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检索条件"主题词=CLASSIFICATION algorithms"

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： V. Sathiyapriya K. Nethra Devi S. Deepa M. Marimuthu P. Manikandan S. Prabu Department of Computer Science and Engineering Knowledge Institute of Technology Kakapalayam India Department of Artificial Intelligence and Data Science Gnanamani College of Technology Namakkal India School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches.

关键词： Image segmentation Accuracy Soft sensors Pancreatic cancer Medical services Feature extraction Prediction algorithms Whale optimization algorithms classification algorithms Ensemble learning

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Optimizing MR Image Registration for Accurate Brain Volume Measurement in Children with Autism Spectrum Disorder

Optimizing MR Image Registration for Accurate Brain Volume M...

引用

International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Shiva Sanati Mahdi Saadatmand Department of Computer Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Electrical Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental conditions, underscoring the need for early and accurate diagnosis. This study aims to identify regions of interest (ROIs) in the brain that significantly differ between children with ASD and typically developing subjects (TDS), enabling precise classification. We propose novel MR image registration algorithms using the ICBM152 digital atlas, applying both rigid and non-rigid transformations. The volumes of 116 gray matter regions were measured using the MNI-AAL atlas, aligned with ICBM152, and compared between the two groups. MR images from 26 typically developing and 26 autistic children, aged 5 to 10 years, were obtained from the ABIDE database. Our registration algorithms achieved a cross-correlation coefficient (CCC) of 0.82 for the ASD group, compared to 0.65 with SPM's Powell method. Non-rigid registration further improved the CCC to 0.88 and reduced convergence time by 2.8 times. Significant increases in gray matter volumes were observed in specific brain regions in the ASD group, consistent with the age-specific theory of autism. Finally, our SVM-based classification achieved 92.3% accuracy, with an F1-score of 90.1%, recall of 90.1%, and precision of 89.4%, demonstrating the method's effectiveness in distinguishing ASD from TDS based on brain features.

关键词： Neuroimaging Support vector machines Autism Image registration Accuracy Sensitivity Volume measurement Grey matter Thalamus classification algorithms

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Active Prewarning Technology for Smart Grid Based on Unsupervised Learning

Active Prewarning Technology for Smart Grid Based on Unsuper...

引用

Machine Learning and Intelligent Systems Engineering (MLISE), International Conference on

作者： Ruiting Qu Xiaolei Tian Shuo Chen State Grid Liaoning Information and Communication Company Shenyang China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

This paper proposes an early warning technology for power grid cloud platform microservice architecture alarm information based on unsupervised learning, discovers potential abnormal patterns and rules, sends out early warning signals in a timely manner, and improves the ability to identify and process power grid cloud platform microservice architecture alarms. According to the above steps, based on the establishment of the power grid business microservice operation alarm log alarm information knowledge base, this article will study the cloud platform operation monitoring data scoring algorithm, classify the cloud platform monitoring information, and then filter out the suspected abnormal data. For different business types of complex scenarios, research on abnormal information classification mining technology based on statistical models, proximity, clustering and classification, apply improved K-means clustering[1] and LOF outlier detection technology[2] to establish a combined detection model, and detect anomalies The data is clustered and outliers detected to achieve active early warning technology of unsupervised learning.

关键词： Knowledge based systems Microservice architectures Clustering algorithms Data models Smart grids classification algorithms Unsupervised learning

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Enhancing Facial Palsy Treatment through Artificial Intelligence: From Diagnosis to Recovery Monitoring

Enhancing Facial Palsy Treatment through Artificial Intellig...

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Conference on Human System Interactions, HSI

作者： Antoni Górecki Magdalena Mazur-Milecka Department of Biomedical Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

The objective of this study is to develop and assess a mobile application that leverages artificial intelligence (AI) to support the rehabilitation of individuals with facial nerve paralysis. The application features two primary functionalities: assessing the paralysis severity and facilitating the monitoring of rehabilitation exercises. The AI algorithm employed for this purpose was Google's ML Kit “face-detection”. The classification of facial nerve palsy was achieved by measuring the asymmetry of the user's face using a proprietary algorithm developed specifically for this study. This approach not only enables a precise assessment of paralysis severity but also allows for a personalized rehabilitation experience. Furthermore, the monitoring of rehabilitation exercise adherence and correctness is conducted through algorithms crafted for this application to ensure that patients are performing their prescribed rehabilitation exercises effectively. This comprehensive system offers a tailored and interactive approach to the management of facial nerve paralysis through the integration of AI algorithms and user-friendly mobile technology.

