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AIML for Road Accident Analysis 4

AIML for Road Accident Analysis

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Rathore, Saurabh Pratap Singh Sanitha, P.C. Varshney, Manoj Verma, Anupama Vashisht, Manisha Saharan, Aaryan Icapsr Management & Research New Delhi India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Business Management Ranchi India Lingayas Vidyapeeth University Dept. of Cse Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Road traffic accidents pose a significant threat to both developed and developing nations, prompting global efforts to enhance road safety. With cars and drivers on the roads, pedestrians also face risks, often becoming innocent victims of accidents. Various factors contribute to road accidents, including driving under the influence, weather conditions, speed, road type, light conditions, road surface, and design. These accidents can cause severe, sometimes unseen injuries with lasting health implications. This study aims to explore road accidents in India using the random forest classifier algorithm and machine learning techniques. © 2024 IEEE.

关键词： Highway accidents

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Unified mechanism-specific amplification by subsampling and group privacy amplification 24

Unified mechanism-specific amplification by subsampling and ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jan Schuchardt Mihail Stoian Arthur Kosmala Stephan Günnemann Dept. of Computer Science & Munich Data Science Institute Technical University of Munich Dept. of Engineering University of Technology Nuremberg

ISBN: (纸本)9798331314385

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results.

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Image-Based Distance Estimation of a Target from a Camera using Color Checker Chart 2

Image-Based Distance Estimation of a Target from a Camera us...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Kamath, Vedavyasa Kurian, Ciji Pearl Shailesh, K.R. Suprabha Padiyar, U. Sontakke, Mangalam Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Electrical and Electronics Engg. Karnataka Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Karnataka Manipal India

ISBN: (纸本)9798350306415

In recent years, photography-based photometry has gained popularity and is being implemented widely. Under this approach, a camera is used as a sensor to estimate photometric quantities like illuminance or luminance. The absolute distance between the target surface and the camera can be crucial for photometric analysis. The technique proposed in this article employs a fixed focal length lens mounted on a DSLR camera. The standardised classic ColorChecker chart is placed over the target surface, and the scene image with the chart is captured. The intuition is that the average pixel area of the swatches present on the chart decreases as the camera is moved away from the chart. OpenCV libraries are used to calculate the average pixel area of the chart color swatches. Distance is measured using an industry-standard laser-based distance measurement tool. Curve fitting is used to establish a mathematical relationship between camera-chart distance and the average pixel area of the swatches. Distance varies from 650 cm to 2 m during the data collection process. The influence of the tilt of the chart plane with respect to the camera sensor plane is also studied. During testing, the proposed power trendline-based mathematical model performed with an error percentage of less than 1. This approach for distance measurement enables finer tuning of the machine learning-based model and, as a result, more accurate illuminance estimation for the work plane. © 2024 IEEE.

关键词： computer vision Curve fitting Distance measurement Image analysis Image edge detection Photometry

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Emergency vs Non-Emergency Vehicle Classification: Enhancing...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madan, S. Shetty, Kishan Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence and Data Science Karnataka Bengaluru India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300826

Due to its dense population, India frequently experiences traffic congestion, which puts lives in danger by trapping vehicles like ambulances, police cars, and fire trucks which run on road for emergency purpose. It becomes crucial to give these vehicles priority and allow for their easy passage. However, it becomes challenging or even impossible for traffic police to effectively handle such circumstances. Therefore, there is a requirement for an automated system that can locate emergency vehicles in high-traffic areas, alert the controller, or drive itself to direct other vehicles to make room. This study suggests an automated method for identifying emergency vehicles from CCTV footage that makes use of deep convolutional neural networks (CNN). The objective is to effectively identify and classify emergency vehicles in real-time, leveraging the power of advanced object detection techniques. With an accuracy of 91.73% and a loss of 0.2120, the suggested technique outperformed existing optimizers in accurately recognizing and classifying emergency vehicles. © 2023 IEEE.

