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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine

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Enhancing the Prediction Accuracy of Brain Tumor Detection b...

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： M Revathi P Kalaiarasi S Jeevitha G Akilandasowmya Artificial Intelligence & Data Science St. Joseph's Institute of Technology Chennai India Department of Machine Learning Kalasalingam University Krishnankoil India Information Technology Jerusalem College of Engineering Chennai India Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy.

关键词： Support vector machines Image segmentation Accuracy machine learning algorithms Brain tumors Biological systems Intelligent systems Diseases

Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation

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arXiv 2023年

作者： Bender, Sidney Anders, Christopher J. Chormai, Pattarawat Marxfeld, Heike Herrmann, Jan Montavon, Grégoire Machine Learning Group Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Max Planck School of Cognition Leipzig Germany BASF SE Ludwigshafen am Rhein Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

This paper introduces a novel technique called counterfactual knowledge distillation (CFKD) to detect and remove reliance on confounders in deep learning models with the help of human expert feedback. Confounders are spurious features that models tend to rely on, which can result in unexpected errors in regulated or safety-critical domains. The paper highlights the benefit of CFKD in such domains and shows some advantages of counterfactual explanations over other types of explanations. We propose an experiment scheme to quantitatively evaluate the success of CFKD and different teachers that can give feedback to the model. We also introduce a new metric that is better correlated with true test performance than validation accuracy. The paper demonstrates the effectiveness of CFKD on synthetically augmented datasets and on real-world histopathological datasets. © 2023, CC0.

关键词： Distillation

Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation

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Towards Fixing Clever-Hans Predictors with Counterfactual Kn...

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Sidney Bender Christopher J. Anders Pattarawat Chormai Heike Marxfeld Jan Herrmann Grégoire Montavon Machine Learning Group Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Max Planck School of Cognition Leipzig Germany BASF SE Ludwigshafen am Rhein Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

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Sharp empirical Bernstein bounds for the variance of bounded random variables

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arXiv 2025年

作者： Martinez-Taboada, Diego Ramdas, Aaditya Department of Statistics & Data Science United States Machine Learning Department Carnegie Mellon University United States

Much recent effort has focused on deriving "empirical Bernstein" confidence sets for the mean µ of bounded random variables, that adapts to unknown variance V(X). In this paper, we provide fully empirical upper and lower confidence sets for the variance V(X), that adapt to both unknown µ and V[(X − µ)2]. Our bounds hold under constant conditional variance and mean, making them suitable for sequential decision making contexts. The results are instantiated for both the batch setting (where the sample size is fixed) and the sequential setting (where the sample size is a stopping time). We show that the first order width of the confidence intervals exactly matches that of the oracle Bernstein inequality;thus, our empirical Bernstein bounds are "sharp". We compare our bounds to a widely used concentration inequality based on self-bounding random variables, showing both the theoretical gains and improved empirical performance of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Random variables

Simultaneous inference for generalized linear models with unmeasured confounders

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arXiv 2023年

作者： Du, Jin-Hong Wasserman, Larry Roeder, Kathryn Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It begins by disentangling marginal and uncorrelated confounding effects to recover the latent coefficients. Subsequently, latent factors and primary effects are jointly estimated through lasso-type optimization. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish the identification conditions of various effects and non-asymptotic error bounds. We show effective Type-I error control of asymptotic z-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model. Copyright © 2023, The Authors. All rights reserved.

关键词： Logistic regression

A Review on the Role of Artificial Intelligence in Personalized learning

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A Review on the Role of Artificial Intelligence in Personali...

Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Rukaiyya Qureshi Shital Hajare Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

This article explores the revolutionary role of Artificial Intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles

关键词： Technological innovation Privacy Ethics Reviews Education Psychology Focusing

An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

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An Investigation of Smart Detection for Small Lung Tumor wit...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

NC-ALG: Graph-Based Active learning Under Noisy Crowd

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

UNCERTAINTY QUANTIFICATION FOR LEARNED ISTA

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arXiv 2023年

作者： Hoppe, Frederik Verdun, Claudio Mayrink Laus, Hannah Krahmer, Felix Rauhut, Holger Mathematics of Information Processing RWTH Aachen University Aachen Germany Department of Mathematics Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany

Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning