuha : Jurnal Kajian Islam, Pendidikan, Budaya dan Sosial 7, no. 1 (July 15, 2020): 33–51. http://dx.doi.org/10.36835/annuha.v7i1.343.

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Анотація:
Blended learning learning pattern is a combination of conventional learning patterns and e-learning. The concept of blended learning applied at the University of Alma-ata uses the WEB system with a composition of blended learning patterns, 75% for conventional learning and 25% for e-learning. The implementation of the blended learning theory in balancing learning capabilities includes two classifications. Learning capabilities include five aspects, namely verbal information, cognitive strategies, intellectual skills, attitudes and motor skills. The five capabilities are divided into two classifications of blended learning theory, namely verbal information and cognitive strategies included in the classification of e-learning, this is because it places more emphasis on the knowledge aspect. Then the three other aspects fall into the classification of conventional learning, this is because the conventional learning process is carried out directly and face-to-face between educators and students, educators become a central figure in learning and play a role not only in conveying science but as explained by Alghazali, educators are al -mudaris, al-mualim, al-muaddib and al-walid.
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7

Lavrentyeva, O. O. "TO THE CLASSIFICATION OF SKILLS." Educational Dimension 4 (December 26, 2002): 364–70. http://dx.doi.org/10.31812/educdim.5121.

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Анотація:
Author o f the article analyses the formation etymology such as action, operation, reception, skills basing on activity approach. By author's opinion intellectual skills are the special class in structure of people experience and can’t be interpreted neither as mental nor as thought.
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8

Belogurov, Anatoly, and Margarita Marushina. "Classification of corporate Executive training programs and clusters of competencies developed in them." KANT 35, no. 2 (June 2020): 189–95. http://dx.doi.org/10.24923/2222-243x.2020-35.39.

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Анотація:
The purpose of this paper is to propose a classification of corporate education programs for executives and talent reserve based on the revealed characteristics of such programs. The study explores existing Russian and foreign research literature, is based on authors' experience in development and implementation of such programs in a corporate academy, a business-school and in corporate universities, and also analyzes the content of other executive education programs provided by leading Russian corporate education organizations. Analysis of executive education programs classifications in foreign literature revealed that none of them inсluded all types of programs delivered in Russian corporate education, so the authors propose an original classification based on the relationship between the target audience and the competencies and skills to be developed. The proposed classification is of practical interest for professionals in corporate education and company HR, as it assists them in defining the type of a program they develop in-house or order from other educational organizations, in accordance with their organizational goals and through developing clusters of competencies and skills of their top-management.
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9

Sriwong, Kittipat, Supaporn Bunrit, Kittisak Kerdprasop, and Nittaya Kerdprasop. "Dermatological Classification Using Deep Learning of Skin Image and Patient Background Knowledge." International Journal of Machine Learning and Computing 9, no. 6 (December 2019): 862–67. http://dx.doi.org/10.18178/ijmlc.2019.9.6.884.

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10

BEKTAŞ, Nurettin, Sümeyra BEKTAŞ, and Arda ÖZTÜRK. "INVESTIGATION OF THE PROBLEM SOLVING AND DECISION-MAKING SKILLS OF KARATE REFEREES." International Refereed Journal of Humanities and Academic Sciences, no. 27 (2022): 0. http://dx.doi.org/10.17368/uhbab.2022.27.01.

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Aim: The aim of this research is to examine the problem-solving skills and decision-making levels of karate referees. Method: The research group consists of 410 (161 female and 249 male) karate referees from different classifications who worked in the Turkish Karate Federation in the 2020-2021 season. As a data collection tool, the 35-item “Problem Solving Inventory” developed by Heppner and Petersen (1982) and adapted into Turkish by Taylan (1990) and the original Mann. et al. (1998) and adapted into Turkish by Deniz (2004) "Melbourne Decision Making Scale I-II" (MKVÖ) was used. Independent Sample T-Test, One-Factor Analysis of Variance and Pearson Correlation Test were applied to the obtained data after descriptive statistical operations were performed. Results and Conclusion: As a result of the research, while there was no significant difference in the decision-making levels of karate referees according to the refereeing year (p>0.05), there was a significant difference in the decision-making levels according to gender and refereeing classification (p<0.05). When we look at the problem-solving skills of karate referees, there were significant differences in problem-solving skills according to gender, refereeing year and refereeing classification (p<0.05).
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Більше джерел

Дисертації з теми "Skinks Classification"

1

Al-Anezi, Yousuf. "Computer based learning environment for mathematical classification skills." Thesis, University of Leeds, 1994. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.666890.

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Viana, Joaquim Mesquita da Cunha. "Classification of skin tumours through the analysis of unconstrained images." Thesis, De Montfort University, 2009. http://hdl.handle.net/2086/2400.

