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A Neural Network Model for Autonomous Vehicles Safety Check Using Camera-only/Camera-Lidar Images

A Neural Network Model for Autonomous Vehicles Safety Check ...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Dengyi Liu Honggang Wang Hua Fang Graduate Computer Science and Engineering Department Katz School of Health and Science Yeshiva University NY Dept. of Computer and Information Science University of Massachusetts Dartmouth MA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Camera and LiDAR sensors play a crucial role in vehicle perception systems, enabling accurate detection of obstacles and other vehicles in autonomous driving technologies. Despite their complementary advantages, ensuring the reliability of camera data under varying environmental conditions poses a significant challenge. This paper introduces a novel system for camera data consistency checking through two methods: (1) a camera-only consistency checking mechanism utilizing an effective point-to-point feature extraction neural network inspired by the D2-Net method, and (2) a camera-LiDAR fusion data consistency checking approach employing advanced sensor fusion techniques. By integrating a critical reliability check for camera data and fusing it with LiDAR information, our system leverages the strengths of both sensor types to enhance the robustness and efficiency of autonomous vehicle navigation. Experimental results demonstrate that the proposed methods significantly improve the safety and performance of autonomous driving by ensuring reliable sensor data fusion, enabling safe navigation even in challenging environments.

关键词： Deep learning Accuracy Neural networks Sensor fusion Cameras Feature extraction Sensor systems Sensors Safety Autonomous vehicles

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Semi-automatic performance support robot that can attach and detach guitars

Semi-automatic performance support robot that can attach and...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Kazuki Tsurumi Ryo Marutsuka Gou Koutaki Dept. of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Research has primarily focused on fully automatic instrument-playing robots. However, it is difficult to support the experience of playing musical instruments for people with physical disabilities or those who are inexperienced in playing musical instruments. To address this issue, we studied a "semi-automatic" instrument-playing robot based on human-robot collaboration. Our research specifically focused on the guitar, which are stringed instruments with complex pressed strings. The developed robot enables individuals with physical disabilities and those without musical experience to easily play musical instruments. Additionally, the instruments can be easily removed without causing damage.

关键词： Performance evaluation Power demand Instruments Noise Music Pressing Paralysis Wrapping Mechanical engineering Robots

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Defending Authenticity: Real-Time Signature Verification with Deep Learning Models

Defending Authenticity: Real-Time Signature Verification wit...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Malireddy Charan Kumar Reddy Katragadda Megha Shyam Kandlapalli Aravind Sai Vishwas Hn Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Deep learning-based signature verification uses sophisticated neural network topologies to verify signatures. Deep learning models may be trained to discriminate between authentic and falsified signatures with a high degree of accuracy by utilizing methods such as vgg, xceptionmodels and Transfer Learning. These models are made to examine the complex characteristics and patterns found in signatures, which allows them to identify minute variations that could point to fraud. Deep learning methods provide a reliable and effective way to handle jobs involving the verification of signatures by utilizing large libraries and complex algorithms. The implementation of deep learning techniques for image processing and classification tasks, which can be adapted for signature verification applications. By preprocessing signature images, extracting relevant features, and training deep learning models on labeled data, the system can learn to differentiate between authentic and forged signatures effectively. Using deep learning involves the development and deployment of neural network models that can accurately authenticate signatures by analyzing their unique characteristics and patterns.

关键词： Deep learning Training Handwriting recognition Adaptation models Accuracy Neural networks Transfer learning Writing Feature extraction data models

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A Proposed Framework for Smart Geo-Fencing for Religious Places 7

A Proposed Framework for Smart Geo-Fencing for Religious Pla...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Vishnoi, Ankit Choudhury, Tanupriya Patni, Jagdish Chandra Sar, Ayan Mahdi, Hussain Falih Maharashtra Pune412115 India CSE Dept. Lavale Campus Maharashtra Pune412115 India Pune India School of Computer Science Uttarakhand Dehradun248007 India University of Diyala Dept. of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350383065

The innovative system known as 'Smart Geo-fencing for Religious Places' integrates Blockchain and Zero Power Device technology to enhance the reverence of sacred sites. By amalgamating the robustness of blockchain with geofencing capabilities and electronic apparatuses, this solution delineates invisible perimeters encircling religious structures like mosques, churches, and temples. As worshippers enter these digitally marked zones with their smartphones, the setup is capable of monitoring select operations such as making calls, sending texts, or receiving app alerts to foster a serene atmosphere. Furthermore, leveraging blockchain's prowess guarantees that all recorded data remains secure and unalterable. The incorporation of devices that consume minimal energy is crucial for ensuring sustainability. This sophisticated fusion promotes tranquility within religious communities by adept.y marrying contemporary technological advancements with age-old spiritual practices. © 2023 IEEE.

关键词： Blockchain

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Deliberate Occlusion: Asymmetry in Facial Expression 6

Deliberate Occlusion: Asymmetry in Facial Expression

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Chan, Hung-Tse Chao, Yen-Chun Hsia, Chih-Hsien Nat. Taiwan Univ. of Sci. and Tech. Dept. of Electronic and Computer Engineering Taipei City Taiwan Asia American International Academy New Taipei City Taiwan National Ilan University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350323535

Facial expression recognition (FER) has become increasingly common in research. However, region feature technologies that play an important role in this field have not been well developed. In this study, a psychology-based solution to address problems in FER of regional features was proposed. This method employs the use of deliberate occlusion for enhancement and attention module to find meaningful and important features in the regions processing features with high inter-class similarity and variation. The result of this study shows that the approach has higher accuracy than recent models and, through the use of feature heatmap visualization, the possibility of approaching FER from human facial asymmetry is raised. © 2023 IEEE.

