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FireLite: Leveraging Transfer Learning for Efficient Fire Detection in Resource-Constrained Environments

FireLite: Leveraging Transfer Learning for Efficient Fire De...

引用

international conference on computer and Information Technology (ICCIT)

作者： Mahamudul Hasan Md Maruf Al Hossain Prince Mohammad Samar Ansari Sabrina Jahan Abu Saleh Musa Miah Jungpil Shin Electronic and Electrical Engineering University of Chester Chester United Kingdom Dept. of Computer Science and Engineering German University Bangladesh Gazipur Bangladesh University of Chester United Kingdom Dept. of Computer Science and Engineering Bangladesh Army University of Science and Technology Saidpur Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Fire hazards are extremely dangerous, particularly in sectors such the transportation industry where political unrest increases the likelihood of their occurring. By employing IP cameras to facilitate the setup of fire detection systems on transport vehicles losses from fire events may be prevented proactively. However, the development of lightweight fire detection models is required due to the computational constraints of the embedded systems within these cameras. We introduce "FireLite," a low-parameter convolutional neural network (CNN) designed for quick fire detection in contexts with limited resources, in answer to this difficulty. With an accuracy of 98.77%, our model—which has just 34,978 trainable parameters—achieves remarkable performance numbers. It also shows a validation loss of 8.74 and peaks at 98.77 for precision, recall, and F1-score measures. Because of its precision and efficiency, FireLite is a promising.

关键词： Deep learning Accuracy Embedded systems Computational modeling Prevention and mitigation Transportation industry Transfer learning Cameras Loss measurement Convolutional neural networks

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Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives

Robust Surgical Tool Tracking with Pixel-based Probabilities...

引用

IEEE international conference on Robotics and Automation (ICRA)

作者： Christopher D’Ambrosia Florian Richter Zih-Yun Chiu Nikhil Shinde Fei Liu Henrik I. Christensen Michael C. Yip College of Physicians and Surgeons Columbia University La Jolla California U.S.A. Department of Computer Science and Engineering University of California San Diego La Jolla California U.S.A. Deparment of Electrical and Computer Engineering University of California San Diego La Jolla California U.S.A.

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Controlling robotic manipulators via visual feedback requires a known coordinate frame transformation between the robot and the camera. Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation. These errors result in poor localization of robotic manipulators and create a significant challenge for applications that rely on precise interactions between manipulators and the environment. In this work, we estimate the camera-to-base transform and joint angle measurement errors for surgical robotic tools using an image based insertion-shaft detection algorithm and probabilistic models. We apply our proposed approach in both a structured environment as well as an unstructured environment and measure to demonstrate the efficacy of our methods.

关键词： Visualization Uncertainty Medical robotics Robot kinematics Robot vision systems Transforms Manipulators

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Aircraft-Type Classification Using Deep Learning Algorithms

Aircraft-Type Classification Using Deep Learning Algorithms

引用

international IEEE conference on Intelligent systems, IS

作者： Noora Alkharji Hessa Almazrouei Shaikha Alzaabi Ali Bou Nassif Mariame Elsalhy Manar Abu Talib Department of Computer Engineering University of Sharjah Sharjah UAE Department of Computer Science University of Sharjah Sharjah UAE

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

In the field of artificial intelligence (AI), deep learning (DL) has become an essential methodology with many uses, particularly in the aviation sector. The efficiency and effectiveness of tasks connected to aircraft recognition and classification are considerably improved by DL. The goal of this study is to examine and deal with problems related to efficiency and accuracy in DL models for identifying and classifying aircrafts. In this article, we suggest a solution that utilizes a customized convolutional neural network (CNN) model that our team has created and assess its performance in comparison to other deep learning (DL) models like VGG16 and ResNet50. Accuracy, recall, precision, and f1-score are among the metrics used for evaluation. The outcomes of this study demonstrate significant gains and offer insightful information for future endeavors in the aviation field.

关键词： Deep learning Accuracy Atmospheric modeling Military aircraft Data models Classification algorithms Convolutional neural networks Noise measurement Aircraft Residual neural networks

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Application of CNN Classifier for Somatosensory ERP-Based Brain-computer Interface

Application of CNN Classifier for Somatosensory ERP-Based Br...

引用

IEEE international conference on Computational Intelligence and Virtual Environments for measurement systems and Applications

作者： Junlin Wang Xingyu Lu Ningbo Fei Xiaodong Li Yong Hu Department of Orthopaedics and Traumatology The University of Hong Kong Hong Kong S.A.R. Department of Computer and Information Science University of Macau Macau S.A.R. Orthopedics Center The University of Hong Kong-Shenzhen Hospital Shenzhen China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

A brain-computer interface (BCI) system based on event-related potentials (ERP) can be used to measure human intent for improved motor function rehabilitation applications. Somatosensory ERP is a paradigm especially suitable for patients with visual or hearing impairment, yet few algorithms were dedicated to it. In this study we conducted ERP experiments on healthy subjects with somatosensory (facilitated by electric stimuli), audio, and visual sessions. A convolutional neural network (CNN) classifier was specifically tuned to optimize classification accuracy of somatosensory ERP. The network includes temporal-spatial processing layers followed by MobileNet-styled module with Squeeze-Excitation. The model was trained as subject-specific classifier. The effect of using multiple ERP trials for classification and reduced number of EEG channels was also evaluated. For the standard single-trial 64-channel configuration, the proposed model achieved 81.4% accuracy for electric stimuli data, higher than the baseline models by 1.4 to 3.3%. Switching from 64 to 8-channel configuration brought only 3.2% decreased accuracy while further reducing to 4-channel did not make significant changes. Increasing the number of ERP trials used for classification improves the accuracy, which plateaued around 90% at 5-trial. In terms of implementation, the proposed model brought the classification accuracy improvement without significantly increasing the model size or inference time tested on mobile platforms. Ablation study showed that the presented architecture was the optimal among evaluated variations. For future development, the proposed model and somatosensory ERP paradigm will be assessed in terms of user intent recognition on real patients, and the model architecture will be continuously optimized for designated BCI task and dataset.

关键词： Somatosensory Visualization Accuracy Computational modeling Virtual environments computer architecture Brain modeling

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A Light Gradient Boosted Model for Network Intrusion Detection

A Light Gradient Boosted Model for Network Intrusion Detecti...

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Smart systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), international conference on

作者： Vyshnavi Manduru Tharun Gopi Reddy Kasireddy Karthik Manchina Vijay Shanmuk Davuluri Venkatrama Phani Kumar S Venkata Krishna Kishore K Department of CSE VFSTR Deemed to be University Guntur India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This paper investigates the development of a robust Network Intrusion Detection System (NIDS) utilizing the Light- GBM classification algorithm within the domain of data mining techniques. Extensive data preprocessing, feature engineering, and selection methods are applied to extract meaningful insights from a comprehensive dataset of web traffic attributes. Light-GBM, renowned for its meticulous approach compared to alter- native models, is specifically selected for its capacity to enhance NIDS accuracy and efficiency in discerning normal network traffic from potential intrusions. Evaluation results demonstrate the superior performance of LightGBM across diverse metrics, exhibiting high accuracy, precision, and recall. These results underscore the effectiveness of LightGBM in network intrusion detection and stress the significance of deliberate model selection for achieving high detection accuracy in practical scenarios.

关键词： measurement Accuracy Computational modeling Data preprocessing Network intrusion detection Machine learning Feature extraction

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Hindi Text Summarization: Using BERT

Hindi Text Summarization: Using BERT

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Intelligent Algorithms for Computational Intelligence systems (IACIS), international conference on

作者： Mukesh More Pallavi Yevale Abhang Mandwale Kundan Agrawal Om Mahale Sahilsing Rajput Department of Computer Engineering International Institute of Information Technology Pune India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This paper points out a vacuum in the literature on text summarization: not enough attention has been paid to methods for Indian languages. This research investigates the current state-of-the-art techniques for summarizing Indian languages. The review reveals a bias in current research towards extractive summarization, which involves choosing important lines, whereas abstractive summary, which involves rewriting text, is more common. The paper advocates for a more balanced examination of both abstractive and extractive summarizing strategies specifically adapted for Indian languages by examining current methodologies and identifying their advantages and disadvantages. In certain situations, the suggested system outperforms models such as SummaRuNNer, Lead-3, NeuSumHD. These improvements improve the resilience of the model as well as the quality of the generated summaries with a ROUGE-1 score of 41.43 and ROUGE-2 score of 24.54.

关键词： measurement Adaptation models Reviews Computational modeling Text summarization Bidirectional control Transformers Long short term memory Standards Resilience

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HLAS: An Enhanced Abstractive Summarization Method Based on Hybrid Loss Optimization

HLAS: An Enhanced Abstractive Summarization Method Based on ...

