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quora-insincere-questions-classification test data for educational purpose Credits: https://www.kaggle.com/c/quora-insincere-questions-classification/

Well we're back, and in true aussie fashion we are fucking late. Oh well, I'm sure our one fan will be consoled by the fact that this may possibly be our most Australian podcast yet, if only for the mention of Stack Hats. If you don't know what they are you led a deprived and sad life. Anyways, on with the show! Shit we talk about: - STACKHATS!! - We love old school games - Silent Hill Homecoming, it aint no 5 - Sam and Max = God - Scratch that, Mike Patton = God - Duke Forever, RIP - Ahhhh, [url=http://gamingisstupid.com/2009/05/06/the-chair-story-revival/]that chair story[/url]. - We get pissy about Ghostbusters, then at eachother - Where the fuck is our MvC2 demo? - The RC Spotlight: Tits in games. Seriously. - Dillinger Drops his phone number again. Somebody call the bastard. - Listener questions yaaaaayyyy! - AnimuCast! - I know too much about Luchadores Intro Music: Bionic Commando: Rearmed OST - Power Plant Break Music: Selkies the endless obsessions - Between The Buried And Me. 8Bit Version by [url=http://www.youtube.com/user/xTwilightxxPrincessx]xtwilightxxprincessx[/url] Outro Music: Bulb - Illustrated 8 Test

Well we're back, and in true aussie fashion we are fucking late. Oh well, I'm sure our one fan will be consoled by the fact that this may possibly be our most Australian podcast yet, if only for the mention of Stack Hats. If you don't know what they are you led a deprived and sad life. Anyways, on with the show! Shit we talk about: - STACKHATS!! - We love old school games - Silent Hill Homecoming, it aint no 5 - Sam and Max = God - Scratch that, Mike Patton = God - Duke Forever, RIP - Ahhhh, [url=http://gamingisstupid.com/2009/05/06/the-chair-story-revival/]that chair story[/url]. - We get pissy about Ghostbusters, then at eachother - Where the fuck is our MvC2 demo? - The RC Spotlight: Tits in games. Seriously. - Dillinger Drops his phone number again. Somebody call the bastard. - Listener questions yaaaaayyyy! - AnimuCast! - I know too much about Luchadores Intro Music: Bionic Commando: Rearmed OST - Power Plant Break Music: Selkies the endless obsessions - Between The Buried And Me. 8Bit Version by [url=http://www.youtube.com/user/xTwilightxxPrincessx]xtwilightxxprincessx[/url] Outro Music: Bulb - Illustrated 8 Test

Well we're back, and in true aussie fashion we are fucking late. Oh well, I'm sure our one fan will be consoled by the fact that this may possibly be our most Australian podcast yet, if only for the mention of Stack Hats. If you don't know what they are you led a deprived and sad life. Anyways, on with the show! Shit we talk about: - STACKHATS!! - We love old school games - Silent Hill Homecoming, it aint no 5 - Sam and Max = God - Scratch that, Mike Patton = God - Duke Forever, RIP - Ahhhh, [url=http://gamingisstupid.com/2009/05/06/the-chair-story-revival/]that chair story[/url]. - We get pissy about Ghostbusters, then at eachother - Where the fuck is our MvC2 demo? - The RC Spotlight: Tits in games. Seriously. - Dillinger Drops his phone number again. Somebody call the bastard. - Listener questions yaaaaayyyy! - AnimuCast! - I know too much about Luchadores Intro Music: Bionic Commando: Rearmed OST - Power Plant Break Music: Selkies the endless obsessions - Between The Buried And Me. 8Bit Version by [url=http://www.youtube.com/user/xTwilightxxPrincessx]xtwilightxxprincessx[/url] Outro Music: Bulb - Illustrated 8 Test

