Data Mining Techniques In Computer Aided Cancer Diagnosis
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Data mining techniques are used in computer aided cancer diagnosis and detection. They help physicians interpret complex diagnoses, combine information from multiple sources, and provide support for differential diagnosis. Specific techniques like neural networks, decision trees, and cluster detection are used in ALL diagnosis. Data mining can also be applied to detect gastric and breast cancer by analyzing features in images and using case-based reasoning. National Cancer Institute created 3D mind tools to help scientists detect compounds related to cancer and compare their activities.
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Data Mining Techniques In Computer Aided Cancer Diagnosis
Data mining techniques are used in computer aided cancer diagnosis and detection. They help physicians interpret complex diagnoses, combine information from multiple sources, and provide support for differential diagnosis. Specific techniques like neural networks, decision trees, and cluster detection are used in ALL diagnosis. Data mining can also be applied to detect gastric cancer using single nucleotide polymorphism information. It helps organize healthcare claims data to detect cancer patterns and evaluate treatment efficacy. New applications of data mining and neural networks are also helping detect cancers like breast cancer sooner.
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The document discusses several active areas of work in artificial intelligence applications in radiology at Brown University's AI radiology lab, including COVID-19 detection from chest x-rays, tumor assessment, stroke diagnosis, and more. It provides details on techniques like contrast dropout to deal with missing data, human-in-the-loop approaches, automatic quality estimation, treatment response evaluation, and federated learning to share models without sharing patient data. Performance results and example visualizations from various models are also included.
Ph.D. Defense Presentation Slides (Changhee Han) カリスの東大博論審査会（公聴会）発表スライド Patho...
東京大学情報理工学系研究科創造情報学専攻における、僕の博士論文審査会（公聴会）発表スライド。
Ph.D. Defense Video in Japanese (約50分の審査会発表動画) → https://youtu.be/mXI2j0lkEtk
16歳で東大合格して、10年経ってやっと東大AI博士になれた！！！（感想動画）→ https://youtu.be/LYfeXwal4fc
Ph.D. Thesis (博士論文) → to be released (近日公開）
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation（医用画像拡張に向けた、病変部を意識した敵対的生成ネットワーク）
残念ながら医療に誤診は付き物。
というのも、病気は無限に種類があり、医療には普遍的解釈がないから。
そこで、僕は誤診を激減させ、みんな健康かつ笑顔で暮らせる社会を実現すべく、AI、つまり病変部を意識したGenerative Adversarial Networks (GANs) で、望みの疾患を持つ合成医用画像をガンガン生成した。
これらの合成医用画像は「データ拡張」を通して「コンピュータ支援診断」にも、「医師教育」を通して「医師による診断」にも役立てられる。
どの応用にも内挿と外挿の両方が大切だが、データ拡張では特に多様性、医師教育では特にリアルさを重視する必要がある。
こうした内挿と外挿を効果的に行うことこそが、僕の博論の肝だった。
分野の特性としてデータ不足が避けられない以上、医用画像解析の未来を担うであろう情報変換において、本研究は内挿と外挿それぞれの側面から、すでに先駆的な研究となりつつある。
Noise-to-image GANsによる優れた内挿効果と、病変部を意識したGANsによる優れた外挿効果、バウンディングボックスによる大幅な医師のアノテーション労力削減効果を組み合わせたことには、AIの臨床応用に向けた確固とした哲学と有効性が確認できる。
博論の貢献は3つ。
1. Noise-to-image GANsを用いた重要な臨床応用であるデータ拡張と医師教育を提案し、有用性を検証した
2. それらに必要な外挿を行うべく、常に医師と連携し、腫瘍や結節といった疾患をコントロールする斬新な二次元と三次元のGANsを提案した
3. 多くの医師と議論を重ね、病変部を意識したGANsの臨床意思決定支援システムおよび非熟練医の教育ツールとしての、臨床的関連性を確認した
YouTube → https://t.co/2DNFdSsTC5
研究者ページ → http://changheehan.strikingly.com
Twitter → @TricksterKallis
ご依頼 → Trickster.Kallis@gmail.com
ご質問 → 「#カリスナー」でTwitter投稿
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Data Mining Techniques In Computer Aided Cancer Diagnosis
Data mining techniques are used in computer aided cancer diagnosis and detection. They help physicians interpret complex diagnoses, combine information from multiple sources, and provide support for differential diagnosis. Specific techniques like neural networks, decision trees, and cluster detection are used in ALL diagnosis. Data mining can also be applied to detect gastric cancer using single nucleotide polymorphism information. It helps organize healthcare claims data to detect cancer patterns and evaluate treatment efficacy. New applications of data mining and neural networks are also helping detect cancers like breast cancer sooner.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...