关键词： Face recognition

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Structured Implementation of ML Algorithms for Cardiovascular Disease Detection

Structured Implementation of ML Algorithms for Cardiovascula...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Drishti Arora Farhan Asif Dept. of Computer Science & Engineering Amity University Uttar Pradesh India

Cardiovascular diseases encompass a range of medical conditions that impact the heart and blood vessels The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field According to the WHO, heart diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths To address the need for accurate heart disease prediction, extensive research has employed advanced machine learning models This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers Using these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual’s risk The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods It exhibited remarkable accuracy, yielding prediction results of approximately 90.16% This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study.

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IoT-Based Smart Gauntlet to Assist Physically Challenged and Fragile Mortals

IoT-Based Smart Gauntlet to Assist Physically Challenged and...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Thangam S Gurupriya M Kanderi Johith Kumar Kuthati Shreya Lasya Priya Divakarla Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Individuals who are bedridden or incapacitated struggle to do particular daily tasks because they have trouble with specific hand movements. Some people can need extra help to do jobs or communicate easily. In this situation, flex sensors and an Arduino microcontroller are two features that make IoT smart gloves useful. When a person moves their fingers, the sensors detect those motions, interpret the gestures, and provide the appropriate output to an LCD monitor. The buzzer will sound, the LED will glow, and the carer will receive an emergency alert in case of an emergency. With the assistance of the smart glove, the user can utilize hand motions to turn on the fan or light.

关键词： Temperature sensors Temperature measurement Pressure sensors Microcontrollers Sensor phenomena and characterization Message services Light emitting diodes

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Enhancing Credit Card Fraud Detection: A Machine Learning Framework

Enhancing Credit Card Fraud Detection: A Machine Learning Fr...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Tarek Aziz Md. Maskat Sharif Sha Md Farid Tanjim Mahmud Sapayev Valisher Odilbek Uglu Mohammad Shamsul Arefin Abubokor Hanip Mohammad Shahadat Hossain Dept. of Computer Science and Engineering Dept. of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Technology Wilmington University Delaware United States Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of General Professional Subjects Mamun University Khiva Uzbekistan Washington University of Science and Technology (WUST) Alexandria VA USA Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In the current digital age, credit card tracking is an essential component of financial security. The aim of this study is to use the 1,604,292 data in the Credit Card Transactions Prevention dataset from Kaggle to create efficient fraud detection algorithms. 1,048,574 records in the dataset are put aside for training, and the remaining records are set aside for testing. Preprocessing techniques like one-hot encoding for classification parameters and label encoding for numbers are used to get the data ready for analysis. The preprocessed dataset is then used to train a variety of machine learning classifiers, such as Random Forest, Decision Tree, Gradient Boosting (GB), and Logistic Regression. The goal is to use binary target properties to identify fraudulent transactions. The Random Forest classifier obtains the highest accuracy, with an astonishing 99.47%, according to the results. Remarkably, the system exhibits infrequent occurrences of data loss, underscoring its resilience and efficiency in identifying fraudulent activity. This study emphasizes how important it is to fight credit card theft by using sophisticated machine-learning strategies and meticulous data pretreatment procedures. The results add to the continuing efforts to improve bank security and shield customers from fraudulent online transactions.

关键词： Training Logistic regression Machine learning algorithms Prevention and mitigation Credit cards Encoding Fraud Random forests Testing Resilience

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Revolutionizing Diabetes Prediction Models: Leveraging Advanced Feature Reduction and Classification Methods

Revolutionizing Diabetes Prediction Models: Leveraging Advan...

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Learning and Technology Conference (L&T)

作者： Ameera Attiah Mawaddah Alagha Lujain Bukassim Passent El-Kafrawy Computer Science Dept. College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection techniques to improve diabetes prediction models. Utilizing Principal Component Analysis (PCA) and the Artificial Bee Colony (ABC) algorithm, the study focuses on optimizing feature selection to enhance model accuracy, computational efficiency, and interpretability. Employing two diverse datasets—the Behavioral Risk Factor Surveillance System (BRFSS) and the Pima Indians Diabetes dataset—the research evaluates the performance of Random Forest and Logistic Regression models. Results reveal significant improvements in predictive accuracy and computational efficiency, particularly with ABC-selected features, reducing feature dimensionality while maintaining high accuracy. This study underscores the potential of combining PCA and ABC to revolutionize predictive healthcare analytics, offering robust tools for diabetes management and prevention.

关键词： Dimensionality reduction Accuracy Computational modeling Biological system modeling Medical services Predictive models Feature extraction Diabetes Computational efficiency Principal component analysis

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Enhancing Question-Answering with Knowledge Graph Retrieval and Generation using LLMs

Enhancing Question-Answering with Knowledge Graph Retrieval ...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Supraj Gijre Rishi Agrawal Priyash Laddha Gunjan Keswani Dept. Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India School of Computer Science and Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

This paper describes a novel technique to improving Large Language Models (LLMs) for document analysis that employs knowledge graphs and retrieval-augmented generation (RAG). We are working on constructing a chatbot system that can handle and analyze large documents from a variety of fields. Our approach addresses basic LLM issues including context maintenance and hallucination avoidance. The system combines document chunking, vector embedding, and similarity search with graph-based knowledge representation. Users can upload large papers and answer questions accurately. We show that integrating standard information retrieval approaches with graph-based storage and LLM capabilities improves context awareness and response accuracy across a wide range of document genres. This strategy is especially promising for complicated publications such as financial reports.

关键词： Text analysis Quantum computing Large language models Soft sensors Retrieval augmented generation Knowledge graphs Information retrieval Vectors Maintenance Standards

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