Class 2C. Tree Selection. Keen Ponderosa Pine Tree Classification. Photo by: F.P. Keen Date: September 1934 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Bureau of Entomology Collection; La Grande, Oregon. Image: BUR-8712 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth The following information about the Keen Classification system is excerpted from Wickman, Boyd E. 2005. Harry E. Burke and John M. Miller, pioneers in Western forest entomology. Gen. Tech. Rep. PNW-GTR-638. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 163 p.: "Keen’s research on tree classification continued in Oregon and was called the “Keen Ponderosa Pine Tree Classification.” The Salman-Bongberg approach was called the “California Pine Risk-Rating System.” Both systems evolved from research on tree susceptibility to bark beetle attack that had been conducted in the 1920s by Miller, Keen, Patterson, Person, and Struble. Tree growth rates were a particularly important area of the studies, with Miller and Person proposing that slower growing mature ponderosa pine were more susceptible to attack by the western pine beetle than younger more vigorous trees (Smith et al. 1981). The rating systems resulted in a silvicultural approach to managing the western pine beetle instead of the direct control method of felling infested trees, then peeling and burning the bark. This was a breakthrough for forest managers that allowed them to capture the economic value of susceptible trees before they were killed by bark beetles, and at the same time resulted in decreased beetle populations or at least seemed to prevent outbreaks. The Salman- Bongberg California risk-rating system identified susceptible trees by assigning penalty points to crown and stem characteristics of a tree. This was followed up by a logging practice called sanitation salvage (Smith et al. 1981). The Keen system, involved four classes based on age and four vigor classes within each age class. It was oriented more toward identifying the susceptibility of individual trees to insect attack. Keen also assigned penalty points in his system. It was slightly more complex to use and was not directly tied to the sanitation salvage concept, but a certain level of logging of susceptible trees was generally followed. Keen’s classification was more widely used in ponderosa pine stands of eastern Oregon, and the California system was used for both ponderosa pine and Jeffrey pine stands in eastern California." For more, see: www.fs.fed.us/pnw/publications/pnw_gtr638/ For more about Keen's classifications see: www.fs.fed.us/psw/publications/ecology_of.../psw_1943_kee...

Map 2. Intensity of Blowdown in Areas Covered by The Survey. Classification is based on approximate number of acres per square mile on which 10% or more of the stand is recent blowdown. The data have been generalized for minimum areas of about 3,500 acres from 1"-per-mile scale maps. Map is from: Summary Statement on the 1952 Blowdown-Bark-Beetle Survey in the Douglas-fir Region of Oregon and Washington. Prepared by: Pacific Northwest Forest and Range Experiment Station, US Forest Service; and Portland Forest Insect Laboratory, Bureau of Entomology and Plant Quarantine. Portland, Oregon. 7 pages. Photo by: Unknown Map creation date: 1952 Credit: USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection. Source: Aerial Survey Program digital collection. Image provided by USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Map 2. Intensity of Blowdown in Areas Covered by The Survey. Classification is based on approximate number of acres per square mile on which 10% or more of the stand is recent blowdown. The data have been generalized for minimum areas of about 3,500 acres from 1"-per-mile scale maps. Map is from: Summary Statement on the 1952 Blowdown-Bark-Beetle Survey in the Douglas-fir Region of Oregon and Washington. Prepared by: Pacific Northwest Forest and Range Experiment Station, US Forest Service; and Portland Forest Insect Laboratory, Bureau of Entomology and Plant Quarantine. Portland, Oregon. 7 pages. Photo by: Unknown Map creation date: 1952 Credit: USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection. Source: Aerial Survey Program digital collection. Image provided by USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Map 2. Intensity of Blowdown in Areas Covered by The Survey. Classification is based on approximate number of acres per square mile on which 10% or more of the stand is recent blowdown. The data have been generalized for minimum areas of about 3,500 acres from 1"-per-mile scale maps. Map is from: Summary Statement on the 1952 Blowdown-Bark-Beetle Survey in the Douglas-fir Region of Oregon and Washington. Prepared by: Pacific Northwest Forest and Range Experiment Station, US Forest Service; and Portland Forest Insect Laboratory, Bureau of Entomology and Plant Quarantine. Portland, Oregon. 7 pages. Photo by: Unknown Map creation date: 1952 Credit: USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection. Source: Aerial Survey Program digital collection. Image provided by USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Remote-sensing analysis is conducted for the Naval Postgraduate School campus, containing buildings, impervious surfaces (asphalt and concrete), natural ground, and vegetation. Data is from the Optech Titan, providing three-wavelength laser data (532, 1064, and 1550 nm) at 10–15 points/m2. Analysis techniques for laser-scanner (LiDAR) data traditionally use only x, y, z coordinate information. The traditional approach is used to initialize the classification process into broad-spatial classes (unclassified, ground, vegetation, and buildings). Spectral analysis contributes a unique approach to the classification process. Tools and techniques designed for multispectral imagery are adapted to the LiDAR analysis herein. ENVI's N-Dimensional Visualizer is employed to develop training sets for supervised classification techniques, primarily Maximum Likelihood. Unsupervised classification for the combined spatial/spectral data is accomplished using a K-means classifier for comparison. The campus is classified into 10 and 16 classes, compared to the four from traditional methods. Addition of the spectral component improves the discrimination among impervious surfaces, other ground elements, and building materials. Maximum Likelihood demonstrates 75% overall classification accuracy, with grass (99.9%), turf (95%), asphalt shingles (94%), light-building concrete (89%), sand (88%), shrubs (85%), asphalt (84%), trees (80%), and clay-tile shingles (77%). Post-process filtering by number of returns increases overall accuracy to 82%.

