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Smart Technologies, Communication and Robotics (STCR)

作者： G Prabaharan Vani Rajasekar Velliangiri Sarveshwaran Department of Computer Science and Engineering B V Raju Institute of Technology Narasapur Telangana India Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur TamilNadu India

According to a recent study, using retinal imaging to identify the tumour in the eye is difficult. When an image is analyzed, the affected region of the lesion impacted retinal picture is not discovered in the conventional system due to uneven illumination, low contrast, and blurring, which are symptoms of an eye disease. The proposed methodology was created to identify the eye tumour in the earlier stage with enhanced image quality and increased accuracy. The conventional system has a few drawbacks such as lower accuracy and more computational time to analyze the image. Convolutional neural networks (CNN) have recently demonstrated its result of their capacity to learn various category image denotions that aid in image classification in various sectors. These networks have a tremendous ability to extract the proper features from input images with training on millions of images, which produces accurate categorization. The deep learning-based transfer learning approach is suggested for earlier detection of eye tumours. The five pre-trained models such as AlexNet, ResNet, DensNet, VGG 19 and Inception model were used for efficient eye tumour classification. The performance analysis of the proposed approach has shown that higher accuracy of 98.63% and this model shows enhanced performance in better classification.

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On the complexity of SHAP-score-based explanations: tractability via knowledge compilation and non-approximability results

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 2717-2774页

作者： Marcelo Arenas Pablo Barceló Leopoldo Bertossi Mikaël Monet Department of Computer Science & Institute for Mathematical and Computational Engineering School of Engineering & Faculty of Mathematics Universidad Católica de Chile and IMFD Chile Institute for Mathematical and Computational Engineering School of Engineering & Faculty of Mathematics Universidad Católica de Chile and IMFD Chile and National Center for Artificial Intelligence CENIA Chile SKEMA Business School Montreal Canada Univ. Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL Lille France

Scores based on Shapley values are widely used for providing explanations to classification results over machine learning models. A prime example of this is the inuential SHAP-score, a version of the Shapley value that can help explain the result of a learned model on a specific entity by assigning a score to every feature. While in general computing Shapley values is a computationally intractable problem, we prove a strong positive result stating that the SHAP-score can be computed in polynomial time over deterministic and decomposable Boolean circuits under the so-called product distributions on entities. Such circuits are studied in the field of Knowledge Compilation and generalize a wide range of Boolean circuits and binary decision diagrams classes, including binary decision trees, Ordered Binary Decision Diagrams (OBDDs) and Free Binary Decision Diagrams (FBDDs). Our positive result extends even beyond binary classifiers, as it continues to hold if each feature is associated with a finite domain of possible *** also establish the computational limits of the notion of SHAP-score by observing that, under a mild condition, computing it over a class of Boolean models is always polynomially as hard as the model counting problem for that class. This implies that both determinism and decomposability are essential properties for the circuits that we consider, as removing one or the other renders the problem of computing the SHAP-score intractable (namely, #P-hard). It also implies that computing SHAP-scores is #P-hard even over the class of propositional formulas in DNF. Based on this negative result, we look for the existence of fully-polynomial randomized approximation schemes (FPRAS) for computing SHAP-scores over such class. In stark contrast to the model counting problem for DNF formulas, which admits an FPRAS, we prove that no such FPRAS exists (under widely believed complexity assumptions) for the computation of SHAP-scores. Surprisingly, this negative re

关键词： explainable AI shapley values SHAP score knowledge compilation FPRAS

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Machine Learning Driven Cardiovascular Disease Prediction Among Male Senior Adults

Machine Learning Driven Cardiovascular Disease Prediction Am...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Poongothai Elango Kasthuri A Mariammal G T.R. Saravanan Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department Of Computer Science Arulmigu Subramania Swamy Arts and Science College Thoothukudi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The heart is a critical organ at the heart of the circulatory system, acting as a powerful muscle pump that keeps life going by constantly pumping blood throughout the body. Heart illnesses, often known as cardiovascular diseases (CVDs), are a broad category of ailments that affect the heart and blood arteries. The severity of these illnesses ranges from minor to life-threatening. Heart disease prediction using machine learning is essential for improving patient care, reducing healthcare costs, and addressing a major public health challenge. It leverages advanced data analytics to enable early detection, personalized treatment, and more efficient allocation of resources, ultimately leading to better health outcomes and a positive impact on public health. Heart disease prediction involves the use of machine learning and data analysis techniques to assess an individual's risk of developing heart-related conditions. This process begins with collecting a wide range of medical data from kaggle for analysis, including demographics, medical history, and clinical measurements. In this analysis, Random forest is used as a feature estimate for the study of cardiovascular disease prediction for male senior adults in the age group of 37 to 70 years since we analyzed from the existing research that it performs better for heart disease. Recursive Feature Elimination with Cross-Validation (RFECV) is used to assess feature importance. It trains the model using logistic regression and achieves an accuracy of 82.4%. Predicting cardiovascular health among senior male adults is of utmost importance to proactively manage and mitigate the risks associated with these conditions.