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Skin cancer is the most frequent malignant neoplasm for Caucasian individuals. According to the Skin Cancer Foundation, the incidence of melanoma, the most malignant of skin tumours, and resultant mortality, have increased exponentially during the past 30 years, and continues to grow. [1]. Although often intractable in advanced stages, skin cancer in general and melanoma in particular, if detected in an early stage, can achieve cure ratios of over 95% [1,55]. Early screening of the lesions is, therefore, crucial, if a cure is to be achieved. Most skin lesions classification systems rely on a human expert supported dermatoscopy, which is an enhanced and zoomed photograph of the lesion zone. Nevertheless and although contrary claims exist, as far as is known by the author, classification results are currently rather inaccurate and need to be verified through a laboratory analysis of a piece of the lesion’s tissue. The aim of this research was to design and implement a system that was able to automatically classify skin spots as inoffensive or dangerous, with a small margin of error; if possible, with higher accuracy than the results achieved normally by a human expert and certainly better than any existing automatic system. The system described in this thesis meets these criteria. It is able to capture an unconstrained image of the affected skin area and extract a set of relevant features that may lead to, and be representative of, the four main classification characteristics of skin lesions: Asymmetry; Border; Colour; and Diameter. These relevant features are then evaluated either through a Bayesian statistical process - both a simple k-Nearest Neighbour as well as a Fuzzy k-Nearest Neighbour classifier - a Support Vector Machine and an Artificial Neural Network in order to classify the skin spot as either being a Melanoma or not. The characteristics selected and used through all this work are, to the author’s knowledge, combined in an innovative manner. Rather than simply selecting absolute values from the images characteristics, those numbers were combined into ratios, providing a much greater independence from environment conditions during the process of image capture. Along this work, image gathering became one of the most challenging activities. In fact several of the initially potential sources failed and so, the author had to use all the pictures he could find, namely on the Internet. This limited the test set to 136 images, only. Nevertheless, the process results were excellent. The algorithms developed were implemented into a fully working system which was extensively tested. It gives a correct classification of between 76% and 92% – depending on the percentage of pictures used to train the system. In particular, the system gave no false negatives. This is crucial, since a system which gave false negatives may deter a patient from seeking further treatment with a disastrous outcome. These results are achieved by detecting precise edges for every lesion image, extracting features considered relevant according to the giving different weights to the various extracted features and submitting these values to six classification algorithms – k-Nearest Neighbour, Fuzzy k-Nearest Neighbour, Naïve Bayes, Tree Augmented Naïve Bayes, Support Vector Machine and Multilayer Perceptron - in order to determine the most reliable combined process. Training was carried out in a supervised way – all the lesions were previously classified by an expert on the field before being subject to the scrutiny of the system. The author is convinced that the work presented on this PhD thesis is a valid contribution to the field of skin cancer diagnostics. Albeit its scope is limited – one lesion per image – the results achieved by this arrangement of segmentation, feature extraction and classification algorithms showed this is the right path to achieving a reliable early screening system. If and when, to all these data, values for age, gender and evolution might be used as classification features, the results will, no doubt, become even more accurate, allowing for an improvement in the survival rates of skin cancer patients.
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3

Wan, Fengkai. "Deep Learning Method used in Skin Lesions Segmentation and Classification." Thesis, KTH, Medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233467.

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Malignant melanoma (MM) is a type of skin cancer that is associated with a very poor prognosis and can often lead to death. Early detection is crucial in order to administer the right treatment successfully but currently requires the expertise of a dermatologist. In the past years, studies have shown that automatic detection of MM is possible through computer vision and machine learning methods. Skin lesion segmentation and classification are the key methods in supporting automatic detection of different skin lesions. Compared with traditional computer vision as well as other machine learning methods, deep neural networks currently show the greatest promise both in segmentation and classification. In our work, we have implemented several deep neural networks to achieve the goals of skin lesion segmentation and classification. We have also applied different training schemes. Our best segmentation model achieves pixel-wise accuracy of \textbf{0.940}, Dice index of \textbf{0.867} and Jaccard index of \textbf{0.765} on the ISIC 2017 challenge dataset. This surpassed the official state of the art model whose pixel-wise accuracy was 0.934, Dice index 0.849 and Jaccard Index 0.765. We have also trained a segmentation model with the help of adversarial loss which improved the baseline model slightly. Our experiments with several neural network models for skin lesion classification achieved varying results. We also combined both segmentation and classification in one pipeline meaning that we were able to train the most promising classification model on pre-segmented images. This resulted in improved classification performance. The binary (melanoma or not) classification from this single model trained without extra data and clinical information reaches an area under the curve (AUC) of 0.684 on the official ISIC test dataset. Our results suggest that automatic detection of skin cancers through image analysis shows significant promise in early detection of malignant melanoma.
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4

Dhinagar, Nikhil J. "Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366384987.