Document from Tamil Nadu Legislative Assembly Digital Archive

Document from Tamil Nadu Legislative Assembly Digital Archive

In this study, Doppler signals were recorded from 40 healthy volunteers and the right and left hand ulnar and radial arteries of 40 rheumatoid arthritis patients. Multiple Signal Classification method, one of the subspace signal processing methods is applied to the obtained Doppler signals and the feature of signs has been reached. Diseased and healthy people have been distinguished by using three different normalization techniques, including (z-score, minimum-maximum and decimal scaling) and artificial neural networks classification. K-fold cross-validation, classification accuracy, sensitivity and specificity are used to interpret and described the results of medical diagnostic test. Ali Osman Özkan"Effect of Normalization Techniques on Multilayer Perceptron Neural Network Classification Performance for Rheumatoid Arthritis Disease Diagnosis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd3576.pdf Article URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/3576/effect-of-normalization-techniques-on-multilayer-perceptron-neural-network-classification-performance-for-rheumatoid-arthritis-disease-diagnosis/ali-osman-özkan

In this paper, an automated supervised image classification technique, specifically for classifying images in the cultural heritage domain, is developed. The developed technique classifies images according to a particular date, culture, people and historical age. The proposed technique consists of two stages, feature extraction using the unsupervised segmentation technique, and the classification stage using supervised classification techniques. Common features are extracted, and their histograms are applied to three classifiers: k-nearest neigh bor (KNN), logistic regression (LR), and decision tree (DT). When our technique was applied to a repository of images from cultural heritage, it showed reduced complexity and improved classification accuracy. DT has achieved a higher weighted average recall. This is also represented by the weighted average f-measure where DT has obtained 0.81. DT has outperformed the other classifiers in terms of classifying heritage images.

Facial expression is a kind of nonverbal communication that conveys information about a person's emotional state. Human emotion detection and recognition remains a major task in computer vision (CV) and artificial intelligence (AI). To recognize and identify the many sorts of emotions, several algorithms are proposed in the literature. In this paper, the modified Viola-Jones method is introduced to provide a robust approach capable of detecting and identifying human feelings such as angerness, sadness, desire, surprise, anxiety, disgust, and neutrality in real-time. This technique captures real-time pictures and then extracts the characteristics of the facial image to identify emotions very accurately. In this method, many feature extraction techniques like gray-level co-occurrence matrix (GLCM), linear binary pattern (LBP) and robust principal components analysis (RPCA) are applied to identify the distinct mood states and they are categorized using a convolution neural network (CNN) classifier. The obtained outcome demonstrates that the proposed method outperforms in terms of determining the rate of emotion recognition as compared to the current human emotion recognition techniques.

