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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions 40

Vertical Validation: Evaluating Implicit Generative Models f...

引用

40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Elkady, Mai Bui, Thu Ribeiro, Bruno Inouye, David I. Computer Science Dept. Purdue University West LafayetteIN United States Electrical and Computer Engineering Dept. Purdue University West LafayetteIN United States

There has been a growing excitement that implicit graph generative models could be used to design or discover new molecules for medicine or material design. Because these molecules have not been discovered, they naturally lie in unexplored or scarcely supported regions of the distribution of known molecules. However, prior evaluation methods for implicit graph generative models have focused on validating statistics computed from the thick support (e.g., mean and variance of a graph property). Therefore, there is a mismatch between the goal of generating novel graphs and the evaluation methods. To address this evaluation gap, we design a novel evaluation method called Vertical Validation (VV) that systematically creates thin support regions during the train-test splitting procedure and then reweights generated samples so that they can be compared to the held-out test data. This procedure can be seen as a generalization of the standard train-test procedure except that the splits are dependent on sample features. We demonstrate that our method can be used to perform model selection if performance on thin support regions is the desired goal. As a side benefit, we also show that our approach can better detect overfitting as exemplified by memorization. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Generative adversarial networks

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ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images 2

ResNet-50: The Deep Networks for Automated Breast Cancer Cla...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Tejaswini Nayak, Debasish Swapnesh Kumar Kar, Anindita Jena, Lambodar Swarnkar, Tripti University Dept. of Computer Application FET-ITER Bhubaneswar India University FET-ITER Dept. of Computer Science and Engineering Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798350370188

One in every four cancer cases in women is breast cancer, which is the most prevalent malignancy in this group globally. In 2020, breast cancer will account for one in every eight new instances of cancer, according to projections of 2.3 million new cases. Breast cancer identification, both early and accurate, is critical to improve patient outcomes and survival rates. Magnetic resonance imaging (MRI) has developed as a valuable method for diagnosing breast cancer. Deep learning algorithms, notably convolutional neural networks (CNNs), have demonstrated exceptional effectiveness in a variety of medical image processing applications, including breast cancer classification. The ResNet-50 architecture has received a lot of interest in this context because of its excellent performance in image recognition tasks. ResNet-50 is a deep residual network that introduces skip connections and residual learning to solve the difficulty of training very deep neural networks. A total number of 1480 cancer MRI samples are collected from the Kaggle database, having two classes, which include healthy and malignant scans. It is observed that our proposed model with the data pre-processing techniques has achieved a classification accuracy of 92.01%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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NutriSpy: Investigate Your Food's Health Quotient 3

NutriSpy: Investigate Your Food's Health Quotient

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Thanay Kumar, Y.R. Joshi, Sudhanva S Sujan, A.T. Ujwal Karanth, K.V. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

NutriSpy is a comprehensive web and mobile application designed to enhance users' health and wellbeing through personalized food and exercise recommendations. At its core, NutriSpy integrates three powerful features: a tailored workout recommendation system, an advanced food identification tool, and a robust diet advisory service. By leveraging state-of-the-art machine learning algorithms, NutriSpy accurately identifies food items, assesses their nutritional and caloric content, and generates customized dietary guidance. The food identification system employs image recognition and natural language processing to provide precise nutritional information from photographs or descriptions of meals. This is combined with a diet advice system that considers individual health profiles, dietary restrictions, and fitness goals to offer personalized nutrition plans. Additionally, NutriSpy's workout recommendation system aligns exercise routines with users' dietary needs and fitness aspirations, creating a harmonious balance between food intake and physical activity. NutriSpy's sophisticated technology facilitates real-time adjustments and recommendations, making it a dynamic tool for promoting healthier lifestyles. By analyzing user data and integrating current health trends, NutriSpy not only aids in immediate dietary and fitness decisions but also contributes to long-term health improvements. This study explores the development and functionality of NutriSpy, detailing its components, underlying technologies, and potential impact on fostering healthier living habits. © 2024 IEEE.

关键词： Diet Recommendation system Exercise Recommendation System Food Recognition System

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SafeSync: A Comprehensive Safety and Monitoring System for Two-Wheeler Users Using Machine Learning, ESP32, GPS, and GSM 6

SafeSync: A Comprehensive Safety and Monitoring System for T...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sunil, K.S. Adithyan, P. Krishnan, Anjana Hashim Sharaf, C.P. Paul, Jerin John, Salvi Hanna Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350381689