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5

Moulis, Armand. "Automatic Detection and Classification of Permanent and Non-Permanent Skin Marks." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138132.

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Анотація:
When forensic examiners try to identify the perpetrator of a felony, they use individual facial marks when comparing the suspect with the perpetrator. Facial marks are often used for identification and they are nowadays found manually. To speed up this process, it is desired to detect interesting facial marks automatically. This master thesis describes a method to automatically detect and separate permanent and non-permanent marks. It uses a fast radial symmetry algorithm as a core element in the mark detector. After candidate skin mark extraction, the false detections are removed depending on their size, shape and number of hair pixels. The classification of the skin marks is done with a support vector machine and the different features are examined. The results show that the facial mark detector has a good recall while the precision is poor. The elimination methods of false detection were analysed as well as the different features for the classifier. One can conclude that the color of facial marks is more relevant than the structure when classifying them into permanent and non-permanent marks.
När forensiker försöker identifiera förövaren till ett brott använder de individuella ansiktsmärken när de jämför den misstänkta med förövaren. Dessa ansiktsmärken identifieras och lokaliseras oftast manuellt idag. För att effektivisera denna process, är det önskvärt att detektera ansiktsmärken automatiskt. I rapporten beskrivs en framtagen metod som möjliggör automatiskt detektion och separation av permanenta och icke-permanenta ansiktsmärken. Metoden som är framtagen använder en snabb radial symmetri algoritm som en huvuddel i detektorn. När kandidater av ansiktsmärken har tagits, elimineras alla falska detektioner utifrån deras storlek, form och hårinnehåll. Utifrån studiens resultat visar sig detektorn ha en god känslighet men dålig precision. Eliminationsmetoderna av falska detektioner analyserades och olika attribut användes till klassificeraren. I rapporten kan det fastställas att färgskiftningar på ansiktsmärkena har en större inverkan än formen när det gäller att sortera dem i permanenta och icke-permanenta märken.
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6

Ridell, Patric, and Henning Spett. "Training Set Size for Skin Cancer Classification Using Google’s Inception v3." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209443.

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Анотація:
Today, computer aided diagnosis (CADx) is a common occurrence in hospitals. With image recognition, computers are able to detect signs of breast cancer and different kinds of lung diseases. For a convolutional neural network (CNN) that classifies images, the accuracy depends on the amount of data it is trained on and performs better as the amount of training data increase. This introduces a need for relevant images for the classes the classifier is supposed to differentiate between. However, when input data is increased, so does the computational cost, leading to a trade-off between accuracy and computational time. In a study by Cho et al. the accuracy improvement stagnates, when comparing the accuracy with different amounts of training data. This creates interests in finding that point of stagnation, since further increase of input data would lead to longer computational time but little effect on the accuracy. In this study, the pre-trained CNN Google Inception v3 is retrained with various amounts of skin lesion images. The objective is to detect whether the image represents a benign nevus or malignant melanoma. When comparing the accuracy for these different training sessions it is concluded that the accuracy increases when trained with more data. However, a stagnation point for the accuracy is not found.
Datorstödd diagnostik (CADx) är idag vanligt förekommande inom sjukvården. Med datorseende är det möjligt att undersöka huruvida bilder påvisar tecken för till exempel bröstcancer och lungsjukdomar. Träffsäkerheten för convolutional neural networks (CNN) klassificering av sjukdomar beror till viss del på hur mycket data det tränats på. Stora datamängder är en förutsättning för att CNN ska kunna ge pålitliga diagnoser. En stor mängd indata innebär dock att bräkningstiden ökar. Detta medför att det kan behöva göras en avvägning mellan träffsäkerhet och beräkningstid. Cho et al. har i en studie visat att träffsäkerhetens förbättring stagnerar när mängden indata ökar. Det finns därför ett intresse i att hitta den punkt där träffsäkerheten stagnerar, eftersom ytterligare ökning av indata skulle innebära längre beräkningstid men med liten förbättring i träffsäkerhet. I denna uppsats tränas Googles förtränade CNN om på varierade mängder bilder på hudfläckar, i syfte att avgöra om en bild föreställande en hudfläck visar tecken på malignt melanom eller om den bedöms vara godartad. Studiens resultat ger indikationer på att träffsäkerheten för klassificeraren förbättras när mängden träningsdata ökar. Däremot finns inte underlag för att fastställa en punkt då träffsäkerheten stagnerar.
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Boman, Joakim, and Alexander Volminger. "Evaluating a deep convolutional neural network for classification of skin cancer." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229740.