Today in the entire world in numerous territories, Breast malignancy is a main source of disease especially in lady in which most normal bosom anomalies are masses and calcifications. Early location of determination is the way to bosom malignancy controls that expansion the accomplishment of treatment, spare lives and diminish cost. It is troublesome for the radiologist to perceive the majority on a mammogram since they are encompassed by muddled tissues, thus numerous frameworks have been produced to help the radiologist in distinguishing mammography injuries that may demonstrate the nearness of bosom disease. The exploratory outcomes demonstrate that the proposed CAD framework enormously enhances the five target records in correlation with mass recognition and characterizations framework for mammography picture preprocessing. S. Thilagavathi | Dr. M. S. Irfan Ahmed"Mass Detection and Classification System for Mammography Image Preprocessing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8223.pdf  http://www.ijtsrd.com/medicine/other/8223/mass-detection-and-classification-system-for-mammography-image-preprocessing/s-thilagavathi

Today in the entire world in numerous territories, Breast malignancy is a main source of disease especially in lady in which most normal bosom anomalies are masses and calcifications. Early location of determination is the way to bosom malignancy controls that expansion the accomplishment of treatment, spare lives and diminish cost. It is troublesome for the radiologist to perceive the majority on a mammogram since they are encompassed by muddled tissues, thus numerous frameworks have been produced to help the radiologist in distinguishing mammography injuries that may demonstrate the nearness of bosom disease. The exploratory outcomes demonstrate that the proposed CAD framework enormously enhances the five target records in correlation with mass recognition and characterizations framework for mammography picture preprocessing. S. Thilagavathi | Dr. M. S. Irfan Ahmed"Mass Detection and Classification System for Mammography Image Preprocessing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8223.pdf  http://www.ijtsrd.com/medicine/other/8223/mass-detection-and-classification-system-for-mammography-image-preprocessing/s-thilagavathi

57 p. : 25 cm

Features are the basis on which grouping is done to produce accurate results. The main goal of the feature selection is to determine the minimal features that are more efficient and could render high accuracy when compared with the whole set of features. Rough sets could only be considered as the effective tool for feature selection. Nowadays feature selection algorithms based on rough sets are prevailing. When they are considered on some analysis, it reveals that they could make high cost and much time to be worked out, as it suffers from intensive and exponential computation.   For the purpose of eliminating the disadvantagesof these existing algorithms, a new algorithm called Feature Selection Models with Relative Decision Entropy is proposed. This algorithm is mainly based on roughness and degree of dependency, that includes both positive and boundary region calculation. It implies that this algorithm could provide good scalability for large data sets and lessens the cost along with the computation time.

Bibliography: p. 23-24

The internal inspection of fabrics is one of the most important phases of production in order to achieve high quality standard in the textile industry. Therefore, developing efficient automatic internal control mechanism has been an extremely major area of research. In this paper, the famous architecture GoogLeNet was fine-tuned into two configurations for texture defect classification that was trained on a textile texture database (TILDA). The experimental result, for both configurations, achieved a significant overall accuracy score of 97% for motif and a non-motif-based images and 89% for mixed images. In the results obtained, it was observed that the second model, which updates the last six layers, was more successful than the first one; which updates the last two layers.

33 leaves. ; 28 cm

Zoomable full resolution image available at davidrumsey.com . This image 6322.181 is a part of image group 6322.000 . Color. Relief shown in contours. Topography surveyed 1897-99. Includes 711 + xi pages, 39 maps. Accompanying Papers: Lewis and Clarke Forest Reserve, Montana, by H.B, Ayers, pages 27-80. Mount Rainier Forest Reserve, Washington, by Fred G. Plummer, pages 81-144. Olympic Forest Reserve, Washington, by Arthur Dodwell, pages 145-208. Cascade Range Forest Reserve, by John B. Leiberg, pages 209-498. Stanislaus and Lake Tahoe Forest Reserve, by George B. Sudworth, pages 499-562. Classification of Lands, by Henry Gannett and others, pages 563-602. Woodland of Indian Territory, by C.H. Fitch, pages 603-672. Timber Conditions of the Pine Region of Minnesota, by H.B.Ayers, pages 673-690.