关键词： Heart Data analysis Telemedicine Soft sensors Pumps Aging Cardiovascular diseases

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Employing a deep learning technique to categorize Internet of Things (IoT) traffic in a smart city context

Employing a deep learning technique to categorize Internet o...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Anu Priya, S. Rajesh kanna, B. Beaulah Jeyavathana, R. Bhat, Niyati Rajalakshmi, S. Srimathi, S. Chennai600117 India Department of Electronics and Communication Engineering Alagappa Chettiyar College of Engineering and Technology Karaikudi India Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India B.E Computer Engineering University of Mumbai Mumbai India Department of Electronics and Communication Engineering RMK College of Engineering and Technology India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350305708

The exponential increase in quantity of cars on the roads of smart cities has led to traffic jams, air contamination, and delays in the delivery of essential goods. Every day road accidents continue to be a leading cause of death and serious injury. The Internet of Things-based Traffic Management System is utilized to manage traffic flow, ensure safe data transfer, and spot accidents. Autonomous vehicles and smart devices have sensors built into an IoT-based ITM system to help with identification, data collection, and transmission. Connectivity options for the Internet of Things are being severely tested by the proliferation of IoT devices and applications. This can lead to severe difficulties in the allocation of the spectrum, the resources needed to analyze the IoT data, and the allowable delay for important spare situations throughout the end-to-end communication chain. Therefore, accurate categorization or forecasting of the IoT devices' time-varying traffic characteristics is essential. However, the difficulty of making this sort of categorization remains open. Current approaches tend to be based on machine learning methods, which have a high computational cost despite not taking into account the traffic's fine-grained flow features. In this research, we address this issue by developing a two-stage categorization system based on deep learning for IoT devices in a smart city that draws on both connectivity and statistical information. In order to excerpt the internet and statistical characteristics set for various IoT strategies, we must do data cleaning and preprocessing. Based on the results of the tests, it appears that the proposed categorization has a 99% chance of being correct. © 2023 IEEE.

关键词： Autonomous Vehicle Deep Learning IoT Traffic Data Smart-City Traffic Management

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NTFS: Introduction and Analysis from Forensics Point of View

NTFS: Introduction and Analysis from Forensics Point of View

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Advancement in Technology (ICONAT), International Conference for

作者： Rahul Hermon Upasna Singh Bhupendra Singh School of Computer Engineering and Mathematical Sciences Defense Institute of Advanced Technology Pune India Computer Science and Engineering Department Indian Institute of Information Technology Pune India

New Technology File System (NTFS) is a file system being used by Windows Operating Systems since 1993. NTFS is a file system used by the Windows Operating Systems for storing, cataloguing and discovering/finding files. In this work we will learn about the basics of NTFS to include its file structure and other important attributes. Then carry out analysis of NTFS file system using WinHex and Active@ Disk Editor tools from a forensics point of view, compare the results of both tools. Lastly, we carried out comparison of NTFS in Windows 10 and 11 Operating System to include forensics analysis using WinHex and Active@ Disk Editor tools.

关键词： File systems Operating systems Forensics

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Brain Tumor Detection Using ADARN Optimizer

Brain Tumor Detection Using ADARN Optimizer

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Disruptive Technologies (ICDT), International Conference on

作者： Rajiv Kumar Avinash Dwivedi Shivani Joshi Vikas Chandra Tripathi Department of Applied Computational Science and Engineering NVIDIA AI Learning & Research Center G L Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments.

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Machine Learning Based Early Prediction of Parkinson's Disease for Sustainable Healthcare 1st

Machine Learning Based Early Prediction of Parkinson's Disea...