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Анотація:
Computer-aided diagnosis (CAD) has become an important part of themedical field. Skin cancer is a common and deadly disease that a CADsystem could potentially detect. It is clearly visible on the skin andtherefore only images of skin lesions could be used in order to pro-vide a diagnosis. In 2017, a research group at Stanford University de-veloped a deep convolutional neural network (CNN) that performedbetter than dermatologists during classification of skin lesions.This thesis makes an attempt at implementing the method pro-vided in the Stanford report and evaluate the performance of the CNNduring classification of skin lesion comparisons not tested in their study.The previously unseen binary classification use cases are melanomaversus solar lentigo and melanoma versus seborrheic keratosis. Usingtransfer learning, Inception v3 was trained for various skin lesions.The CNN was trained with 16 training classes. During validation ofthe CNN, an accuracy of 68.3% was achieved during a 3-way classi-fication. Testing the same comparisons as the Stanford study an ac-curacy of 71% was achieved for melanoma versus nevus and 91% forseborrheic keratosis versus basal and squamous cell carcinoma. Theaccuracy results for the new comparisons were 84% for seborrheic ker-atosis versus melanoma and 83% for solar lentigo versus melanoma.The results suggest that out of the binary classifications performedin this study, nevus versus melanoma is the most difficult for the CNN.It should be noted that our results were different from the Stanfordstudy and that more statistical methods should have been used whenobtaining the results
Computer-aided diagnosis (CAD) har blivit en viktigt del av det medi-cinska området. Hudcancer är en vanlig och dödlig sjukdom som ett CAD system potentiellt kan upptäcka. Den är klart synlig på hudenoch därför skulle endast bilder av hudskador kunna användas för attge en diagnos. År 2017 utvecklade en forskningsgrupp från StanfordUniversity ett deep convolutional neural network (CNN) som preste-rade bättre än dermatologer vid klassificering av hudskador. Detta kandidatexamensarbete gör ett försök till att implementerametoden tillhandahållen i Stanford rapporten och utvärdera CNN:etsresultat vid klassifikation av hudskador som inte testades i deras stu-die. De binära fall som tidigare inte har testas är melanoma emot solarlentigo och melanoma emot seborrheic keratosis. Med hjälp av transferlearning tränades Inception v3 för olika hudskador. CNN:et tränadesmed 16 typer av hudförändringar. I valideringsprocessen uppmättesen korrekthet på 68.3% under 3-vals klassifikation. I tester av sammatyp av jämförelser som i Stanford studien uppmätes en korrekthet på71% för melanoma emot nevus och 91% för seborrheic keratosis emotbasal and squamous cell carcinoma. Resultatet av de nya jämförelser-na var 84% för seborrheic keratosis emot melanoma och 83% för solarlentigo emot melanoma. Resultaten tyder på att av de binära klassificeringarna utförda idenna studie, är nevus emot melanoma den svårast för CNN:et. Detbör noteras att våra resultat skilde sig från Stanford studien och attmer stat
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8

Sahlgren, Michaela, and Nour Alhunda Almajni. "Skin Cancer Image Classification with Pre-trained Convolutional Neural Network Architectures." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259622.

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Анотація:
In this study we compare the performance of different pre-trained deep convolutional neural network architectures on classification of skin lesion images. We analyse the ISIC skin cancer image dataset. Our results indicate that the architectures analyzed achieve similar performance, with each algorithm reaching a mean five-fold cross-validation ROC AUC value between 0.82 and 0.89. The VGG-11 architecture achieved highest performance, with a mean ROC AUC value of 0.89, despite the fact that it performs considerably worse than some of other architectures on the ILSVRC task. Overall, our results suggest that the choice of architecture may not be as crucial on skin-cancer classification compared with the ImageNet classification problem.
I denna studie jämför vi hur väl olika förtränade konvolutionella neurala nätverksarkitekturer klassificerar bilder av potentiellt maligna födelsemärken. Detta med hjälp av datasetet ISIC, innehållande bilder av hudcancer. Våra resultat indikerar att alla arkitekturer som undersöktes gav likvärdiga resultat vad gäller hur väl de kan avgöra huruvida ett födelsemärke är malignt eller ej. Efter en femfaldig korsvalidering nådde de olika arkitekturerna ett ROC AUC-medelvärde mellan 0.82 och 0.89, där nätverket Vgg-11 gjorde allra bäst ifrån sig. Detta trots att samma nätvärk är avsevärt sämre på ILSVRC. Sammantaget indikterar våra resultat att valet av arkitektur kan vara mindre viktigt vid bildklassificering av hudcancer än vid klassificering av bilder på ImageNet.
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9

Almasiri, osamah A. "SKIN CANCER DETECTION USING SVM-BASED CLASSIFICATION AND PSO FOR SEGMENTATION." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5489.