引用

Machine Learning and computer Application (ICMLCA), international conference on

作者： Hao Chen Yuan Ji Lankang Zhu Shu Yang Li Cui Changhui Feng Penghao Wang Yuxing Tan Hanhan Ma Jiaxing Hengchuang Electric Power Design and Research Institute Co. Ltd Jiaxing Hengchuang Jiaxing China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The conventional training of summarization models using maximum likelihood loss faces a challenge: the discrepancy between training and evaluation objectives. Training minimizes token-level loss, while evaluation focuses on sentence or document quality, leading to less effective results. To address this, we propose the Hybrid Loss Abstractive Summarization (HLAS) framework, which combines maximum likelihood loss with global sequence prediction metrics, aligning training with evaluation. Experiments on the BillSum and GovReport datasets show that HLAS significantly outperforms baseline models, achieving higher ROUGE scores and better summarization quality.

关键词： Training measurement Computational modeling Machine learning computer applications Hybrid power systems Stability analysis Optimization Faces Testing

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A Deep Assessmen of Smart Logistics Kind of Parks with the Fog Based and Enabled Sensor Cloud Type Platforms

A Deep Assessmen of Smart Logistics Kind of Parks with the F...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart systems (ISTEMS), international conference on

作者： Savita Yadav Ashish Gupta Preethi D Department of Internet of Things Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

The purpose of this research is to compare, in the environment of a clever logistics park, the effectiveness of fog-enabled sensor clouds (FSC) with traditional cloud computing with fog computing models. Expanding on our earlier work, we describe the physical instrument virtualization framework and scheme that are essential to the envisioned FSC. There is a careful construction of the network model and a mathematical explanation of the FSC parameters. We use a well-known logistics company in China is a case study to assess the efficacy of the suggested platform and clarify the network architecture in a real-world logistics situation. The experiment's results confirm that, with regard to bandwidth usage and service delay, the FSC for logistics parks is demonstrably superior to standard internet-based and fog computing models.

关键词： measurement Cloud computing Computational modeling Bandwidth Mathematical models Power capacitors Virtualization

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Object Fusion for Collective Perception: Will it Scale?

Object Fusion for Collective Perception: Will it Scale?

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international conference on Intelligent Transportation

作者： Alexander Willecke Roman Stahl Lars C. Wolf Technische Universität Braunschweig Institute of Operating Systems and Computer Networks Braunschweig Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

With Collective Perception, stations exchange information from their local perception with each other via broadcast communication. The information about perceived objects can enhance their awareness, allowing perception beyond their line of sight and increasing traffic safety. Therefore, received object information must be processed by an Object Fusion system that creates a holistic environment model. Solving this fusion problem is not trivial and computationally expensive. However, the computing power required for a single station that implements an Object Fusion system depends on the amount of object data that must be processed. The amount of data will rise with a progressing market introduction as more stations share their perception. For analyzing the scalability of fusion, the Artery V2X Simulation framework was extended with an Object Fusion architecture. The results show how much object data must be processed and stored for three realistic large-scale traffic scenarios at different Market Penetration Rates.

关键词： measurement Scalability Computational modeling Roads Road side unit computer architecture Safety Vehicle-to-everything Arteries Standards

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SCaO-DMN: Skill Cavier Optimization Enabled Deep Maxout Network for Privacy Preservation of Health Care Data based Federated Learning

SCaO-DMN: Skill Cavier Optimization Enabled Deep Maxout Netw...

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Distributed systems, computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Tanya Patel Manasi Gyanchandani Akhtar Rasool Debangana Ram Dept of CSE MANIT Bhopal India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Privacy for cloud properties is significant since the cloud has entrusted, and isolated sensitive data for public access. Thus, the Skill Cavier Optimization-based Deep Maxout Network (SCaO-DMN) is developed for privacy preservation. Here, the local training is done with the local data at each node and is updated on the server. Then, model aggregation is conducted and downloaded by a global module at each node. In the training module, the healthcare data is subjected to noisy treatment for privacy preservation. After that, the noise matrix process is achieved by DMN trained by SaCO. Lastly, Local updation and aggregation at the server is done using the average and stored in the cloud. The devised model is evaluated using Accuracy, loss function and Mean Squared Error (MSE) and obtained values of 91.887, 0.081, and 0.012. The SCaO-DMN can help in an effective treatment plan as it protects the data more securely.

关键词： Training Cloud computing Data privacy Federated learning Computational modeling Medical services Data models Servers Noise measurement Optimization

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