关键词： Accuracy Medical services Paralysis Mobile applications classification algorithms Internet Artificial intelligence

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Optimizing Communication Efficiency of GNN Inference in Distributed Systems

Optimizing Communication Efficiency of GNN Inference in Dist...

引用

International Conference on Solid-State and Integrated Circuit Technology

作者： Wenqian Zhou Qiaosha Zou The Institute of Brain-inspired Circuits and Systems Fudan University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

With the widespread adoption of Graph Neural Network (GNN) models, the demand for GNN inference acceleration has increased due to their slow runtime, especially when handling large-scale graphs. While distributed systems have shown promising results in computation acceleration, their potential for optimizing communication remains largely unexplored. In this paper, we propose novel feature pairing and feature compression methods for efficient and reduced data communication. Moreover, we co-design a multi-core system with Network-on-Chip (NoC) for evaluation. Experimental results show that our techniques can achieve up to 54.1% reduction in communication overhead, and 1.65x speedup in runtime.

关键词： Integrated circuit technology Runtime Computational modeling Redundancy Network-on-chip Graph neural networks Inference algorithms classification algorithms Data communication

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Smart and sustainable framework for Maize Leaf Disease Prediction using Deep Learning Techniques

Smart and sustainable framework for Maize Leaf Disease Predi...

引用

Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： J. Mokshagna Sai G. Indira Priyadarshini G. Pranay Goud D. Haswanth Venkat Sai Varma R. Pitchai D. Jyothirmai Department of CSE B V Raju Institute of Technology Narsapur Telangana State India Department of CSE B V Raju Institute of Technology Narsapur India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

Maize production plays a significant role in India's agricultural landscape and overall food security. The crop's nutritional composition, rich in carbohydrates, fiber, vitamins, and minerals, makes it a valuable addition to the Indian diet. Maize leaf diseases are a major threat to maize farming, reducing production and quality on a global scale. Maize rust, northern leaf blight, southern leaf blight, and maize stripe virus are some of the most notable diseases. These diseases cause substantial financial losses by reducing yields, increasing production costs due to disease management methods, and compromising grain quality. Numerous investigations used machine learning and deep learning algorithms for predicting maize leaf disease. Deep learning revolutionized maize leaf disease prediction by applying powerful algorithms to large datasets of leaf images and classifying diseases such as rust, blight, and mosaic virus. The proposed work explores the application of deep learning algorithms for predicting Maize leaf diseases based on leaf images and associated metadata. By leveraging a diverse dataset of millet leaf images annotated with disease labels, the proposed models aim to accurately identify and classify different types of leaf diseases, thereby facilitating timely intervention and disease management strategies.

关键词： Deep learning Training Machine learning algorithms Production Predictive models Prediction algorithms classification algorithms Convolutional neural networks Viruses (medical) Diseases

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Smart Solutions for Utilizing Advanced Machine Learning for Robust Credit Card Fraud Detection

Smart Solutions for Utilizing Advanced Machine Learning for ...

引用

Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Shanvi Chauhan Kanwarpartap Singh Gill Rahul Chauhan Hemant Singh Pokhariya Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

This research primarily aims to shed light on the serious issue of credit card fraud, which has become much worse with the advent of internet shopping and, more specifically, the present COVID-19 epidemic. Developing a machine learning system capable of distinguishing between legitimate and fraudulent credit card transactions is the primary objective of this project, which aims to decrease an annual loss of ∃24 billion. Using data such as transformed numerical characteristics after PCA analyses, transaction time and amount, and Logistic Regression, Decision Tree Classifier, and K-Nearest Neighbours approaches are employed in the research study. The accuracy rates of these algorithms are demonstrated by the cross-validation score, ROC AUC score, and F1 score within the context of fraud detection. Improving the models’ accuracy and resilience is as simple as using statistical tests like ANOVA when selecting features. To improve the detection of fraudulent behaviour and to accurately compare the results of various algorithms, balanced datasets are essential, as this shows. There was a 91% F1 score, 92.35% ROC AUC, and 98.01% crossvalidation accuracy rate for fraud detection in the logistic regression model. Alternatively, a decision tree classifier's fraud detection cross-validation score was 96.67%, ROC AUC was 91.36%, and F1 was 90%. When it came to detecting fraud, K-Nearest Neighbours performed exceptionally well with scores of 97.63% for ROC AUC, 99.34% for cross-validation, and 97% for F1.