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Various techniques are developed for detecting skin cancer. However, the type of maligned skin cancer is still an open problem. The objective of this study is to diagnose melanoma through design and implementation of a computerized image analysis system. The dataset which is used with the proposed system is Hospital Pedro Hispano (PH²). The proposed system begins with preprocessing of images of skin cancer. Then, particle swarm optimization (PSO) is used for detecting the region of interest (ROI). After that, features extraction (geometric, color, and texture) is taken from (ROI). Lastly, features selection and classification are done using a support vector machine (SVM). Results showed that with a data set of 200 images, the sensitivity (SE) and the specificity (SP) reached 100% with a maximum processing time of 0.03 sec.
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Inal, Aydin. "Practical science process skills in physics, with special reference to test item assessment and classification." Master's thesis, University of Cape Town, 2002. http://hdl.handle.net/11427/11566.

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Bibliography: leaves 207-218.
This study describes the development, validation, classification, administration and assessment of a compact programme of ten core practical task items chosen from a pool of 33 practical tasks developed for the purpose of this study in basic school physical science. The practical items encouraged and measured various science process skills laid out in the South African Revised National Curriculum Statement Draft. The derivation and classification of the specially designed diagnostic practical task items by experienced lecturers, teachers and academics constitutes an original and crucial part of the study. The objective is to assess the consensus of juries of four to eleven expert science educators on classification of the ten core practical activities, matching the categories. The investigation establishes whether there is a perceived relevant match or a perceived "irrelevant" mismatch between the science process skills tested by the current experimental programme of practical items and the descriptive theories of practical science and its classification schemes and criteria proposed by (a) Franus (1992), (b) Gardner (1983), (c) White (1988), (d) Solomon (1998), (e) Lock (1990), (t) Kapenda, Kandjeo-Marenga, Gaoseb, Kasanda and Lubben's (2001) the Cambridge-based International General Certificate of Education after Millar, Ie Marechal and Tiberghiea (1999), (g) Race (1997) and (h) OBE (Revised National Curriculum Statement Draft, 2001). Lock's assessment framework for practical tasks was found to be the most relevant scheme among the others. The study also identified eight process skills that are highly relevant to practical tasks of the compact programme. These skills included: (a) comprehension skills; (b) recognising given item of apparatus; (c) following instructions; (d) carrying out tasks and handling science apparatus; (e) observation skills; (t) interpretation of the observations; (g) making predictions; and (h) reporting and communicating scientific information.
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Книги з теми "Skinks Classification"

1

Ouboter, Paul E. A revision of the genus Scincella (Reptilia, Sauria, Scincidae) of Asia, with some notes on its evolution. Leiden: Rijksmuseum van Natuurlijke Historie, 1986.

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2

Towns, D. R. A field guide to the lizards of New Zealand. 2nd ed. Wellington: Dept. of Conservation, 1988.

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3

Zug, George R. Systematics of the Carlia "fusca" lizards (Squamata:Scincidae) of New Guinea and nearby islands. Honolulu: Bishop Museum Press, 2004.

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Zug, George R. Systematics of the Carlia "fusca" lizards (Squamata: Scincidae) of New Guinea and nearby islands. Honolulu: Bishop Museum Press, 2004.

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5

Fortune, Christopher Joseph. The investigation and classification of measurement skills. Salford: University of Salford, 1993.

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6

Claybourne, Anna. Can you tell a skink from a salamander?: Classification. Chicago, Ill: Raintree, 2005.

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7

Zanzibar. Wizara ya Elimu, Utamaduni na Michezo. Second status report: Occupational analyses : technical support services for the establishment and operation skills development centres in Zanzibar. Zanzibar: Ministry of Education, Culture, and Sports, 2005.

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Claybourne, Anna. Can you tell a skink from a salamander? Oxford: Raintree, 2006.

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9

Roper, Clyde F. E. Comparative morphology and function of dermal structures in oceanic squids (Cephalopoda). Washington, D.C: Smithsonian Institution Press, 1990.

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10

Goyhman, Oskar. Organization and holding of events. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1071381.

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Every day around the world held a lot of events: exhibitions, conferences, presentations, festivals, etc. which requires professional skills, communication skills and creativity. In the textbook are given classification of activities, the technology for phased development and examples of some of them. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for students on specialties and directions of the service sector, and also for teachers, professionals, various agencies and all who have to organize events.
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Частини книг з теми "Skinks Classification"

1

Obagi, Zein E. "Skin Classification." In Obagi Skin Health Restoration and Rejuvenation, 65–85. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-0-387-21801-4_5.