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1st International Conference on Sustainable Development through Machine Learning, Artificial Intelligence, and IoT, ICSD 2023

作者： Chauhan, Ritu Mehta, Khushi Alankar, Bhavya Kaur, Harleen Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Amity Institute of Information Technology Amity University UP Mumbai India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031470547

The healthcare sector and technology are working hand-in-hand, which has led researchers to focus on Parkinson's disease. In a world where medical science and technology intertwined, there was a group of researchers determined to unravel the mysteries of Parkinson's disease. Medical science with machine learning algorithms, aimed to develop a robust prediction model for diagnosing this complex neurological disorder. The researchers explored various algorithms including Xgboost, Naive Bayes, Tree Classifier, KNN model, SVC model, Logistic Model, Random Forest, AdaBoost, and Gradient Boosting, using a diverse dataset encompassing clinical measurements, genetic information, and demographic factors. The researchers utilized various machine learning algorithms to uncover patterns and correlations, shed light on relationships, make accurate predictions, foster insights, differentiate Parkinson's disease, understand odds and probabilities, tackle uncertainty, and improve accuracy. By combining these algorithms, they constructed a prediction model for the accurate diagnosis of Parkinson's disease, bringing us closer to unraveling its mysteries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Adaptive boosting

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Batch-Parallel Compressed Sparse Row: A Locality-Optimized Dynamic-Graph Representation

Batch-Parallel Compressed Sparse Row: A Locality-Optimized D...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Brian Wheatman Randal Burns Helen Xu University of Chicago Chicago IL Department of Computer Science Johns Hopkins University Baltimore MD School of Computational Science and Engineering Georgia Institute of Technology Atlanta GA

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

The default data structure for storing sparse graphs is Compressed Sparse Row (CSR), which enables efficient algorithms but is not designed to accommodate changes to the graph. Since many real-world graphs are dynamic (i.e., they change over time), there has been significant work towards developing dynamic-graph data structures that can support fast algorithms as well as updates to the graph. This paper introduces Batch-Parallel Compressed Sparse Row (BP-CSR), a batch-parallel data structure optimized for storing and processing dynamic graphs based on the Packed Memory Array (PMA). At a high level, Batch-Parallel Compressed Sparse Row extends Packed Compressed Sparse Row (PCSR, HPEC '18), a serial dynamic-graph data structure built on a PMA. However, since the original PCSR runs only on one thread, it cannot take advantage of the parallelism available in multithreaded machines. In contrast, Batch-Parallel Compressed Sparse Row is built on the batch-parallel Packed Memory Array data structure (PPoPP '24) and can support fast parallel algorithms and updates. The empirical evaluation demonstrates that Batch-Parallel Compressed Sparse Row supports fast parallel updates with minimal cost to algorithm performance. Specifically, BatchParallel Compressed Sparse Row performs up to $\mathbf{4 2 0}$ million inserts per second. Across a suite of 10 graph algorithms and 10 input graphs, Batch-Parallel Compressed Sparse Row incurs $1.05 \times$ slowdown on average and about $1.5 \times$ slowdown at most compared to Compressed Sparse Row (CSR), a classical static graph representation. Furthermore, the empirical results show that Batch-Parallel Compressed Sparse Row outperforms existing tree-based and PMA-based dynamic-graph data structures on both algorithms and updates.

关键词： Costs Heuristic algorithms Instruction sets Throughput Arrays Parallel algorithms

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CONSENSUS BASED STOCHASTIC CONTROL

arXiv

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arXiv 2025年

作者： Lyu, Liyao Chen, Jingrun Department of Computational Mathematics Science & Engineering Michigan State University MI48824 United States School of Mathematical Sciences Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215127 China

We propose a gradient-free deep reinforcement learning algorithm to solve high-dimensional, finite-horizon stochastic control problems. Although the recently developed deep reinforcement learning framework has achieved great success in solving these problems, direct estimation of policy gradients from Monte Carlo sampling often suffers from high variance. To address this, we introduce the Momentum Consensus-Based Optimization (M-CBO) and Adaptive Momentum Consensus-Based Optimization (Adam-CBO) frameworks. These methods optimize policies using Monte Carlo estimates of the value function, rather than its gradients. Adjustable Gaussian noise supports efficient exploration, helping the algorithm converge to optimal policies in complex, nonconvex environments. Numerical results confirm the accuracy and scalability of our approach across various problem dimensions and show the potential for extension to mean-field control problems. Theoretically, we prove that M-CBO can converge to the optimal policy under some assumptions. © 2025, CC BY-NC-SA.

关键词： Deep reinforcement learning

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