Our work, 'SafeSync' proposes a complete safety and monitoring system aimed to address the considerable hazards encountered by two-wheeler users, such as accidents and rash driving scenarios. Unlike other systems, SafeSync combines powerful technology such as ESP32, GPS, and GSM with an innovative application of machine learning methods. This system specializes in detecting and alerting to accidents, tracking vehicles in real time, and identifying risky driving behaviour. SafeSync goes beyond traditional safety measures by not only identifying ongoing accidents but also forecasting potential events. This predictive feature enables the system to proactively inform users or authorities in advance, assisting in the prevention of accidents. The Flutter-built dedicated mobile app serves as a user-friendly interface, offering real-time updates on the vehicle's status and location. The program also allows users to customise monitoring settings and add parental restrictions. By actively recognizing and warning users to dangerous practises, SafeSync strives to improve rider safety and promote responsible driving behaviour. Finally, the initiative helps overall road safety by providing a solid solution for two-wheeler users to monitor and enhance their road safety. © 2024 IEEE.

关键词： Accident Alert ESP32 GPRS GPS GSM MPU6050 Rash Driving

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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Yiu, Cheuk Tung Shadow Woo, Kam Tim The Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350350821

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes. © 2024 IEEE.

关键词： Robotic assembly

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Tracking Dynamic Conditional Neural Correlation during Task Learning 46

Tracking Dynamic Conditional Neural Correlation during Task ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Zixu Chen, Shuhang Li, Mingdong Wang, Yiwen The Hong Kong University of Science and Technology Dept. Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350371499

Single neuron modulates the external stimuli, and neural population coordinates to encode information. An alternate method for examining the coordinated populational activity in neural encoding is conditional neural correlation (CNC). However, such correlations are not static during a new task learning process as neurons adapt their tunings over time for better performance. To investigate how neurons adjust their firing patterns during learning, it's essential to track the time-variant correlation. In this paper, we propose to mathematically model the dynamical CNC by implementing the integrated point process filter which incorporates neural correlation and single neural tuning into decoding. Specifically, we generate synthetic M1 neurons' firing data to simulate the dynamic change of the conditional neural correlation over days, while a rat learns a two-lever discrimination task. By comparing the tracked CNC with the designed CNC, our results show that the CNC can be better tracked over time by CIPPF than that of decoder assuming conditional independence among neurons, which indicates the possibility to better understand the brain dynamics during task learning. © 2024 IEEE.

关键词： Decoding

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Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets 41

Beyond the ROC Curve: Classification Trees Using Cost-Optima...

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41st International Conference on Machine Learning, ICML 2024

作者： Gabidolla, Magzhan Zharmagambetov, Arman Carreira-Perpiñán, Miguel Á. Dept. of Computer Science and Engineering University of California Merced United States United States

Important applications such as fraud or spam detection or churn prediction involve binary classification problems where the datasets are imbalanced and the cost of false positives greatly differs from the cost of false negatives. We focus on classification trees, in particular oblique trees, which subsume both the traditional axis-aligned trees and logistic regression, but are more accurate than both while providing interpretable models. Rather than using ROC curves, we advocate a loss based on minimizing the false negatives subject to a maximum false positive rate, which we prove to be equivalent to minimizing a weighted 0/1 loss. This yields a curve of classifiers that provably dominates the ROC curve, but is hard to optimize due to the 0/1 loss. We give the first algorithm that can iteratively update the tree parameters globally so that the weighted 0/1 loss decreases monotonically. Experiments on various datasets with class imbalance or class costs show this indeed dominates ROC-based classifiers and significantly improves over previous approaches to learn trees based on weighted purity criteria or over- or undersampling. Copyright 2024 by the author(s)

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Accessing Visual Information for Divyangjan 4

Accessing Visual Information for Divyangjan

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Rani, M. S. Divya Ghodke, Hrishikesh Vivekanand Manikandan, M. Manaswini, R. Shivani, K. Dhara, Sri Sai Venkata Gayathri Shreya Presidency University Dept. of Electronics and Communication Engineering Bengaluru560064 India Presidency University Dept. of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331518981

Approximately 1.3 billion people worldwide suffer from a visual impairment. Typically, they must use Braille to read printed materials. However, when the content is not printed in Braille, these individuals have difficulties. Even though there are a lot of electronic reading aids available, the costs remain prohibitive. Therefore, our work suggests a low-cost document reader that uses a webcam to take pictures of printed materials. The Optical Character Recognition (OCR) framework's image-to-text conversion is then used to turn the acquired image into text. Finally, the Text to Speech (TTS) framework's text-to-speech conversion will be used to read the text aloud in speech format. Consequently, a person with a visual impairment can use hearing rather than touch to comprehend printed materials that are not written in Braille. Users may view documents printed in five different languages. The choice of different document sizes is this work's greatest novelty. The system is equipped with a Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, a web camera and speaker. The Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, webcam, and speaker are all part of the system. © 2024 IEEE.

关键词： Stepping motors

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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