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A Smart Vehicle Monitoring System Using Real-Time data Processing and Advanced Sensor Integration 5

A Smart Vehicle Monitoring System Using Real-Time Data Proce...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sathish, M. Prabha Devi, D. Kanchana, R. Pranesh, R.K. Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telangana Hyderabad India Kumaraguru College of Technology Department of Information Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

the 'Smart Vehicle Monitoring System' presents a comprehensive solution for enhancing road safety and user authentication in the realm of modern transportation. this system integrates advanced technologies to monitor crucial aspects of vehicle usage, ensuring both the rider's safety and the vehicle's safety. the core functionalities encompass monitoring the usage of safety gear such as helmets and detecting alcohol consumption. User authentication is achieved through a combination of fingerprint recognition and Radio-frequency identification (RFID). this multi-layered approach ensures that only authorized individuals can operate the vehicle. the proposed system further extends its capabilities to monitor essential vehicle parameters, including tire pressure and fuel levels. In the event of an accident, a vibration sensor is employed to swiftly detect impact, triggering an alert system through GSM communication. this timely notification ensures prompt assistance and emergency response. Wireless communication plays a vital role in the system, with the helmet connected to an Arduino Mega via the ESP protocol. this acilitates real-time data exchange, allowing for seamless integration of various components. To enhance user interface and information dissemination, the system utilizes an LCD to present real-time status updates. Additionally, the integration of a GPS module enables the sharing of live location data, contributing to improved navigation and overall safety. In conclusion, the Smart Vehicle Monitoring System not only prioritizes the safety of the rider but also addresses authentication concerns and provides a holistic approach to vehicle monitoring. through the amalgamation of cutting-edge technologies, this system aims to redefine the standards of safety and security in contemporary transportation. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Efficient Method of Parameters Optimization of LSSVM for Large Sample data 5

Efficient Method of Parameters Optimization of LSSVM for Lar...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Zhao, Qiang Zhang, Cheng-Long Xu, Ran Cao, Yu-Fei Li, Yang Beijing Institute of Electronic System Engineering Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In order to effectively solve the problem of traditional LSSVM being computationally infeasible for large sample data, an efficient method of parameters optimization of least squares support vector machine (LSSVM) for large sample data is proposed. the regularization parameter and Gaussian kernel parameter are optimized by using the sequential optimization method based on Kriging surrogate. three classical regression datasets, i.e., abalone, cpusmall and space_ga, are used to validate the above method. then, a comparison is made between the prediction results obtained by using the proposed method and by directly using the LS-SVMlab toolbox. the results show that the proposed method can not only assure the prediction accuracy but also reduce the training time markedly. Finally, the proposed method is successfully applied to calculate the firing data of precision weapons. © 2022 IEEE.

关键词： Kriging

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A Novel Approach Exploiting machine learning to Detect SQLi Attacks 5

A Novel Approach Exploiting Machine Learning to Detect SQLi ...

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5th IEEE international conference on Advanced Systems and Emergent Technologies (IC_ASET)

作者： Ashlam, Ahmed Abadulla Badii, Atta Stahl, Frederic Univ Reading Dept Comp Sci Reading Berks England German Res Ctr Artificial Intelligence GmbH DFKI Lab Niedersachsen Marine Percept D-26129 Oldenburg Germany

ISBN: (纸本)9781665427357

the increasing use of Information Technology applications in the distributed environment is increasing security exploits. Information about vulnerabilities is also available on the open web in an unstructured format that developers can take advantage of to fix vulnerabilities in their IT applications. SQL injection (SQLi) attacks are frequently launched with the objective of exfiltration of data typically through targeting the back-end server organisations to compromise their customer databases. there have been a number of high profile attacks against large enterprises in recent years. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP. Various machine learning and deep learning algorithms have been applied to detect and prevent these attacks. However, such preventive attempts have not limited the incidence of cyber-attacks and the resulting compromised database as reported by (CVE) repository. In this paper, the potential of using data mining approaches is pursued in order to enhance the efficacy of SQL injection safeguarding measures by reducing the false-positive rates in SQLi detection. the proposed approach uses CountVectorizer to extract features and then apply various supervised machine-learning models to automate the classification of SQLi. the model that returns the highest accuracy has been chosen among available models. Also a new model has been created PALOSDM (Performance analysis and Iterative optimisation of the SQLI Detection Model) for reducing false-positive rate and false-negative rate. the detection rate accuracy has also been improved significantly from a baseline of 94% up to 99%.