Zoomable full resolution image available at davidrumsey.com . This image 6322.020 is a part of image group 6322.000 . Color. Relief shown in hachures and contours. Shows forest yield in B.M. (board measure or board feet). Includes 711 + xi pages, 39 maps. Accompanying Papers: Lewis and Clarke Forest Reserve, Montana, by H.B, Ayers, pages 27-80. Mount Rainier Forest Reserve, Washington, by Fred G. Plummer, pages 81-144. Olympic Forest Reserve, Washington, by Arthur Dodwell, pages 145-208. Cascade Range Forest Reserve, by John B. Leiberg, pages 209-498. Stanislaus and Lake Tahoe Forest Reserve, by George B. Sudworth, pages 499-562. Classification of Lands, by Henry Gannett and others, pages 563-602. Woodland of Indian Territory, by C.H. Fitch, pages 603-672. Timber Conditions of the Pine Region of Minnesota, by H.B.Ayers, pages 673-690.

Zoomable full resolution image available at davidrumsey.com . This image 6322.117 is a part of image group 6322.000 . Color. Relief shown in contours. Includes 711 + xi pages, 39 maps. Accompanying Papers: Lewis and Clarke Forest Reserve, Montana, by H.B, Ayers, pages 27-80. Mount Rainier Forest Reserve, Washington, by Fred G. Plummer, pages 81-144. Olympic Forest Reserve, Washington, by Arthur Dodwell, pages 145-208. Cascade Range Forest Reserve, by John B. Leiberg, pages 209-498. Stanislaus and Lake Tahoe Forest Reserve, by George B. Sudworth, pages 499-562. Classification of Lands, by Henry Gannett and others, pages 563-602. Woodland of Indian Territory, by C.H. Fitch, pages 603-672. Timber Conditions of the Pine Region of Minnesota, by H.B.Ayers, pages 673-690.

Zoomable full resolution image available at davidrumsey.com . This image 6322.117 is a part of image group 6322.000 . Color. Relief shown in contours. Includes 711 + xi pages, 39 maps. Accompanying Papers: Lewis and Clarke Forest Reserve, Montana, by H.B, Ayers, pages 27-80. Mount Rainier Forest Reserve, Washington, by Fred G. Plummer, pages 81-144. Olympic Forest Reserve, Washington, by Arthur Dodwell, pages 145-208. Cascade Range Forest Reserve, by John B. Leiberg, pages 209-498. Stanislaus and Lake Tahoe Forest Reserve, by George B. Sudworth, pages 499-562. Classification of Lands, by Henry Gannett and others, pages 563-602. Woodland of Indian Territory, by C.H. Fitch, pages 603-672. Timber Conditions of the Pine Region of Minnesota, by H.B.Ayers, pages 673-690.

1 volume (various pagings) 28 cm

Document number CIA-RDP66R00638R000100110068-7 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP67-00059A000100100029-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP57-00384R000400160109-3 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP57-00384R000400160109-3 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

43 p

pages cm

Book digitized by Google from the library of the University of Michigan and uploaded to the Internet Archive by user tpb.

Protecting against threats requires effective classification methods which identify different forms of malicious software as part of cybersecurity operations. Signature-based detection methods that traditional approaches use struggle to detect new as well as previously unseen malware variants. Deep learning features convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as a strong substitute for malware classification because these networks automatically detect sophisticated patterns in raw data. Deep learning neural networks which contain CNN layers excel at extracting spatial features from malware binaries together with images but the RNN components excel at monitoring sequential dependencies within API call sequences as well as network traffic patterns. The research examines CNNs and RNNs in malware identification through an evaluation of their functional advantages and operational difficulties and assessment of their malware categorization performance. Deep learning technology enhances this framework because it provides superior accuracy while maintaining scalability and responsiveness against modern complex malware threats.

