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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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Predicting Uterine Cancer Outcomes using Deep learning on Genomic Diagnosis from Histopathology Images 3

Predicting Uterine Cancer Outcomes using Deep Learning on Ge...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kavitha, A. Chandran, Anto Sam B. Divislay, R. Department of Information Technology St. Joseph’s College of Engineering Chennai India

ISBN: (纸本)9798331532420

Uterine cancer is still a significant health problem among females around the world. Early diagnosis and correct disease prognosis are essential to applying effective treatment., A new approach is proposed for outcome prediction in uterine cancer based on deep learning methods in the analysis of genomic data and machine learning models of histopathology image analysis. The methodology relates genomic data to histopathology images, building up a better diagnostic tool. The introduced deep learning framework for analyzing genomic profiles, especially biomarkers that present a prognosis for uterine cancer. The developed machine learning-based models to classify histopathology images obtained from uterine tissues, thus increasing the accuracy of diagnosis. combine these two data sources using a new fusion technique;this further raises the bar in the performance of this prediction by making a more accurate representation of changes occurring in cancer. Preliminary results indicate a significant increase in the accuracy of classification and outcome prediction, outperforming the existing methods. This integrated approach will provide a sound framework for early detection and personalized treatment strategies for uterine cancer by responding to the current limitations of diagnostic methodologies and offering new insights into the disease management process. algorithm integrates genomics and histopathological data to provide positive outcomes with the prediction of uterine cancer. The data collected is preprocessed, and then applied to two separate models which serve as critical feature extractors in the form of a CNN for image data and Transformer for genomic data, whose outputs are fused with an attention mechanism to bring out informative features that augment better predictive accuracy. It undergrows robust validation incorporating performance metrics, and will be released for clinical use in real-life applications with continuous monitoring to ensure adaptability as we

关键词： Lung cancer

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A Multi-Platform Compatible Remote Control based on Gesture Recognition Algorithm 3

A Multi-Platform Compatible Remote Control based on Gesture ...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Chen, Yan Wang, Xingchen Yang, Jingyuan Wu, Zhipeng Chen, Yangyang Peng, Yunyue College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

ISBN: (纸本)9798350380989

With the rapid advancement of information technology, one pressing concern lies in how to make electronic devices more user-friendly across diverse scenarios. This article proposes a novel gesture-based remote control system built on the Android platform, utilizing deep learning, the SSD algorithm, HOG features, and an SVM classifier. The system aims to foster the advancement of smart classrooms, medical command systems, smart homes, and other fields, while also enhancing accessibility for special groups by improving their interaction with mobile phones and other electronic devices. Through accurate gesture recognition and rapid response, it will provide users with a more convenient and efficient operating experience, thus promoting the wide application of artificial intelligence technology across various domains. © 2024 IEEE.

关键词： Android platform Deep learning Gesture Recognition

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An Improved Model for Gene Expression data Analysis Using Combined Feature Reduction and ELM Approaches 3

An Improved Model for Gene Expression Data Analysis Using Co...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tripathy, Jogeswar Dash, Rasmita Pattanayak, Binod Kumar Mohapatra, Debaniranjan Tripathy, Nrusingha Nayak, Subrat Kumar Iter Deemed to Be University Department of Cse Odisha Siksha 'o' Anusandhan Bhubaneswar India Iter Deemed to Be University Department of Ece Odisha Siksha 'o' Anusandhan Bhubaneswar India

ISBN: (纸本)9798350354379

On a worldwide scale, the second leading cause of death is cancer. To combat cancer, a variety of approaches have been proposed. It is strongly predicted that gene expression data would be used to help in the development of better cancer detection. On the other hand, gene expression data is dimensionally high, has a small sample size in comparison to the data dimension, and many genes are redundant and also noisy by nature. As a result, rather than using all genes to classify gene expression data, it would be wiser to use a subset of genes. Thus, in this research work, the proposed model is designed considering ranked-based filter sequence (FS) approaches for significant feature extraction and Extreme learning machine (ELM) for data classification. This analysis is carried out over four gene expression data. As FS approaches are highly successful for high dimension data, significant features are extracted using 4-stage feature reduction strategy applying RF approach. Thereafter the reduced data is applied for classification. ELM is a learning algorithm for a Single-Layer Feed-Forward Network (SLFN) that converges much faster than traditional methods and produces promising results. Thus, due to its superior approximation capability and generalization, ELM has equipped the interest of numerous researchers in recent years. In this analysis, considering different types of activation functions and considering a collection of network architecture, four numbers of ELM models are designed and used for classification purposes with four different activation functions. From all the experimental results it is found that IIELM (CM4) outperforms over all the model. © 2024 IEEE.