关键词： data mining OWASP SQL injection attacks false positive false negative CountVectorizer

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Search-Time Efficient Device Constraints-Aware Neural Architecture Search 10th

Search-Time Efficient Device Constraints-Aware Neural Archit...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Dutta, Oshin Kanvar, Tanu Agarwal, Sumeet Indian Inst Technol Delhi India

ISBN: (纸本)9783031451690;9783031451706

Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each device is infeasible due to their varying memory and computational constraints. To address these concerns, we automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS). We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints such as model size and floating-point operations. It incorporates weight sharing and channel bottleneck techniques to speed up the search time. Based on our experiments, we see that DCA-NAS outperforms manual architectures for similar sized models and is comparable to popular mobile architectures on various image classification datasets like CIFAR-10, CIFAR-100, and Imagenet-1k. Experiments with search spaces-DARTS and NAS-Bench-201 show the generalization capabilities of DCA-NAS. On further evaluating our approach on HardwareNAS-Bench, device-specific architectures with low inference latency and state-of-the-art performance were discovered.

关键词： Neural Architecture Search DARTS Meta-learning Edge Inference Constrained Optimization

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Episodic Projection Network for Out-of-Distribution Detection in Few-shot learning 26

Episodic Projection Network for Out-of-Distribution Detectio...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Fan, Jiawei Ou, Zhonghong Yu, Xie Yang, Junwei Wang, Shigeng Kang, Xiaoyang Zhang, Hongxing Song, Meina Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the increasing demands of safety-critical computer vision applications have attracted extensive research on Out-of-Distribution (OOD) detection in recent years. Nevertheless, a large proportion of real-world tasks are in low-data regime, and the gap between meta-learning paradigm and OOD detection mechanism causes low performance in few-shot settings. In order to bridge the gap, we first propose an simple yet effective Episodic Projection Scheme (EPS). EPS is designed to project feature vectors to task-specific feature space for OOD detection, without sacrificing generalization of few-shot models. We then construct a multi-modal representation space for few-shot OOD detection by employing representations of the labels and their synonyms. At last, we put forward a few-shot OOD detection framework named Episodic Projection Network (EPN), which can integrate many kinds of perturbation based OOD algorithms with ease. To verify effectiveness of the proposed scheme, we implement several OOD algorithms into EPN and conduct experiments on two few-shot classification datasets, i.e., Omniglot and mini-ImageNet. Experimental results demonstrate that accuracy has been increased by 5% by integrating the OOD algorithms into the EPN framework.

关键词： Computer vision Semantic segmentation Perturbation methods Neural networks Object detection Feature extraction Classification algorithms

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A Review on the Application of Radiomics and Deep learning for Disease Identification in Musculoskeletal Radiography 5

A Review on the Application of Radiomics and Deep Learning f...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Das, Shyamali Rama Devi, C. Nayagam, M. Gomathy Suganthi, D. thachamkode, Fidha Senthil, K. Manoj Cmr Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India St.Joseph's College of Engineering Electrical and Electronics Engineering India Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Science Chennai602105 India Kmct Dental College Department of Oral Medicine and Radiology Kerala Kozhikode673602 India Kongu Engineering College Electronics and Communication Engineering Erode638 060 India

ISBN: (纸本)9798350321425

Musculoskeletal disorders cause most disability worldwide. Musculoskeletal (MSK) issues are diagnosed, predicted, and treated utilizing imaging methods, magnetic resonance imaging (MRI), and ultrasound, and their precise interpretation by specialized radiologists. Radiological scanning aids metabolic health, aging, and diabetes assessment. Proposed research shows how machine learning, specifically deep learning, may be used to analyze MRI data quickly and reliably, addressing a crucial clinical need in MSK radiology. As primary care radiography services grow, so does interest in how AI, and especially deep learning, might help radiologists and primary care physicians make accurate diagnoses and enhance patient treatment. Chronic pain impairs mobility, dexterity, and function. Diagnosis requires bone X-rays. Deep learning algorithms are increasingly used in musculoskeletal radiology, with promising outcomes. the proposed method uses a calibrated ensemble of deep learners to musculoskeletal radiograph recognition. In the suggested model uses three deep neural networks, which are commonly utilized in deep learning-based solutions for this problem domain. Publicly available dataset to demonstrates the suggested strategy outperforms three individual models and a standard ensemble learner. this study strongly supports the use of a calibrated ensemble approach for identifying abnormalities in musculoskeletal X-rays, where the greatest radiologist accuracy is between 0.95 and 0.98 in Cohen's kappa statistic. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Fifth international conference on Artificial Intelligence and Computer Science, AICS 2023