关键词： Object detection

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WelQrate: defining the gold standard in small molecule drug discovery benchmarking 24

WelQrate: defining the gold standard in small molecule drug ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yunchao (Lance) Liu Ha Dong Xin Wang Rocco Moretti Yu Wang Zhaoqian Su Jiawei Gu Bobby Bodenheimer Charles David Weaver Jens Meiler Tyler Derr Computer Science Dept. Vanderbilt University (VU) Neural Science Dept. Amherst College Chemistry Dept. VU Computer Science Dept. University of Oregon Data Science Institute VU MD Anderson Cancer Center Computer Science Dept. Vanderbilt University (VU) and Electrical and Computer Engineering Dept. VU and Psychology Dept. VU Chemistry Dept. VU and Institute of Chemical Biology VU Chemistry Dept. VU and Center for Structural Biology VU and Pharmacology Dept. VU and Institute for Drug Discovery Leipzig University (LU) and Computer Science Dept. LU and Chemistry Dept. LU. Computer Science Dept. Vanderbilt University (VU) and Data Science Institute VU

ISBN: (纸本)9798331314385

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate dataset collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets; WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening; Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at ***.

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Enhancing Printed Circuit Board Defect Detection through Ensemble Learning 1

Enhancing Printed Circuit Board Defect Detection through Ens...

引用

1st IEEE International Workshop on Future Intelligent Technologies for Young Researchers, FITYR 2024

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Gao, Jerry Liu, Jun San José State University Department of Applied Data Science San José United States Virginia Tech Dept. of ISE Blacksburg United States San José State University Dept. of Computer Engineering San José United States

ISBN: (纸本)9798331539757

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-The-Art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024 IEEE.

关键词： Contrastive Learning

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An Identification of Guava Fruit Disease Using ML 3

An Identification of Guava Fruit Disease Using ML

引用

3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Nandi, Subarna Hasan, Nazmul Rashid, Omor Faruk Al Miraz, Shafiun Reza, Md. Shafi Rahman, Farjana International University of Business Agriculture and Technology Uttara Model Town Miyan Research Institute Dhaka Bangladesh Kazla Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh London South Bank University Dept. of Data Science 103 Borough Rd LondonSE1 0AA United Kingdom Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jeonbuk National University Graduate School of Integrated Energy of AI North Jeolla Jeonju Korea Republic of

ISBN: (纸本)9798350388282

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers' socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used. © 2024 IEEE.

关键词： Fruits

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Risk Estimation in a Markov Cost Process: Lower and Upper Bounds 41

Risk Estimation in a Markov Cost Process: Lower and Upper Bo...

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41st International Conference on Machine Learning, ICML 2024

作者： Thoppe, Gugan Prashanth, L.A. Bhat, Sanjay P. Bengaluru India Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ϵ-accuracy, either in expected or high-probability sense, requires at least Ω(1/ϵ2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs’ mean. Even in that case, our lower bound of Ω(1/ϵ2) improves upon the existing Ω(1/ϵ) bound (Metelli et al., 2023). Copyright 2024 by the author(s)

关键词： Cost estimating

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Ocular Cataract Identification Using Deep Convolutional Neural Networks 6

Ocular Cataract Identification Using Deep Convolutional Neur...

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6th International Conference on Artificial Intelligence, Big data, Computing and data Communication Systems, icABCD 2023

作者： Manuel, Feliciana M. E. Saide, Saide Manuel Ali, Felermino D. M. A. Lotfi, Sanae Lurio University Faculty of Engineering Dept. of Computer Engineering Pemba Mozambique New York University Center for Data Science New York United States

ISBN: (纸本)9798350314809

Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images. © 2023 IEEE.

关键词： Convolution

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Creating UML Class Diagrams with General-Purpose LLMs

Creating UML Class Diagrams with General-Purpose LLMs

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IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT)

作者： Mina Shehata Blaire Lepore Hailey Cummings Esteban Parra Dept. of Math Computer Science and Data Science Belmont University Nashville TN USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

General-purpose large language models (LLMs) have become a versatile tool in software development and maintenance. These models offer support in tasks such as under-standing, writing, and summarizing code. While these LLMs can generate code quickly their use in software modeling is relatively under-explored. This research aims to study ChatGPT's ability to generate class diagrams from source code. To do this, we engineered a prompt that takes in source code to create a UML class diagram for that system. We used this prompt to create class diagrams for 40 systems and assessed the diagrams on their correctness and structure. Our results show that ChatGPT creates class diagrams that accurately capture 90% of the classes and their attributes and 66 % of the associations. While ChatGPT performed nearly flawlessly for smaller projects, the diagrams for larger projects had more issues. We conclude that ChatGPT is best utilized as a complementary tool rather than the sole resource for software modeling and maintenance.

关键词： Visualization Codes Source coding Scalability Unified modeling language Writing Chatbots Software systems Maintenance Software development management

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