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Advancements in Sensor Technology and Computer Vision for Enhanced Road Safety and Anomaly Detection using Machine learning Approach 3

Advancements in Sensor Technology and Computer Vision for En...

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3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Jesi, V. Elizabeth Jenitha Mary, L. Saranya, S. Poonguzhali, C. Mohanaprakash, T.A. Saranya, K. School of Computing College of Engineeering Srm Institute of Science and Technology Department of Networking and Communications Kattankulatur Chennai India Dhanalakshmi College of Engineering Department of Cse Chennai India Rajalakshmi Institute of Technology Department of Aids Chennai India S A Engineering College Department of Ece Chennai India Panimalar Engineeering College Department of Cse Chennai India Panimalar Engineeering College Department of Aids Chennai India

ISBN: (纸本)9798350306989

Development of sensor technology has significantly increased recently. To combat the high rates of fatalities and expenditures connected with injuries caused by traffic, sensor technology and computer vision have achieved considerable advancements. To improve road safety, an intelligent system has been created with the aim of spotting anomalies in the road and providing driving support. Utilizing a combination of accelerometer sensors, vibration motion sensors, gyroscopic sensors, and GPS technology, this system is smoothly integrated into cars. A comprehensive and evidence-based systematic review focused on Computer Vision applications for automated Road Defect and Anomaly Detection is still lacking, despite the fact that prior studies and applications of computer vision have concentrated on particular aspects of road hazards. The system can identify several types of road irregularities based on the current location of the vehicle by using machine learning techniques to examine sensor data. Additionally, it gathers and compares data from other vehicles at the same GPS locations to confirm the presence of road problems like speed bumps or potholes. The GPS application provides access to a cloud-based repository where all the acquired data is safely kept. The technology notifies drivers ahead of time, advising them to slow down as they approach GPS spots that have previously been identified as having road abnormalities. The results of this poll could provide the scientific community with insightful information that would help improve traffic conditions and safety in general. experience. © 2023 IEEE.

关键词： Anomaly detection

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A Study of Deep learning Models in Diagnosis and Prediction of Chronic Diseases 3

A Study of Deep Learning Models in Diagnosis and Prediction ...

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3rd international conference on Issues and Challenges in intelligent Computing Techniques, ICICT 2022

作者： Begum, Sayyada Hajera Vidyullatha, P. Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering A.P Vaddeswaram India

ISBN: (纸本)9781665482684

With the introduction of big data and its incredible advancement in image procurement devices, transformation of medical data into valuable knowledge has become an important challenge in the area of bioinformatics. The medical images procured require huge analysis and diagnosis of images using AI techniques like Machine and Deep learning (ML, DL) that yields automated diagnosis solutions. Deep learning methods can provide optimized and precise solutions for medical image diagnosis and can be an important methodology for imminent health care applications. Deep learning in biomedical image classification, automated image classification can aid in effective and early treatment of various diseases thus reducing mortality and morbidity rate. This paper gives an introduction to Deep learning models and reviews some contemporary deep learning models and their application in detecting various diseases. © 2022 IEEE.

关键词： Image classification

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An intelligent Decision Support System for Road Freight Transport 23rd

An Intelligent Decision Support System for Road Freight Tran...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Carvalho, Hugo Silva Pilastri, Andre Matta, Arthur Matos, Luis Miguel Novais, Rui Cortez, Paulo CCG ZGDV Inst EPMQ Guimaraes Portugal ABMN Business Solut Azurem Guimaraes Portugal Univ Minho Dept Informat Syst ALGORITMI Res Ctr LASI Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

This paper presents an intelligent Decision Support System (IDSS) to optimize transport and logistics activities in a set of Portuguese companies currently operating in the freight transport sector. This IDSS comprises three main modules that can be used individually or chained together, dedicated to: a geographic clustering detection of transport services;a transport driver suggestion;and a route and truck-load optimization. The IDSS was entirely designed and developed to support real-time data and it consists of an end-to-end solution (E2ES), given that it covers all the main transport and logistics processes since the registration in the database to the optimized transport plan. The entire set of functionalities inserted in the IDSS was designed and validated by freight transport sector experts from the different companies that will use the proposed system.