Fifth International Conference on Artificial Intelligence an...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

ISBN: (纸本)9781510668621

the proceedings contain 143 papers. the topics discussed include: lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data;research on rock image classification algorithm based on multi-task learning and pseudo-labels;research on intelligent recognition algorithm for common roadbed diseases based on radar images;pre-planning of individualized talus implants based on computed tomography-automated segmentation;mining order-preserving submatrix based on charm for gene expression data;an improved watershed algorithm and its application in image edge extraction;research on augmented reality registration method based on SA-PSO algorithm;a personalized test paper generation model for English examination based on knowledge tracking;and improved ACLPSO algorithm for automatic parameter optimization.

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Enhancing Weed recognition in Cotton Fields through Advanced Imaging and learning Techniques 5th

Enhancing Weed Recognition in Cotton Fields Through Advanced...

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5th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Singh, Jagendra Shaik, Nazeer Sahu, Dinesh Prasad Tiwari, Mohit Haque, Mustafizul Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Dr. D. Y. Patil Vidyapeeth’s Centre for Online Learning Dr. D. Y. Patil Vidyapeeth Pune India Ghaziabad India

ISBN: (纸本)9789819788606

this research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models’ ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cotton

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Target Detection for Running Around Obstacles Based on Improved YOLOv5 Algorithm

Target Detection for Running Around Obstacles Based on Impro...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Guo Hui Zhao Meng Sports Equipment Industry Technology Research Institute Shenyang University of Technology Shenyang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In order to save manpower & resources, make the test results more accurate, and make the testing equipment easier to handle during the 15 meter obstacle running test process, the YOLOv5 algorithm in object detection will be used to intelligently upgrade the obstacle running test technology. the YOLOv5 algorithm will be improved to further improve the detection accuracy. Add attention mechanism module to YOLOv5 algorithm and use multiple loss functions: GIoU and CIoU, train the dataset again, compare the obtained training results, and retain the appropriate loss function for this experiment. through comparison of results, it was found that the CIoU loss function is more suitable for this experiment, and the improved algorithm has an accuracy of over 90% for target recognition. the use of intelligent target recognition technology can effectively reduce the occurrence of personnel fatigue, data bias, and other related issues during the detection process. At the same time, it also simplifies the testing equipment, realizes the intelligent upgrade of testing technology, and improves the accuracy of detection.

关键词： YOLO Training Meters Accuracy machine learning algorithms Target recognition Fatigue pattern recognition Personnel Testing

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Enhance Large Language Models for Scientific Paper Question Answering

Enhance Large Language Models for Scientific Paper Question ...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Zhonghan Chen Yongquan Jiang Yan Yang Xuanpei Jiang Qiangwei Zhang School of Computer and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Recently, large language models (LLMs) have demonstrated promising applications across various domains, offering significant assistance to humanity. this study explores how to refine LLMs through fine-tuning to enhance their performance on scientific paper question answering tasks. We begin by collecting and constructing a composite task dataset that includes both textual and tabular data. To enhance the LLM's capabilities on composite tasks, we propose Mixture LoRA (MixLoRA) approach, which combines the advantages of composite task learning and parameter-efficient fine-tuning. Furthermore, we include general domain experts in MixLoRA to alleviate the issue of knowledge forgetting in the model in order to maintain its general capabilities. Comprehensive experiments are designed to validate the effectiveness of the method, with the results indicating that the approach enables the model to successfully learn downstream tasks with different data distributions while also maintaining the model's generalization abilities.

关键词： Humanities Large language models Optical character recognition Manuals machine learning Routing Multitasking Question answering (information retrieval) data models pattern recognition

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