The document discusses several active areas of work in artificial intelligence applications in radiology at Brown University's AI radiology lab, including COVID-19 detection from chest x-rays, tumor assessment, stroke diagnosis, and more. It provides details on techniques like contrast dropout to deal with missing data, human-in-the-loop approaches, automatic quality estimation, treatment response evaluation, and federated learning to share models without sharing patient data. Performance results and example visualizations from various models are also included.
Ph.D. Defense Presentation Slides (Changhee Han) カリスの東大博論審査会（公聴会）発表スライド Patho...
東京大学情報理工学系研究科創造情報学専攻における、僕の博士論文審査会（公聴会）発表スライド。
Ph.D. Defense Video in Japanese (約50分の審査会発表動画) → https://youtu.be/mXI2j0lkEtk
16歳で東大合格して、10年経ってやっと東大AI博士になれた！！！（感想動画）→ https://youtu.be/LYfeXwal4fc
Ph.D. Thesis (博士論文) → to be released (近日公開）
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation（医用画像拡張に向けた、病変部を意識した敵対的生成ネットワーク）
残念ながら医療に誤診は付き物。
というのも、病気は無限に種類があり、医療には普遍的解釈がないから。
そこで、僕は誤診を激減させ、みんな健康かつ笑顔で暮らせる社会を実現すべく、AI、つまり病変部を意識したGenerative Adversarial Networks (GANs) で、望みの疾患を持つ合成医用画像をガンガン生成した。
これらの合成医用画像は「データ拡張」を通して「コンピュータ支援診断」にも、「医師教育」を通して「医師による診断」にも役立てられる。
どの応用にも内挿と外挿の両方が大切だが、データ拡張では特に多様性、医師教育では特にリアルさを重視する必要がある。
こうした内挿と外挿を効果的に行うことこそが、僕の博論の肝だった。
分野の特性としてデータ不足が避けられない以上、医用画像解析の未来を担うであろう情報変換において、本研究は内挿と外挿それぞれの側面から、すでに先駆的な研究となりつつある。
Noise-to-image GANsによる優れた内挿効果と、病変部を意識したGANsによる優れた外挿効果、バウンディングボックスによる大幅な医師のアノテーション労力削減効果を組み合わせたことには、AIの臨床応用に向けた確固とした哲学と有効性が確認できる。
博論の貢献は3つ。
1. Noise-to-image GANsを用いた重要な臨床応用であるデータ拡張と医師教育を提案し、有用性を検証した
2. それらに必要な外挿を行うべく、常に医師と連携し、腫瘍や結節といった疾患をコントロールする斬新な二次元と三次元のGANsを提案した
3. 多くの医師と議論を重ね、病変部を意識したGANsの臨床意思決定支援システムおよび非熟練医の教育ツールとしての、臨床的関連性を確認した
YouTube → https://t.co/2DNFdSsTC5
研究者ページ → http://changheehan.strikingly.com
Twitter → @TricksterKallis
ご依頼 → Trickster.Kallis@gmail.com
ご質問 → 「#カリスナー」でTwitter投稿
A Novel Approach for Breast Cancer Detection using Data Mining Techniques
The document presents an agenda for a talk on using data mining techniques for breast cancer detection. It begins with background on cancer and breast cancer. It then discusses pattern recognition, data mining tools like Weka, and various classification techniques including Naive Bayes, SVM, C4.5, KNN, BF tree, and IBK that could be used. The talk will cover preprocessing data, feature selection, applying classification algorithms, and evaluating performance. The goal is to introduce a novel approach for breast cancer detection using these data mining methods.
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Tools, methods, algorithms, software for integrative analysis of Pathology, Radiology, "omics" and outcome data.