关键词： intelligent decision support system Transport and logistics Geospatial clustering Resource optimization Route optimization TAM 3

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On Autonomous Drone Navigation Using Deep learning and an intelligent Rainbow DQN Agent 23rd

On Autonomous Drone Navigation Using Deep Learning and an In...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Karatzas, Andreas Karras, Aristeidis Karras, Christos Giotopoulos, Konstantinos C. Oikonomou, Konstantinos Sioutas, Spyros Univ Patras Comp Engn & Informat Dept Patras Greece Univ Patras Dept Management Sci & Technol Patras Greece Ionian Univ Dept Informat Corfu Greece

ISBN: (纸本)9783031217524;9783031217531

Drones are intelligent devices that offer solutions for a continuously expanding variety of applications. Therefore, there would be a significant improvement if these systems could explore space automatically and without human-supervision. This work integrates cutting-edge artificial intelligence techniques that allow drones to travel independently. Following an overview of reinforcement learning methods built for discrete action space settings, a multilayer Perceptron model is constructed for feature extraction along with Hybrid neural networks. The agent employed in the experiments is a Rainbow DQN agent trained on the AirSim simulator. The experimental results are encouraging as the agent was tested for 16 missions and the accuracy was higher than 93%. In particular, the success for action selection was 97% and 93 for mission success. Finally, future work related to the navigation of autonomous drones is discussed including current concepts and methods of integration with more sophisticated algorithmic approaches.

关键词： Drones Autonomous drone navigation Deep learning Reinforcement learning intelligent agents Swarm intelligence UAV

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Effective Prevention of Semantic Drift in Continual Deep learning 23rd

Effective Prevention of Semantic Drift in Continual Deep Lea...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Saadi, Khouloud Khan, Muhammad Taimoor Natl Univ Comp & Emerging Sci Islamabad Pakistan

ISBN: (纸本)9783031217524;9783031217531

Lifelong machine learning or continual learning models attempt to learn incrementally by accumulating knowledge across a sequence of tasks. Therefore, these models learn better and faster. They are used in various intelligent systems that have to interact with humans or any dynamic environment. Dynamically expandable networks are continual deep learning models that allow its architecture to expand with a sequence of tasks. The model retains knowledge from the previous tasks that results in high performance on newer tasks. The existing models use Minkowski distance measures to separate nodes of the current network, resulting in higher catastrophic forgetting. These measures are susceptible to high dimensional sparse vectors, resulting in sub-optimum performance. We propose ang-DEN, as a dynamically expanding continual learning architecture that use angular distance metric. It addresses semantic drift through better separation of nodes achieving 97% average accuracy with an improvement of 1.3% across all tasks on MNIST variant datasets.

关键词： Continual deep learning Catastrophic forgetting Semantic drift ang-DEN

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Deep learning Based Predictive Analytics for Decentralized Content Caching in Hierarchical Edge Networks 23rd

Deep Learning Based Predictive Analytics for Decentralized C...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Chakraborty, Dhruba Rabbi, Mahima Hossain, Maisha Khaled, Saraf Noor Oishi, Maria Khanom Alam, Md Golam Rabiul Brac Univ 66 Mohakhali Dhaka 1212 Bangladesh

ISBN: (纸本)9783031217524;9783031217531

The content-centric network is a state-of-the-art networking architecture for content distribution and content caching. However, it is inefficient to cache every content in each network device. The modern edge computing technology opens the door for content caching on the edge of the network. However, still, we have to decide which content we should cache and which content we should replace from the cache. Deep learning-based predictive analytics can play an important role in selecting content for caching purposes. In this research, we will use LSTM-based Recurrent Neural Network(RNN) for decentralized content caching at the hierarchical edge of the network.

关键词： Predictive analytics Content Caching Edge networking Deep learning Recurrent Neural Network(RNN) Long short-term memory(LSTM) Decentralized Hierarchical

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Benchmarking data Augmentation Techniques for Tabular data 23rd

Benchmarking Data Augmentation Techniques for Tabular Data

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Machado, Pedro Fernandes, Bruno Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

Imbalanced learning and small-sized datasets are usual in machine learning problems, even with the increased data availability provided by recent developments. The performance of learning algorithms in the presence of unbalanced data and significant class distribution skews is known as the "imbalanced learning problem". The models' performance on such problems can drastically decrease for certain classes with an uneven distribution because the models do not learn the distributive features of the data and present accuracy too favorable for a specific set of classes of data. As an example, this can have negative consequences when talking about cancer detection since the model may poorly identify unhealthy patients. Hence, data augmentation techniques are usually conceived to evaluate how models would behave in non-data-scarce environments, generating synthetic data that mimics the characteristics of real data. By applying those techniques, the amount of available data can be increased, balancing the class distributions. However, there are no standardized data augmentation processes that can be applied to every domain of tabular data. Therefore, this study aims to identify which characteristics of a dataset provide a better performance when synthesizing samples by a data augmentation technique in a tabular data environment.