In the modern era, the issue of road accidents has become an increasingly critical global concern, requiring urgent attention and innovative solutions. This investigation has compiled an extensive dataset of 10,356 accident occurrences that occurred between the years 2018 and 2022 in Ernakulam district. By utilizing advanced feature selection methodologies, such as genetic algorithm and coyote optimization, this research has identified pivotal accident determinants. The study harnesses the potential of deep learning techniques, encompassing recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and multilayer perceptron (MLP) for classifying accidents according to severity (categorized as fatal, grievous, and severe). Eight predictive models are trained using the dataset, and the top two are ensembled. Integrating deep learning and optimization strategies, this research aims to create a robust accident classification system. The system will help in developing proactive policies that can reduce the frequency and severity of accidents in Ernakulam district

In the modern era, the issue of road accidents has become an increasingly critical global concern, requiring urgent attention and innovative solutions. This investigation has compiled an extensive dataset of 10,356 accident occurrences that occurred between the years 2018 and 2022 in Ernakulam district. By utilizing advanced feature selection methodologies, such as genetic algorithm and coyote optimization, this research has identified pivotal accident determinants. The study harnesses the potential of deep learning techniques, encompassing recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and multilayer perceptron (MLP) for classifying accidents according to severity (categorized as fatal, grievous, and severe). Eight predictive models are trained using the dataset, and the top two are ensembled. Integrating deep learning and optimization strategies, this research aims to create a robust accident classification system. The system will help in developing proactive policies that can reduce the frequency and severity of accidents in Ernakulam district

Declassified US intelligence documents related to China found in the US National Archives.

Declassified US intelligence documents related to China found in the US National Archives.

Addressed in this paper is an information-theoretics inspired entropy co-occurrence approach to extract subset features denoting relative extents of gene expression levels in a DNA microarray data. Experimentally deduced microarray data (via wet-lab studies) exhibits intensity gradation seen as yellow, green and red spots. Such distinguishable (color) features of microarray patterns depict the associated heterogeneity of meso textures and their spatial distribution manifested as intensity gradation in the colored spots. Normally, a procedure is sought to distinguish two such patterns of microarrays in which the colored spots are distinctly expressed. For this purpose, considered in this study is the so-called Haralick’s feature-finding algorithms and deducing Haralick’s feature scores in terms of the entropy co-occurrence information in an image with reference to a threshold profile. The scores obtained from intensity gradations of colored spots can be correlated to the relative extent of the color status of the spots. Hence corresponding gene expression (depicting tumor, non-tumor or mixed condition etc. as implied by proportional abundance of color pixels in the microarray) is quantitatively indicated. Relevant strategy in the use of Haralick’s algorithms in microarray contexts is a new and novel approach. Details on simulation experiments using synthetic microarray patterns and results obtained thereof are presented to illustrate the efficacy of the proposed method.

Includes indexes

Includes indexes

36 p. ; 16 cm

Document number CIA-RDP93B01194R000900010059-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240017-7 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP93B01194R001000240037-5 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Folder: REMOTE VIEWING SESSION DATA. SITE # 0110. REMOTE VIEWER: 095; STAR GATE was an umbrella term for the Intelligence Community effort that used remote viewers who claimed to use clairvoyance, precognition, or telepathy to acquire and describe information about targets that were blocked from ordinary perception. The records include documentation of remote viewing sessions, training, internal memoranda, foreign assessments, and program reviews. The STAR GATE program was also called SCANATE, GONDOLA WISH, DRAGOON ABSORB, GRILL FLAME, CENTER LANE, SUN STREAK. Files were released through CREST and obtained as TIF files by the Black Vault and converted to PDF by That 1 Archive.

Document number CIA-RDP83B00823R000200060220-3 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP83B00823R000200060220-3 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP83B00823R000200060220-3 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

xiii, 240 p