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This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
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developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
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According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
How does machine learning help in cancer detection
Machine learning helps in cancer detection and prognosis in several ways. It can analyze large datasets to identify patterns related to cancer susceptibility, recurrence, and survivability. Supervised and unsupervised machine learning have been used to extract meaningful features from gene expression and medical imaging data to classify cancers and segment tumors. This reduces errors, improves accuracy, and eliminates manual, time-consuming analysis compared to methods relying solely on human expertise. Machine learning is advancing medical research and cancer treatment by allowing vast amounts of data to be processed efficiently.
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This document provides an overview of a radiogenomic imaging prototype that utilizes data from The Cancer Imaging Archive (TCIA). It discusses how DICOM images contain valuable metadata and how radiomics aims to extract higher dimensional data from images for improved decision support. The prototype would allow segmentation and feature extraction of images from TCIA and correlate these with genomic and pathology data from The Cancer Genome Atlas. Within 6 months, the goal is to create a standalone Python application that demonstrates key imaging analysis capabilities and is compatible with TCIA data standards.
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This document discusses pathomics-based biomarkers, tools, and methods for multi-scale integrative analysis in biomedical informatics. It summarizes several projects involving extracting quantitative features from pathology and radiology images using image segmentation and analysis techniques. These features are then linked to molecular data and clinical outcomes using statistical and machine learning methods to develop biomarkers. The tools and methods described aim to standardize and optimize feature extraction while accounting for uncertainties.
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Big Data Provides Opportunities, Challenges and a Better Future in Health and...
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...
This document discusses applying machine learning algorithms to predict chronic kidney disease. It:
1) Applied three algorithms (C4.5 decision tree, SVM, and Bayesian Network) to a chronic kidney disease dataset containing 400 patients and 24 attributes to classify patients as having chronic kidney disease or not.
2) Found that the C4.5 decision tree algorithm had the best performance based on accuracy (63%), error rate (0.37), kappa statistic (0.97), and other evaluation metrics. SVM and Bayesian Network performance was lower.
3) Concludes C4.5 decision tree is the most efficient algorithm for predicting chronic kidney disease based on this medical dataset.
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Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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Explains how Cancer Management can be made more effective using an integrated approach. From collecting data across reports to predict tumor growth and to be able to help users manage their condition through diet or therapy, the platform can be used to constantly track and monitor outcomes.
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Machine learning algorithms show promise in improving medical image analysis and diagnosis by helping physicians more accurately interpret images. Such algorithms can be trained using labeled medical image data to learn the differences between benign and malignant tumors, and then apply that learning to analyze new images and predict the likelihood of tumors being benign or malignant. However, it is important to address the potential pitfalls of machine learning and ensure its safe and effective use in medical applications.
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Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DTComputer-Aided Diagnosis using Class-Weighted Deep Neural Network
This paper proposes a deep learning method using a convolutional neural network to classify breast cancer tumors as benign or malignant using medical images. The network outperforms existing methods, achieving 99.52% and 99.68% accuracy on two datasets. A class-weighted loss function allows tuning the network's performance by adjusting weights to address data imbalances. The proposed method can reliably support cancer diagnosis.
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This document proposes and tests a knowledge-based machine learning system to automatically detect errors in radiation treatment plans. Key steps include characterizing treatment plan "fingerprints" using variations in multi-leaf collimator leaf positions and gantry angles. Three machine learning algorithms - support vector machine, k-nearest neighbors, and Bayesian - are developed and tested on real patient data, all achieving 100% success in distinguishing treatment plans for different cancer types. The system is intended to improve safety by prospectively detecting incorrect plans before treatment and alerting users.
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The document discusses brain tumor segmentation from MRI images. It describes how brain tumors are classified, outlines the segmentation process which includes preprocessing, segmentation, feature extraction and classification. Local binary patterns and support vector machines are used for feature extraction and classification. The accuracy, sensitivity and specificity are calculated to measure the performance of the segmentation system. Figures show examples of segmented images and comparisons of results from support vector machines and decision tree approaches.