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2

Hermanek, P., and L. H. Sobin. "Skin Tumours." In TNM Classification of Malignant Tumours, 83–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82982-6_6.

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3

Zhang, Susu, Jeff Douglas, Shiyu Wang, and Steven Andrew Culpepper. "Reduced Reparameterized Unified Model Applied to Learning Spatial Rotation Skills." In Handbook of Diagnostic Classification Models, 503–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05584-4_24.

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4

Hassan, Rola, Hanan Faruqui, Reem Alquraa, Ayman Eissa, Fatma Alshaiki, and Mohamed Cheikh. "Classification Criteria and Clinical Practice Guidelines for Rheumatic Diseases." In Skills in Rheumatology, 521–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8323-0_25.

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AbstractRheumatic diseases have many classification criteria and management guidelines that are continuously being updated in order to improve the quality of healthcare provision. With these ever-evolving criteria and guidelines, practicing clinicians need an easy way to get to the core of these updates and to retain them in an easy and memorable way. Classification criteria are meant to differentiate between similar diseases and also to confirm or rule out a certain disease based on inclusion and exclusion criteria. The diagnosis of rheumatic diseases can be challenging since many clinical signs and symptoms as well as many laboratory markers are not specific and can be positive in many diseases.
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5

Hexsel, Doris, Camile L. Hexsel, and Fernanda Naspolini Bastos. "Cellulite: Classification and Scoring." In Measuring the Skin, 1–5. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26594-0_93-1.

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6

Humbert, Philippe. "Classification of Facial Wrinkling." In Measuring the skin, 698–703. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08585-1_74.

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7

Hexsel, Doris, Camile L. Hexsel, and Fernanda Naspolini Bastos. "Cellulite: Classification and Scoring." In Agache's Measuring the Skin, 1385–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32383-1_93.

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8

Xu, Xueli, and Matthias von Davier. "Applying the General Diagnostic Model to Proficiency Data from a National Skills Survey." In Handbook of Diagnostic Classification Models, 489–501. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05584-4_23.

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9

Manzato, Emilia. "Classification of Eating Disorders." In Eating Disorders and the Skin, 3–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29136-4_1.

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10

Bonham, Mary Ben. "Double-Skin Façade Classifications." In Bioclimatic Double-Skin Façades, 41–75. New York : Routledge, 2020.: Routledge, 2019. http://dx.doi.org/10.4324/9781315661384-3.

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Тези доповідей конференцій з теми "Skinks Classification"

1

Zhmakina, N. L., and K. S. Zdorovenko. "THE THEORETICAL STUDY OF THIRD-GRADERS’ COGNITIVE UNIVERSAL LEARNING SKILLS FORMATION TO SOLVE PROBLEMS IN MATH LESSONS." In Культура, наука, образование: проблемы и перспективы. Нижневартовский государственный университет, 2021. http://dx.doi.org/10.36906/ksp-2021/48.

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This article shows the theoretical basis of third-graders’ cognitive universal learning skills formation to solve problems. It describes the structure of problem-solving skills, classification of problems, solutions of problems, techniques that are used by primary school teachers, coordination between cognitive universal learning skills, and stages of problem-solving.
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2

Kumar, Ayushi, Ari Kapelyan, and Avimanyou K. Vatsa. "Classification of Skin Phenotype: Melanoma Skin Cancer." In 2021 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2021. http://dx.doi.org/10.1109/isec52395.2021.9763999.

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3

Ginigaddara, B., S. Perera, Y. Feng, and P. Rahnamayiezekavat. "Offsite construction skills prediction: A conceptual model." In 10th World Construction Symposium. Building Economics and Management Research Unit (BEMRU), University of Moratuwa, 2022. http://dx.doi.org/10.31705/wcs.2022.52.

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Industry 4.0 driven technological advancements have accelerated the uptake of Offsite Construction (OSC), causing the need for re-skilling, up-skilling, and multi-skilling traditional onsite construction skills and competencies. The purpose of this paper is to develop a conceptual model that predicts OSC skills as a response to the OSC demand. The paper is a theoretical presentation of a skill profile prediction model which introduces the key concepts, OSC typology, OSC skill classification and their relationships. Components, panels, pods, modules, and complete buildings represent the OSC typology. Managers, professionals, technicians, and trade workers, clerical and administration workers, machine operators and drivers, and labourers constitute the OSC skill classification. The conceptual model takes the OSC project parameters: gross floor area, OSC value percentage and skill quantities as input and provides predicted skill variations as the output. The skills are quantified in “manhours/m2” under six skill categories, for five distinct OSC types. As such, the research presents a comprehensive conceptual model for the development of an OSC skills predictor to capture the skill variations and demand in a construction market moving towards rapid industrialisation. The research contributes to the existing body of knowledge by identifying the key concepts, parameters, and mutual relationships of those parameters that are needed to develop a realistic prediction of future trends of OSC skills.
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4

Bissoto, Alceu, and Sandra Avila. "Improving Skin Lesion Analysis with Generative Adversarial Networks." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12986.