关键词： data augmentation Imbalanced data Machine learning

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Smart GPS Based Vehicle Speed Limit Controller on zone identification using Geo-Fencing Algorithm 3

Smart GPS Based Vehicle Speed Limit Controller on zone ident...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Upendra, Modem Reddy, Varun Safa, M. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattankulathur Chennai603203 India

ISBN: (纸本)9798350381931

In light of growing concerns about road safety and the surge in traffic accidents, this project aspires to an Internet of Things smart GPS-based vehicle speed limit controller system. The primary objective is to elevate road safety by harnessing real-time data and automated control mechanisms to enforce speed limits, thereby diminishing incidents of speeding-related accidents. This project adopts a comprehensive approach, integrating both hardware and software components. Vehicle-mounted sensors, encompassing speed sensors and GPS receivers, continually gather real-time data on vehicle speed and location. Furthermore, road condition data is sourced from multiple outlets, such as weather stations and traffic cameras. This data is wirelessly transmitted to a central control unit, which processes and analyzes it. The control unit deploys machine learning algorithms to evaluate vehicle speed limits against predefined speed limits for specific road segments. Depending on this analysis, it transmits control signals back to the vehicle when necessary. These signals can encompass driver warnings, power limitations, or autonomous speed control. These methodologies collectively enabled the creation of an intelligent and adaptable GPS-based vehicle speed control system, offering the potential to significantly enhance road safety by dynamically enhancing road safety regulating vehicle speeds in different zones, ensuring the safety of people and drivers alike. The Smart GPS-based vehicle speed limit controller system has undergone successful development and testing, both in controlled environments and on public roads. The outcomes manifest a remarkable reduction in incidents of speeding and an enhanced adherence to speed limits, exhibiting an accuracy level exceeding 96%. In real-world scenarios, the system effectively mitigates speeding-related risks, leading to a substantial decline in traffic accidents. Its ability to adapt to changing road conditions and. Its scalability ren

关键词： Internet of things

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Efficient Sensor Selection for Individualized Prediction Based on Biosignals 23rd

Efficient Sensor Selection for Individualized Prediction Bas...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Vieth, Markus Grimmelsmann, Nils Schneider, Axel Hammer, Barbara Bielefeld Univ Bielefeld Germany Bielefeld Univ Appl Sci Bielefeld Germany

ISBN: (纸本)9783031217524;9783031217531

Soft sensors combine a hardware component with an intelligent algorithmic processing of the raw sensor signals. While individualization of software components according to a person's specific needs is comparably cheap, individualization of the sensor hardware itself is usually impossible in mass production. At the same time, the number of raw sensors should be minimum to reduce production costs. In this contribution, we propose to model this challenge as a feature selection problem, which optimizes a feature set simultaneously with respect to a family of functions corresponding to individualized post-processing of sensor signals. This concept is integrated into a number of different classical feature selection schemes, and evaluated in the context of the placement of pressure sensors as part of a shoe insole. It turns out that feature selection respecting the class of functions is superior to both placement based on anatomical considerations and classical feature selection methods.

关键词： intelligent wearables Feature selection Model individualization

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Tabular data Interpretation on COVID-19 Through Explainable Machine learning 3rd

Tabular Data Interpretation on COVID-19 Through Explainable...

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3rd international conference on "Emerging Trends and Technologies on intelligent Systems" (ETTIS-2023)

作者： Jo, Ashly Ann Paul, Caina Rose Arundhathi Nair, S. Thomas, Jeena Raj, Ebin Deni Department of Computer Science and Engineering Indian Institute of Information Technology Valavoor—Chakkampuzha Rd Kottayam686635 India

ISBN: (纸本)9789819939626

Over the last decade, several AutoML techniques have been introduced for developing fully automated ML pipelines, from preprocessing to final deployment. Researchers have shown that consumers do not have trust in AutoML-generated models because of data gaps and errors. Despite the fact that such autonomously synthesized ML pipelines are capable of achieving a comparable performance. The lack of explanations for the built-in ML pipelines and the transparency of AutoML systems affect the clinical decisions in Healthcare. The paper builds on a substantial body of related work in the fields of trust in artificial intelligence (AI), XAI, AutoML, and explaining the decision-making of AutoML systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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