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This document contains the slides from a lecture on radiomics for oncology given by Andre Dekker. The lecture covers the rationale for radiomics, which is to use quantitative features extracted from medical images to help predict outcomes like tumor behavior, survival, and response to treatment using machine learning. The major workflow steps of radiomics are discussed, from image acquisition and feature extraction to modeling and validation. Key challenges like robust segmentation and feature reproducibility are also addressed. New directions for radiomics research include applications in preclinical studies, other modalities like PET and MRI, and linking radiomic features to genomic data. Overall, radiomics holds promise to help personalized medicine but large amounts of standardized data are still needed for proper validation of models.
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Breast cancer is one of the leading cancers for women in developed countries including India. It is the
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Paper id 36201506
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
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Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...
This document presents a novel approach for brain tumor classification in MRI images using feature selection and extraction. It extracts intensity, texture, and shape-based features from MRI images and applies principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction. Support vector machines (SVM) are then used to classify tumors as white matter, gray matter, CSF, abnormal or normal tissue. The technique is tested on 140 brain MRI images and achieves high classification accuracy compared to previous methods.
Big data
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Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
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This document describes a study that uses machine learning and deep learning techniques to detect breast tumors using a publicly available dataset. Various classification models were developed including logistic regression, decision trees, random forests, support vector machines, gradient boosting, extreme gradient boosting, LightGBM, and a recurrent neural network. These models were trained on features from the dataset to classify breast tumor cases as benign or malignant. The models' performance was evaluated using various metrics like accuracy, precision, recall, and F1 score. Ensemble techniques like bagging and boosting were also explored to improve the models' performance at detecting breast cancer.
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The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
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The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
ICBO 2014, October 8, 2014
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Case Study: Advanced analytics in healthcare using unstructured data
This document summarizes a case study on using unstructured data like images to analyze diabetic retinopathy. Retinal images from half a million patients were analyzed using neural networks and computer vision to detect disease patterns. This automated analysis compressed 50 years of clinical experience into 24 hours to more accurately diagnose patients. The results complemented physician expertise. This case study demonstrates the potential of advanced analytics and large datasets to enhance medical diagnosis using unstructured data like images.
HEALTH PREDICTION ANALYSIS USING DATA MINING
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
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Comparative Study on Cancer Images using Watershed Transformation
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
The Evolution and Impact of Medical Science Journals in Advancing Healthcare
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
journals on medical
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
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with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Building RAG with self-deployed Milvus vector database and Snowpark Container...
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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Data Mining Techniques In Computer Aided Cancer Diagnosis
- 1. Data mining techniques in computer aided cancer diagnosis
- 2. Data mining in cancer diagnosis Bio medical data mining happens to be a long standing problem in scientific research where scientists are looking for innovative ideas methods to mine bio-medical data. Data mining in CAD(computer aided diagnosis) helps doctors to make optimal decisions quickly and accurately.
- 3. Data mining techniques allow the doctors to quickly categorize the difference between malignant and benign tumors. Key factor analysis is done to find the difference between benign and tumor cells. Decision trees may be used for clustering, classification, prediction or estimation. One of the useful applications in medical sciences is in management of leukemia as it accounts for about 33% of pediatric malignancies.
- 4. ALL Child acute lymphatic leukemia( also called acute lymphatic leukemia or ALL) is a cancer of blood and bone marrow. Its most common in children and gets worse if not treated in early stages.
- 5. Key data mining techniques used in ALL diagnosis are: Neural networks Decision trees Cluster detection Genetic algorithms
- 7. Combine information from multiple sources
- 8. Provide support for differential diagnosis
- 11. Detect cancer patterns in general and special populations
- 12. Formulate models that explains the patterns
- 14. New image mining techniques for breast cancer detection The method uses an overlapped technique to precisely detect the presence of breast cancer. Statistical features are extracted from mammograms and used for decision tree induction in order to learn the knowledge for computer assisted image analysis.
- 15. The application of data mining will help to get some additional knowledge about specific features of different classes and the in which they are expressed in the image (can help to find some inherent non-evident links between classes and their imaging in the picture) It can help to get some non-trivial conclusions and predictions can be made on the base of image analysis. The basis for this study is sufficiently a large database with images and expert descriptions.
- 17. A data viewer that provides a series of reports.
- 18. A map viewer that allows users to project compounds onto color maps for rapid visual analysis.
- 19. Links to external publications databases for related articles of interests.