关键词： Adversarial machine learning

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Feature Selection-Based machine learning Approaches for Detecting Android Malware with Explainable AI 3

Feature Selection-Based Machine Learning Approaches for Dete...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Habib, Mahbuba Kibria, Hafsa Binte Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

With the involvement of technology, its negative sides are showing out drastically. As Android is an open-source platform that has nice customization, control and accessibility features cost-effectively, it attracts middle-class device users. As of 2023, Android has a 70.29% global market share. So, its insecurity has a tremendous effect on the mobile industry. This study represents how to efficiently detect Android malware in machine learning with various feature selection techniques like Correlation, Mutual Information, Chi-Square test and Recursive Feature Elimination to serve the optimization of the huge amount of information collected from different apps. Besides, these criteria are tested with different under-sampling and oversampling techniques. This research evaluates some more widely used algorithms: Decision Tree, Random Forest, and XG Boost. Among them, Random Forest surpasses all other algorithms. The output is evaluated with another dataset. Also, Explainable AI is used for the transparency of the models. This implies that Smote NC-based Mutual Information gives the highest outcome(98.5%) with an F1 score of 98% using a 10-fold cross-validation (CV) in Android malware detection. © 2024 IEEE.

关键词： Android malware

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Enhanced data Mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation

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Fault Analysis in Power Transmission Systems Using machine learning 3

Fault Analysis in Power Transmission Systems Using Machine L...

引用

3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Garg, Manik Raj, Pranav Piyush Prakash, Prem Delhi Technological University Electrical Engineering Delhi India

ISBN: (纸本)9798350387193

With the increasing complexity and decentralization of power generation and consumption, the demand for robust fault detection and classification systems in power distri-bution networks has become paramount. This project addresses this challenge by employing machine learning techniques for the detection and classification of faults in the power distribution network. For fault detection, three models, namely Decision Tree, Sup-port Vector machine (SVM), and Random Forest, are utilized to analyze electronic metrics and data. These models prove effective in identifying and localizing faults within the network, providing a foundation for proactive maintenance. In the classification phase, Decision Tree and SVM models are employed to further categorize detected faults. The integration of these models enhances the system's ability to not only detect faults but also classify them. The results indicate that the Random Forest model demonstrates exceptionally high accuracy in fault detection, outperforming other models. In the classification phase, the Decision Tree model exhibits superior performance, providing precise categorization of detected faults. This research delves into innovative methods for predicting network faults, with a particular focus on overhead lines. The proposed approach, backed by the success of the Random Forest and Decision Tree models, demonstrates high accuracy rates using various classifiers, contributing to the development of a comprehensive fault management system. © 2024 IEEE.

关键词： Power distribution faults

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Framework for Optimising Supply Chain Analysis Using machine learning 3rd

Framework for Optimising Supply Chain Analysis Using Machine...

引用

3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Adrika, Vishi Kumar, Sumit Department of Computer Science The NorthCap University Gurgaon India

ISBN: (纸本)9789819725076

An algorithmic framework is presented to predict sales, late deliveries and fraudulent transactions in a supply chain using machine learning (ML) algorithms. The framework utilizes past data to recognize regularities, inclinations, and irregularities within the supply chain, enabling precise forecasting of future occurrences. Several algorithms were used out of which random forest and logistic regression were best performing. Supply chain managers can gain insights from this framework to empower their business and mitigate risks. Supply chain can be optimised and revolutionised by adapting the proposed framework to various supply chain scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Anomalous Behavior Detection in ATM using Different Methodologies: A Review 3

Anomalous Behavior Detection in ATM using Different Methodol...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Thakre, Harsh Bhushanwar, Kush Patel, Anil Computer Engineering Department PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331532420