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Melanoma is the most lethal type of skin cancer. Early diagnosis is crucial to increase the survival rate of those patients due to the possibility of metastasis. Automated skin lesion analysis can play an essential role by reaching people that do not have access to a specialist. However, since deep learning became the state-of-the-art for skin lesion analysis, data became a decisive factor in pushing the solutions further. The core objective of this M.Sc. dissertation is to tackle the problems that arise by having limited datasets. In the first part, we use generative adversarial networks to generate synthetic data to augment our classification model’s training datasets to boost performance. Our method generates high-resolution clinically-meaningful skin lesion images, that when compound our classification model’s training dataset, consistently improved the performance in different scenarios, for distinct datasets. We also investigate how our classification models perceived the synthetic samples and how they can aid the model’s generalization. Finally, we investigate a problem that usually arises by having few, relatively small datasets that are thoroughly re-used in the literature: bias. For this, we designed experiments to study how our models’ use data, verifying how it exploits correct (based on medical algorithms), and spurious (based on artifacts introduced during image acquisition) correlations. Disturbingly, even in the absence of any clinical information regarding the lesion being diagnosed, our classification models presented much better performance than chance (even competing with specialists benchmarks), highly suggesting inflated performances.
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5

Dubal, Pratik, Sankirtan Bhatt, Chaitanya Joglekar, and Sonali Patil. "Skin cancer detection and classification." In 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2017. http://dx.doi.org/10.1109/iceei.2017.8312419.

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6

Gouda, Niharika, and J. Amudha. "Skin Cancer Classification using ResNet." In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA). IEEE, 2020. http://dx.doi.org/10.1109/iccca49541.2020.9250855.

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7

Ojha, Mritunjay Kumar, Dilrose Reji Karakattil, Akshat Devendra Sharma, and Sneha Mary Bency. "Skin Disease Detection and Classification." In 2022 IEEE India Council International Subsections Conference (INDISCON). IEEE, 2022. http://dx.doi.org/10.1109/indiscon54605.2022.9862834.

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8

Chen, Wei, Mohsen Ardabilian, Abdelmalek Zine, and Hassan Zahouani. "Reflectance spectra based skin and non-skin classification." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7350900.

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9

Yoon, Sangho, Michael Harville, Harlyn Baker, and Nina Bhatii. "Automatic Skin Pixel Selection and Skin Color Classification." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312630.

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10

Guo, Jiexin, and Prahlad G. Menon. "Feature Based Classification of Melanoma From Skin Images." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50055.

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Melanoma is one of the most deadly skin cancers and amounts for ∼79% of skin cancer deaths. Early detection and timely therapeutic action can reduce mortality owing to melanoma. In this study, we demonstrate the feasibility of our in-house skin image classification framework, trained based on a library of normal as well as pathological skin images, for automatic feature extraction and detection of melanoma. The described framework begins with active contour segmentation the skin images followed by extraction of both color and texture features from the segmented image and employs a neural network classifier to for trained identification of melanoma cases. Training and testing was conducted using a 10-fold cross validation strategy and led to 88.06% ± 1.65% accuracy in classification of melanoma images.
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Звіти організацій з теми "Skinks Classification"

1

Sheehan, Kathleen, Kikumi Tatsuoka, and Charles Lewis. A Diagnostic Classification Model for Document Processing Skills. Fort Belvoir, VA: Defense Technical Information Center, October 1993. http://dx.doi.org/10.21236/ada273790.

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2

Altamirano, Álvaro, and Nicole Amaral. A Skills Taxonomy for LAC: Lessons Learned and a Roadmap for Future Users. Inter-American Development Bank, November 2020. http://dx.doi.org/10.18235/0002898.

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This note brings together lessons from the IDBs and other institutions efforts to adapt a skills taxonomy for Latin America and the Caribbean countries. These efforts have focused primarily on the ability to gather and make use of labor market information on skills demand from non-traditional data sources like online job vacancies. Most of these efforts have used the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy to underpin the identification and classification of skills. This note is intended to be a starting point and set of considerations for policymakers who may be considering, or already embarking on, similar efforts to use ESCO or other taxonomical structures to help better analyze, understand and use skills-level information for decision making. It also seeks to motivate the need for additional classification systems that help governments take stock of its citizens skills in increasingly complex and rapidly changing labor markets.
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3

Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, October 2020. http://dx.doi.org/10.36967/nrr-2279201.