关键词： Logistic regression Accuracy Machine learning algorithms Signal processing algorithms Machine learning Credit cards Fraud classification algorithms Decision trees Resilience

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An Improved Sand Cat Swarm Algorithm Optimizing Support Vector Machines

An Improved Sand Cat Swarm Algorithm Optimizing Support Vect...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Jiajun Wu Hongxian Wang National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Support vector machines (SVM) are commonly used to solve classification and regression problems, however a suitable kernel function needs to be selected to achieve an effective solution. To solve this problem, we propose an improved sand cat swarm optimization algorithm (SSCSO) to optimize the hyperparameters of SVM. First, Logistic- Icmic mapping is used to generate the initial population to improve the convergence speed. Then, the spiral search strategy is used to balance the global and local search capabilities. Finally, the alert mechanism in the Sparrow search algorithm is integrated to further improve the convergence speed. Simulation experiments of 12 benchmark test functions show that SSCSO has faster convergence speed and more accurate search capabilities than SCSO, SCSO1, SCSO2, SCSO3, and Sparrow Search Algorithm (SSA). In the experiment of modulated signal classification, the internal characteristics of the signal were characterized by extracting high-order cumulants and instantaneous characteristic parameters, and a multi-dimensional feature matrix was constructed for data analysis. The classification accuracy of the SSCSO-SVM algorithm is 4. 3%, 1. 6% and 2. 3% higher than that of the SVM, SCSO-SVM and SSA-SVM algorithms respectively, indicating that the SSCSO-SVM algorithm shows better performance in optimizing the SVM parameters.

关键词： Support vector machines Accuracy Signal processing algorithms Pattern classification Search problems Feature extraction Vectors classification algorithms Optimization Convergence

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ML Based Ultra Processed Food Categorization with Limited Data

ML Based Ultra Processed Food Categorization with Limited Da...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sushil Karpe Laukika Kulkarni Anushree Patil Pranjali Joshi Computer Engineering Department Pune Institute of Computer Technology Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Ultra processed foods have permeated modern diets, raising concerns about their health implications. This study introduces a pioneering approach utilizing ML algorithms notably Random Forest and XGBoost, to analyze packaged food products and categorize them based on their healthiness levels. Data sourced from users included extensive nutritional information of various packaged food items. Leveraging the collected data from our responses Random Forest, XGBoost and Naive Bayes Classifiers were implemented to classify the products into the categories defined by Nutri-Score FOP Labelling system which classifies the food products into 5 categories from A (being most preferable) TO E (being worst preferable) based on the score they achieve depending on their nutritional content. We are able to obtain valuable insights for consumers and regulatory bodies facilitating informed dietary choices and addressing the challenges faced with awareness of ultra-processed foods and their consumption.

关键词： Machine learning algorithms Automation Process control Forestry Food products classification algorithms Naive Bayes methods Labeling Random forests

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Video Smoke Detection Algorithm Based on a Spatial-Temporal Neural Network Model

Video Smoke Detection Algorithm Based on a Spatial-Temporal ...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zhen Cao Xi Zhang Ministry of Emergency Management Shenyang Fire Science and Technology Research Institute Shengyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

a kind of spatial-temporal neural network video smoke detection algorithm is proposed in order to solve the problems associated with the incorrect classification of the static approximate smoke background in the face of the detection of smoke in video detection networks, and the problem of false alarms and of the original test model algorithms being different in different detection environments. Based on the original YOLO v4 neural network algorithm, this paper introduces a k-means + + algorithm and genetic algorithm, while using the algorithm's clustering function to classify the sample points of the real boxes of the image data set, which make it a more suitable anchor. At the same time, the genetic algorithm is used to adjust its anchor in order to allow the generated anchor to adapt to the needs related to smoke detection. In the original neural network model, the dual-stream network model algorithm is used to extract information from the first step of the YOLO algorithm in order to further filter the smoke's characteristics as well as filter out error information, all to improve the detection capabilities of the overall neural network for video smoke fog images. Compared with traditional YOLOv4 networks, the algorithm obtained by the model algorithm has been improved by 8.51°/0. In actual tests, the alarm time requirements of the smoke alarm test program for early fire monitoring and the alarm systems for visual images were improved, and the detection accuracy of the network was also improved based on the assurance of the detection speed, while the performance of the model algorithm was also improved for different scenes.

关键词： YOLO Image recognition Neural networks Clustering algorithms Lighting Filtering algorithms Approximation algorithms Information filters classification algorithms Genetic algorithms

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