The necessity for standardized anomaly detection systems in financial transactions is driven by the alarming increase in ATM fraud and criminal activities. Many techniques for detecting anomalous behavior in ATM systems are also summarized and evaluated in this review article. To better secure financial institutions and customers from fraudsters, it is crucial to examine the reasoning behind these systems, particularly in the areas that pertain to improving security. The overarching purpose of this paper is to present a clear and succinct explanation of the cutting-edge machine learning (ML) and deep learning (DL) algorithms that are presently the backbone of ATM anomaly pattern detection, including more contemporary methods like transfer learning. This review delves into the theoretical foundations of these strategies and compares different algorithms, along with the values of performance indicators that show their strengths and drawbacks. Our goal is to show, via an examination of current methods in actual settings, any flaws or problems that may hinder the development of ATM security in the future. Finally, this work can be used by researchers and those interested in bettering ATM systems and associated secure technologies in the future to study the subject and explore anomaly detection technologies as a means of both practical experience and future advancement. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of A Multimodal Deep Feature Fusion with Ensemble learning Architecture for Real-Time Gas Leak Detection 3

Development of A Multimodal Deep Feature Fusion with Ensembl...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Zhang, Eddie Zhang, Evan Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

Gas leak detection is extremely critical, but poses many significant challenges due to the high cost and large amount of human labor and expertise necessary to complete an inspection. Our previously reported soft robot SPIRo system is designed to address these challenges. This paper proposes a multimodal deep feature fusion system with the deep forest ensemble learning model, which fuses data from gas sensors and thermal images to improve SPIRo's accuracy and redundancy. The model consists of the convolutional layers of three CNNs: the AlexNet, ResNet-50, and MobileNet, and the extracted deep features from them along with the measured gas sensor data are fused into a Deep Forest Classification (DFC) module to make the final decision on whether a gas leak is present or not. The combination of a single CNN and gas sensor data fused with a DFC is also evaluated. Two sets of data have been tested with this system. The results indicate that for the training database, the ensemble model with three CNNs achieved a detection accuracy of 98.5%, compared to 95.0% for the ensemble model with single CNN and 93.1% achieved by each individual model. For the testing database collected by SPIRo, the ensemble model with three CNNs achieved an accuracy of 88.1%, compared to 82.4% for the single CNN models with ensemble and around 73.6% for the individual models. The results from experimental data collected by SPIRo furthers the architecture's effectiveness. Not only is the proposed method more accurate, but it is also more reliable due to increased redundancy in analyzing both thermal images and gas sensor data. In addition, it is also memory efficient, making it suitable for real-time detection applications.

关键词： Multimodal Fusion Deep Feature Fusion Ensemble learning Deep Forest Thermal Imaging Gas Leak Detection

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A machine learning-Based Predictive Model for the Management of Incidents in Small and Medium-Sized Enterprises in Peru 24

A Machine Learning-Based Predictive Model for the Management...

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3rd Asia conference on Algorithms, Computing and machine learning, CACML 2024

作者： Cribillero, Luis F. Quispe, Jeyson I. Castañeda, Pedro Faculty of Information Systems Engineering Universidad Peruana de Ciencias Aplicadas Peru

ISBN: (纸本)9798400716416

In the context of IT incident management, the prioritization and automation of tickets can be a challenge for companies that lack advanced technologies. However, these difficulties can be overcome today by applying machine learning algorithms and techniques that use historical data to train predictive models, which allows for more efficient and effective IT incident management. The article proposes the implementation of a predictive model that uses machine learning to prioritize IT incidents in these companies. The goal of this proposal is to allow small and medium-sized enterprises to prioritize their incidents automatically, using a model that has been previously trained with a supervised multi-label classification algorithm technique to achieve high accuracy. Experimental results show that the Mean Absolute Error (MAE) is 2.79 and a Mean Squared Error (MSE) of 8.21, using the metrics provided by the scikit-learn library. Additionally, the entropy loss approaches a value of 0, suggesting a precise ability of the model to predict real values. Additionally, an average accuracy level of 93.74% was achieved. © 2024 ACM.

关键词： Adversarial machine learning

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