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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&M program. The Mojave Desert Network Inventory & Monitoring (MOJN I&M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub & Herb Dry Wash & Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.
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4

Nechypurenko, Pavlo, Tetiana Selivanova, and Maryna Chernova. Using the Cloud-Oriented Virtual Chemical Laboratory VLab in Teaching the Solution of Experimental Problems in Chemistry of 9th Grade Students. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3175.

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The article discusses the importance of the skills of primary school students to solve experimental problems in chemistry and the conditions for the use of virtual chemical laboratories in the process of the formation of these skills. The concept of “experimental chemical problem” was analyzed, classifications were considered, and methodological conditions for using experimental chemical problems in the process of teaching chemistry were described. The essence of the concept of “virtual chemical laboratories” is considered and their main types, advantages and disadvantages that define the methodically reasonable limits of the use of these software products in the process of teaching chemistry, in particular, to support the educational chemical experiment are described. The capabilities of the virtual chemical laboratory VLab to support the process of solving experimental problems in chemistry in grade 9 have been determined. The main advantages and disadvantages of the virtual chemical laboratory VLab on the modeling of chemical processes necessary for the creation of virtual experimental problems in chemistry are analyzed. The features of the virtual chemical laboratory VLab, the essence of its work and the creation of virtual laboratory work in it are described. The results of the study is the development of a set of experimental tasks in chemistry for students in grade 9 on the topic “Solutions” in the cloud-oriented virtual chemical laboratory VLab.
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5

Kumar, Indraneel, Lionel Beaulieu, Annie Cruz-Porter, Chun Song, Benjamin St. Germain, and Andrey Zhalnin. An Assessment of the Workforce and Occupations in the Highway, Street, and Bridge Construction Industries in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284315018.

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This project explores workforce and occupations within the highway, street, and bridge construction industries (NAICS 237310) in Indiana. There are five specific deliverable comprised of three data reports, one policy document, and a website. The first data report includes an assessment of the workforce based on the eight-part framework, which are industry, occupations, job postings, hard-to-fill jobs, Classification of Instructional Programs (CIP), GAP Analysis, compatibility, and automation. The report defines a cluster followed by a detailed analysis of the occupations, skills, job postings, etc., in the NAICS 237310 industry in Indiana. The report makes use of specialized labor market databases, such as the Economic Modeling Specialists International (EMSI), CHMURA JobsEQ, etc. The analysis is based only on the jobs covered under the unemployment insurance or the Quarterly Census of Employment and Wages (QCEW) data. The second data report analyzes jobs to jobs flows to and from the construction industry in Indiana, with a particular emphasis on the Great Recession, by utilizing the Bureau of Labor Statistics (BLS) data. The third data report looks into the equal employment opportunity or Section 1391 and 1392 data for Indiana and analyzes specific characteristics of that data. The policy report includes a set of recommendations for workforce development for INDOT and a summary of the three data reports. The key data on occupations within the NAICS 237310 are provided in an interactive website. The website provides a data dashboard for individual INDOT Districts. The policy document recommends steps for development of the highways, streets and bridges construction workforce in INDOT Districts.
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6

Kiianovska, N. M. The development of theory and methods of using cloud-based information and communication technologies in teaching mathematics of engineering students in the United States. Видавничий центр ДВНЗ «Криворізький національний університет», December 2014. http://dx.doi.org/10.31812/0564/1094.

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The purpose of the study is the analysis of the development of the theory and methods of ICT usage while teaching higher mathematics engineering students in the United States. It was determined following tasks: to analyze the problem source, to identify the state of its elaboration, to identify key trends in the development of theory and methods of ICT usage while teaching higher mathematics engineering students in the United States, the object of study – the use of ICT in teaching engineering students, the research methods are: analysis of scientific, educational, technical, historical sources; systematization and classification of scientific statements on the study; specification, comparison, analysis and synthesis, historical and pedagogical analysis of the sources to establish the chronological limits and implementation of ICT usage in educational practice of U.S. technical colleges. In article was reviewed a modern ICT tools used in learning of fundamental subjects for future engineers in the United States, shown the evolution and convergence of ICT learning tools. Discussed experience of the «best practices» using online ICT in higher engineering education at United States. Some of these are static, while others are interactive or dynamic, giving mathematics learners opportunities to develop visualization skills, explore mathematical concepts, and obtain solutions to self-selected problems. Among ICT tools are the following: tools to transmit audio and video data, tools to collaborate on projects, tools to support object-oriented practice. The analysis leads to the following conclusion: using cloud-based tools of learning mathematic has become the leading trend today. Therefore, university professors are widely considered to implement tools to assist the process of learning mathematics such properties as mobility, continuity and adaptability.
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